Дисертації з теми "Microwave Satellite Soil Moisture"

La humedad del suelo es la variable que regula los intercambios de agua, energía, y carbono entre la tierra y la atmósfera. Mediciones precisas de humedad son necesarias para una gestión sostenible de los recursos hídricos, para mejorar las predicciones meteorológicas y climáticas, y para la detección y monitorización de sequías e inundaciones. Esta tesis se centra en la medición de la humedad superficial de la Tierra desde el espacio, a escalas global y regional. Estudios teóricos y experimentales han demostrado que la teledetección pasiva de microondas en banda L es optima para la medición de humedad del suelo, debido a que la atmósfera es transparente a estas frecuencias, y a la relación directa de la emisividad del suelo con su contenido de agua. Sin embargo, el uso de la teledetección pasiva en banda L ha sido cuestionado en las últimas décadas, pues para conseguir la resolución temporal y espacial requeridas, un radiómetro convencional necesitaría una gran antena rotatoria, difícil de implementar en un satélite. Actualmente, hay tres principales propuestas para abordar este problema: (i) el uso de un radiómetro de apertura sintética, que es la solución implementada en la misión Soil Moisture and Ocean Salinity (SMOS) de la ESA, en órbita desde noviembre del 2009; (ii) el uso de un radiómetro ligero de grandes dimensiones y un rádar operando en banda L, que es la solución que ha adoptado la misión Soil Moisture Active Passive (SMAP) de la NASA, con lanzamiento previsto en 2014; (iii) el desarrollo de técnicas de desagregación de píxel que permitan mejorar la resolución espacial de las observaciones. La primera parte de la tesis se centra en el estudio del algoritmo de recuperación de humedad del suelo a partir de datos SMOS, que es esencial para obtener estimaciones de humedad con alta precisión. Se analizan diferentes configuraciones con datos simulados, considerando (i) la opción de añadir información a priori de los parámetros que dominan la emisión del suelo en banda L —humedad, rugosidad, temperatura del suelo, albedo y opacidad de la vegetación— con diferentes incertidumbres asociadas, y (ii) el uso de la polarización vertical y horizontal por separado, o del primer parámetro de Stokes. Se propone una configuración de recuperación de humedad óptima para SMOS. La resolución espacial de los radiómetros de SMOS y SMAP (40-50 km) es adecuada para aplicaciones globales, pero limita la aplicación de los datos en estudios regionales, donde se requiere una resolución de 1-10 km. La segunda parte de esta tesis contiene tres novedosas propuestas de mejora de resolución espacial de estos datos: • Se ha desarrollado un algoritmo basado en la deconvolución de los datos SMOS que permite mejorar la resolución espacial de las medidas. Los resultados de su aplicación a datos simulados y a datos obtenidos con un radiómetro aerotransportado muestran que es posible mejorar el producto de resolución espacial y resolución radiométrica de los datos. • Se presenta un algoritmo para mejorar la resolución espacial de las estimaciones de humedad de SMOS utilizando datos MODIS en el visible/infrarrojo. Los resultados de su aplicación a algunas de las primeras imágenes de SMOS indican que la variabilidad espacial de la humedad del suelo se puede capturar a 32, 16 y 8 km. • Un algoritmo basado en detección de cambios para combinar los datos del radiómetro y el rádar de SMAP en un producto de humedad a 10 km ha sido desarrollado y validado utilizando datos simulados y datos experimentales aerotransportados. Este trabajo se ha desarrollado en el marco de las actividades preparatorias de SMOS y SMAP, los dos primeros satélites dedicados a la monitorización de la variación temporal y espacial de la humedad de la Tierra. Los resultados presentados contribuyen a la obtención de estimaciones de humedad del suelo con la precisión y la resolución espacial necesarias para un mejor conocimiento del ciclo del agua y una mejor gestión de los recursos hídricos.
Soil moisture is a key state variable of the Earth's system; it is the main variable that links the Earth's water, energy and carbon cycles. Accurate observations of the Earth's changing soil moisture are needed to achieve sustainable land and water management, and to enhance weather and climate forecasting skill, flood prediction and drought monitoring. This Thesis focuses on measuring the Earth's surface soil moisture from space at global and regional scales. Theoretical and experimental studies have proven that L-band passive remote sensing is optimal for soil moisture sensing due to its all-weather capabilities and the direct relationship between soil emissivity and soil water content under most vegetation covers. However, achieving a temporal and spatial resolution that could satisfy land applications has been a challenge to passive microwave remote sensing in the last decades, since real aperture radiometers would need a large rotating antenna, which is difficult to implement on a spacecraft. Currently, there are three main approaches to solving this problem: (i) the use of an L-band synthetic aperture radiometer, which is the solution implemented in the ESA Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009; (ii) the use of a large lightweight radiometer and a radar operating at L-band, which is the solution adopted by the NASA Soil Moisture Active Passive (SMAP) mission, scheduled for launch in 2014; (iii) the development of pixel disaggregation techniques that could enhance the spatial resolution of the radiometric observations. The first part of this work focuses on the analysis of the SMOS soil moisture inversion algorithm, which is crucial to retrieve accurate soil moisture estimations from SMOS measurements. Different retrieval configurations have been examined using simulated SMOS data, considering (i) the option of adding a priori information from parameters dominating the land emission at L-band —soil moisture, roughness, and temperature, vegetation albedo and opacity— with different associated uncertainties and (ii) the use of vertical and horizontal polarizations separately, or the first Stokes parameter. An optimal retrieval configuration for SMOS is suggested. The spatial resolution of SMOS and SMAP radiometers (~ 40-50 km) is adequate for global applications, but is a limiting factor to its application in regional studies, where a resolution of 1-10 km is needed. The second part of this Thesis contains three novel downscaling approaches for SMOS and SMAP: • A deconvolution scheme for the improvement of the spatial resolution of SMOS observations has been developed, and results of its application to simulated SMOS data and airborne field experimental data show that it is feasible to improve the product of the spatial resolution and the radiometric sensitivity of the observations by 49% over land pixels and by 30% over sea pixels. • A downscaling algorithm for improving the spatial resolution of SMOS-derived soil moisture estimates using higher resolution MODIS visible/infrared data is presented. Results of its application to some of the first SMOS images show the spatial variability of SMOS-derived soil moisture observations is effectively captured at the spatial resolutions of 32, 16, and 8 km. • A change detection approach for combining SMAP radar and radiometer observations into a 10 km soil moisture product has been developed and validated using SMAP-like observations and airborne field experimental data. This work has been developed within the preparatory activities of SMOS and SMAP, the two first-ever satellites dedicated to monitoring the temporal and spatial variation on the Earth's soil moisture. The results presented contribute to get the most out of these vital observations, that will further our understanding of the Earth's water cycle, and will lead to a better water resources management.

SMOS (Soil Moisture and Ocean Salinity) is ESA's Earth Explorer series satellite carrying the novel MIRAS (Microwave Imaging Radiometer with Aperture Synthesis) interferometric synthetic aperture radar. Its objective is monitoring and studying the planet's water cycle by following the changes in soil moisture levels and ocean surface salt concentrations on a global scale. The success of the mission calls for nearly uninterrupted operation of the science payload. However, the instrument experiences sporadically problems with its hardware, which cause losses of scientific data and may require intervention from ground to resolve. The geographical areas in which most of these anomalies occur, polar regions and the South-Atlantic anomaly, give cause to assume these problems are caused by charged particles in the planet's ionosphere. In this thesis, methods of predicting occurrence of hardware anomalies from indicators of Earth radiation environment are investigated.

Dans cette thèse, un algorithme de restitution à base de réseaux de neurones a été développé afin d’estimer l’humidité du sol à partir d’une combinaison d’observations satellitaires en micro-ondes, infrarouge et visible. Une estimation globale des valeurs mensuelles d’humidité du sol a été obtenue pour la période 1993-2000 et est fournie sur une grille à pixel de surface constante avec une résolution équatoriale de 0,25 ◦. Cette estimation de l’humidité du sol a été évaluée avec des données modélisées, des données de télédétec- tion et des observations in situ et a montré une bonne performance à différentes échelles spatiales et temporelles. Une analyse de contenu en information a montré que chacune des différentes observations satellites contribue à une information différente sur l’humidité du sol, avec les données micro-ondes actives plus sensibles à l’évolution temporelle et les données infrarouges thermiques reproduisant mieux les structures spatiales. En outre, une analyse de synergie a révélé que la combinaison de toutes les observations permet une réduction de l’incertitude de restitution de plus de 18 % et que la méthode des réseaux de neurones exploite de manière optimale la synergie des observations par comparaison avec autres approches. Une analyse a démontré la cohérence de l’humidité du sol resti- tuée avec d’autres produits satellitaires réprésentatifs d’autres paramètres hydrologiques (inondations, précipitations) à l’échelle du globe. Cela souligne le potentiel de nôtre jeu de données d’humidité du sol pour les études du cycle de l’eau terrestre. Enfin, il a été démon- tré que la méthode de réseaux de neurones proposée, constitue également un outil efficace pour évaluer les modèles de surface continentale ainsi que la modélisation des processus
In this thesis, a neural network based retrieval algorithm has been developed to compute surface soil moisture from a combination of microwave, infrared and visible satellite obser- vations. A global estimate of monthly mean soil moisture values has been computed for the period 1993-2000 and is provided on an equal-area grid with an equatorial resolution of 0. 25◦. This soil moisture estimate has been evaluated against modelled, remotely sensed and in situ observations and was found to perform well on different spatial and temporal scales. An information content showed that each of the various satellite observations con- tributes information about a different soil moisture variation, with the active microwave data being more sensitive to the temporal evolution and the thermal infrared data better capturing the spatial patterns. Furthermore, a synergy analysis revealed that the combina- tion of all observations permits a reduction of the retrieval uncertainty by more than 18% and that the neural network methodology optimally exploits the synergy of observations compared to other approaches. A joint analysis of various remotely sensed datasets of ter- restrial water cycle components demonstrated the coherence of the retrieved soil moisture with other retrieval products and with global hydrological processes. This underlined its potential to be used for observation-based studies of the terrestrial water cycle. Finally, it has been shown that the proposed neural network methodology also provides an effective tool to evaluate Earth System Models on both a variable and a process basis

Thesis (M.S.)--George Mason University, 2008.
Vita: p. 65. Thesis director: John Qu. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Earth System Science. Title from PDF t.p. (viewed Jan. 11, 2009). Includes bibliographical references (p. 59-64). Also issued in print.

Soil moisture in the shallow subsurface is a primary hydrologic state governing land-atmosphere interaction at various scales. The primary objectives of this study are to model soil moisture in the root zone in a distributed manner and determine scaling properties of surface soil moisture using passive microwave remote sensing. The study was divided into two parts. For the first study, a root zone soil moisture assessment tool (SMAT) was developed in the ArcGIS platform by fully integrating a one-dimensional vadose zone hydrology model (HYDRUS-ET) with an ensemble Kalman filter (EnKF) data assimilation capability. The tool was tested with dataset from the Southern Great Plain 1997 (SGP97) hydrology remote sensing experiment. Results demonstrated that SMAT displayed a reasonable capability to generate soil moisture distribution at the desired resolution at various depths of the root zone in Little Washita watershed during the SGP97 hydrology remote sensing experiment. To improve the model performance, several outstanding issues need to be addressed in the future by: including "effective" hydraulic parameters across spatial scales; implementing subsurface soil properties data bases using direct and indirect methods; incorporating appropriate hydrologic processes across spatial scales; accounting uncertainties in forcing data; and preserving interactions for spatially correlated pixels. The second study focused on spatial scaling properties of the Polarimetric Scanning Radiometer (PSR)-based remotely sensed surface soil moisture fields in a region with high row crop agriculture. A wavelet based multi-resolution technique was used to decompose the soil moisture fields into larger-scale average soil moisture fields and fluctuations in horizontal, diagonal and vertical directions at various resolutions. The specific objective was to relate soil moisture variability at the scale of the PSR footprint (800 m X 800 m) to larger scale average soil moisture field variability. We also investigated the scaling characteristics of fluctuation fields among various resolutions. The spatial structure of soil moisture exhibited linearity in the log-log dependency of the variance versus scale-factor, up to a scale factor of -2.6 (6100 m X 6100 m) irrespective of wet and dry conditions, whereas dry fields reflect nonlinear (multi-scaling) behavior at larger scale-factors.

Spaceborne microwave sensors are capable of estimating various properties of many geophysical phenomena, including the age and extent of Arctic sea ice and the relative soil moisture over land. The measurement and classification of such geophysical phenomena are used to refine climate models, localize and predict drought, and better understand the water cycle. Data from the active Ku-band scatterometers, the Quick Scatterometer (QuikSCAT), and the Oceansat-2 Scatterometer (OSCAT), are here used to classify areas of first-year and multiyear Arctic sea ice using a temporally adaptive threshold on reported radar backscatter values. The result is a 15-year data record of daily ice classification images. An additional ice age data record is produced using the C-band Advanced Scatterometer (ASCAT) and the Special Sensor Microwave Imager Sounder (SSMIS) with an alternate classification methodology based on Bayesian decision theory. The ASCAT/SSMIS classification methodology results in a record which is generally consistent with the QuikSCAT and OSCAT classifications, which conclude in 2014. With multiple ASCAT and SSMIS sensors still operational, the ASCAT/SSMIS ice classifications can continue to be produced into the future. In addition to ice classification, ASCAT is used to estimate the relative surface soil moisture at high-resolution (4.45 — 4.45 km per pixel). The soil moisture estimates are obtained using enhanced resolution image reconstruction techniques and an altered version of the Water Retrieval Package (WARP) algorithm. The high-resolution soil moisture estimates are shown to agree well with the existing lower resolution WARP products while also revealing finer details.

Soil moisture is an important element in hydrological land-surface processes as well as land atmosphere interactions and has proven useful in numerous agronomical, climatological and meteorological studies. Since hydrological soil moisture estimates are usually point-based measurements at a specific site and time, spatial and temporal dynamics of soil moisture are difficult to capture. Soil moisture retrieval techniques in remote sensing present possibilities to overcome the abovementioned limitations by continuously providing distributed soil moisture data atdifferent scales and varying temporal resolutions. The main purpose of this study is to derive soil moisture estimates for the Stockholm region by means of two different approaches from a hydrological and a remote sensing point of view and the comparison of both methods. Soil moisture is both modelled with the Topographic Wetness Index (TWI) based on digital elevation data and with the Temperature‐Vegetation Dryness Index (TVDI) as a representation of land surface temperature and Normalized Difference Vegetation Index (NDVI) ratio. Correlations of both index distributions are investigated. Possible index dependencies onvegetation cover and underlying soil types are explored. Field measurements of soil moistureare related to the derived indices. The results indicate that according to a very low Pearson correlation coefficient of 0.023, nolinear dependency between the two indices existed. Index classification in low, medium and high value categories did not result in higher correlations. Neither index distribution is found to berelated to soil types and only the TVDI correlates alongside changes in vegetation cover distribution. In situ measured values correlate better with TVDIs, although neither index is considered to give superior results in the area due to low correlation coefficients. The decision which index to apply is dependent on available data, intent of usage and scale. The TWI surface is considered to be a more suitable soil moisture representation for analyses on smaller scaleswhereas the TVDI should prove more valuable on a larger, regional scale. The lack of correlation between the indices is attributed to the fact that they differ greatly in their underlying theories. However, the synthesis of hydrologic modelling and remote sensing is a promising field of research. The establishment of combined effective models for soil moisture determination over large areas requires more extensive in situ measurements and methods to fully assess the models’ capabilities, limitations and value for hydrological predictions.

Soil moisture is an integral part of the Earth's hydrological cycle. Therefore, accurate estimation of the Earth's changing soil moisture is required to achieve sustainable land and water management to augment Numerical Weather Prediction (NWP) and forecasting skill, and to develop improved flood and drought monitoring capability. Unfortunately, most of the locations in the world do not have accurate soil moisture information on a relevant spatial and temporal scale. However, after latest advances in remote sensing and mesoscale models, it is now possible to estimate soil moisture using passive microwave satellite imaging such as Soil Moisture and Ocean Salinity (SMOS) and/or mesoscale model like Weather Research and Forecasting (WRF)-NOAH Land Surface Model (LSM). L-band passive remote sensing and WRF-NOAH LSM are potentially very useful for soil moisture sensing due to its all-weather capabilities and in-depth physics oriented relationship between soil emissivity and soil moisture, applicable for a diverse land use/land cover. In commensurate with new era in soil moisture remote sensing, this thesis explores the potential of SMOS satellite and WRFNOAH LSM for soil moisture retrieval over the temperate maritime climate. Also, soil moisture deficit (SM I) is found to be an integral component for irrigation scheduling, drought and flood prediction. Hence, the main focus of this thesis is the evaluation of soil moisture datasets as a method to effectively determine the SMD. All major areas of the improvement aided by the SMOS and WRF NOAH LSM arc addressed. Several novel approaches and investigations dealing with the SMOS soil moisture retrieval using Microwave Polarisation Difference Index (M PDI) and Radiative Transfer Equations arc examined. Input data (soil roughness, land surface temperature and vegetation opacity) sensitivity of different retrieval configurations are evaluated using the various algorithms. Thus, the thesis includes ( I) initial evaluation of SMOS satellite and ECMWF downscaled soil moisture using WRF-NOAH LSM with special reference to sensitivity of growing and non growing seasons; (2) assessment of land parameter retrieval model and tau-omega rationale; (3) a modified soil moisture retrieval algorithm from SMOS brightness temperature; and (4) sensitivity and uncertainty analysis of mesoscale model based product for SMD prediction. Further through this study, SMOS soil moisture downscaling schemes using artificial intelligence techniques with MODIS LST have also been proposed to improve the spatial resolution at a catchment scale and finally, data fusion techniques for improving soil l moisture deficit are presented with the SMOS and WRF-NOAII LSM. The overall finding indicates that the SMOS and WRF-NOAH LSM using ECMWF have been proven not only to improve data quality and soil moisture deficit estimation, but also have a great potential in fostering the soil moisture research and applications. The studies presented in this thesis will enhance our understanding of the Earth's water cycle, will help improve ECMWF forecast, SMOS algorithm, NWP and will lead to better water resource management practices.

Soil moisture condition of a watershed plays a significant role in separation of rainfall into infiltration and surface runoff, and hence is a key parameter for the majority of physical hydrological models. Due to the large difference in dielectric constants of dry soil and water, microwave remote sensing and particularly the commonly available synthetic aperture radar is a potential tool for such studies. The main aim of this study is to produce the distributed soil moisture maps of a catchment from active microwave imagery. For this purpose, nine field trips are performed within a small basin in western Anatolia and point surface soil moisture values are collected with a Time Domain Reflectometer. The field studies are planned to match radar image acquisitions and accomplished over the water year of 2004 - 2005. In this context, first, the Dubois Model, a semi-empirical backscatter model is utilized in the reverse order to develop radar backscatter &
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soil roughness relationship and soil roughness maps of the study area are obtained. Then another relationship is built between radar backscatter and the three governing surface parameters: local incidence angle, soil moisture and soil roughness, which is later used in the soil moisture estimation methods. Depending on land use and vegetation cover condition, surface soil moisture maps of the catchment are produced by Backscatter Correction Factors, Water Cloud Model and Basin Indexes methods. In the last part of the study, the soil moisture maps of the basin are input to a semi-distributed hydrological model, HEC-HMS, as the initial soil moisture condition of a flood event simulation. In order to investigate the contribution of distributed initial soil moisture data on model outputs, simulation of the same flood event is also performed with the lumped initial soil moisture condition. Finally, a comparison between both the distributed and lumped model simulation outputs and with the observed data is carried out.

This research examines the potential to use passive microwave remote sensing for measuring soil moisture extremes that impact agricultural areas in Canada. A validation was made of three passive microwave remote sensing soil moisture data sets, with weekly averaged values from the Land Parameter Retrieval Model (LPRM) applied to AMSR-E C/X-Band data providing the most accurate results (root mean squared error of 5 to 10%). A further evaluation of this data set against a spatially distributed in situ soil moisture network in Alberta suggests that this data set may be less accurate in regions where dense vegetation or open water is present, particularly on the northern edges of the Canadian agricultural extent. A method to derive soil moisture anomalies was developed that uses homogenous regions to spatially aggregate soil moisture statistics to compensate for a short satellite data record. It was found that these anomalies can be estimated with errors of less than 5% when these regions are 15 pixels or more over a seven year time period. Surface soil moisture anomalies from LPRM showed weak but significant relationships to precipitation based drought indices, suggesting promise for using these anomalies for wider soil moisture extremes monitoring. Soil moisture anomalies from CLASS and in situ networks showed inconsistencies with LPRM anomalies in how they capture soil moisture conditions that are relevant to agricultural yield.. These data sets overall show that this approach to quantifying extremes has potential, but improvement to soil moisture retrieval from LPRM and CLASS, and an integration of the information they provide are needed to optimize these data sets for agricultural monitoring.
National Science and Engineering Research Council, Agriculture and Agri-Food Canada, Canadian Space Agency

Soil moisture is a key variable that defines land surface-atmosphere (boundary layer) interactions, by contributing directly to the surface energy and water balance. Soil moisture values derived from remote sensing platforms only accounts for the near surface soil layers, generally the top 5cm. Passive microwave data at L-band (1.4 GHz, 21cm wavelength) measurements are shown to be a very effective observation for surface soil moisture retrieval. The first space-borne L-band mission dedicated to observing soil moisture, the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, was launched on 2nd November 2009.Artificial Neural Network (ANN) methods have been used to empirically ascertain the complex statistical relationship between soil moisture and brightness temperature in the presence of vegetation cover. The current problem faced by this method is its inability to predict soil moisture values that are 'out-of-range' of the training data.In this research, an optimization model is developed for the Backpropagation Neural Network model. This optimization model utilizes the combination of the mean and standard deviation of the soil moisture values, together with the prediction process at different pre-determined, equal size regions to cope with the spatial and temporal variation of soil moisture values. This optimized model coupled with an ANN of optimum architecture, in terms of inputs and the number of neurons in the hidden layers, is developed to predict scale-to-scale and downscaling of soil moisture values. The dependency on the accuracy of the mean and standard deviation values of soil moisture data is also studied in this research by simulating the soil moisture values using a multiple regression model. This model obtains very encouraging results for these research problems.The data used to develop and evaluate the model in this research has been obtained from the National Airborne Field Experiments in 2005.

Soil moisture is a key element in the hydrological cycle, regulating evapotranspiration, precipitation, infiltration and overland flow. However, its effective utilisation in hydrological modelling is still in a state of infancy. Generally, hydrological application of soil moisture data requires: 1) soil moisture data relevant to hydrology, and 2) appropriate hydrological model structure compatible with such data. This thesis focuses on tackling those two aspects by enhancing the compatibility between soil moisture observations and operational hydrological models' soil moisture state variable. In this study satellite soil moisture observations are mainly focused on, because of their increasing availability and large-scale global coverage

Passive microwave remote sensing has shown potential for monitoring near surface soil moisture. This dissertation presents a new approach to representing the effect of vegetation on microwave emission by extending an existing model (Wilheit, 1978) of the coherent propagation of electromagnetic radiation through a stratified medium. The resulting multi-layer microwave emission model is plausibly realistic in that it captures the behavior of the vegetation canopy by considering the dielectric permittivity of the mixture of air and vegetation matter in the canopy and recognizing the vertical distribution of dielectric permittivity through the canopy. The model parameters required to specify the dielectric profile within the canopy are not usually available from data taken in typical field experiments, particularly the parameters that quantify the way the dielectric permittivity of the vegetation and air mix together to give the dielectric permittivity of the canopy. Thus, the feasibility of specifying these parameters using an advanced single-criterion, multiple-parameter optimization technique was investigated. The resulting model was also applied to investigate the sensitivity of microwave emission to specific vegetation parameters. The study continued with an investigation of how the presence and nature of vegetation cover influences the values of geophysical variables retrieved from multi-angle microwave radiometer spectrometer observations, using the upcoming Soil Moisture Ocean Salinity (SMOS) mission as a case study. The extended version of the Wilheit (1978) model was used to calculate synthetic observations of microwave brightness temperature at the look-angles proposed for the SMOS mission for three different soil moisture states (wet, medium, and dry) and four different vegetation covers (grass, crop, shrub, and forest). It was shown that retrieved values are only accurate when the effective values of the opacity coefficient used in the Fresnel model are made to vary in a prescribed way with look-angle, soil moisture status, and vegetation. The errors in retrieved values that may be induced by poor specification of vegetation cover were investigated by imposing random errors in the values of vegetation-related parameters in the forward calculations of synthetic observations made with the extended Wilheit model. The results show that poorly specified vegetation can result in both random and systematic errors in the retrieved values of the geophysical variables. (Abstract shortened by UMI.)

Soil moisture is an important variable in hydrological modelling used for real time flood forecasting and water resources management. However, it is a very challenging task to measure soil moisture over a hydrological catchment using conventional in-situ sensors. Remote sensing is gaining popularity due to its large coverage suitable for soil moisture measurement at a catchment scale albeit there are still many knowledge gaps to be filled in. This thesis focuses on investigating soil moisture estimation from remote sensing satellite and land surface model (LSM) coupled with a Numerical Weather Prediction (NWP) model. A hydrological-based approach has been conducted to assess/evaluate the estimated soil moisture using event-based water balance and Probability Distributed Model (PDM). An Advance Microwave Scanning Radiometer (AMSR) and a physically- based Land Parameters Retrieval Model (LPRM) have been used to retrieve surface soil moisture over the sturdy area. The LPRM vegetation and roughness parameters have been empirically calibrated by a new approach proposed in this thesis. The relevant parameters are calibrated on the hydrological model through achieving the best correlation between the observation-based catchment storage and the retrieved surface soil moisture. The development of the land surface model coupled with the NWP model is used to estimate soil moisture at different combinations of soil layers. The optimal combination of the top two layers is found to have the best performance when compared to the catchment water storage. Regression-based mathematical models have been derived to predict the catchment storage from the estimated soil moisture based on both satellite remote sensing and the LSM-NWP model. Three schemes are proposed to examine the behaviour of soil moisture products over different seasons in order to find the appropriate formulas in different scenarios. Finally, weighted coefficients and arithmetic average data fusion methods are explored to integrate two independent soil moisture products from the AMSR-E satellite and the LSM-NWP. It has been found that the merged output is a significant improvement over their individual estimates. The implementation of the fusion technique has provided a new opportunity for information integration from satellite and NWP model. Keywords: Soil moisture, Satellite remote sensing, satellite, land surface model, NWP model, rainfall-runoff model, water balance, PDM model

>Magister Scientiae - MSc
Soil moisture forms an integral part of the hydrological cycle and exerts considerable influence on hydrological processes at or near the earth’s surface. Knowledge of soil moisture is important for planning and decision-making in the agricultural sector, land and water conservation and flood warning. Point measurements of soil moisture, although highly accurate, are time consuming, costly and do not provide an accurate indication of the soil moisture variation over time and space as soil moisture has a high degree of spatial and temporal variability. The spatial variability of soil moisture is due to the heterogeneity of soil water holding properties, the influence of plants, and land uses. The downscaling of satellite microwave soil moisture estimates and soil water balance modelling was investigated at six transects in the semi-arid, Western Cape Province of South Africa, as alternatives to in situ soil measurements. It was found that microwave soil moisture estimates compared well to in situ measurements at the six transects (study sites), with coefficient of determination (r2) values greater than 0.7 and root mean square error (RMSE) values less than 1.5%. Downscaling using the universal triangle method, performed well at 4 of the 6 transects, with r2 values great than 0.65 and low to moderate RMSE values (0.5-12%). Soil water balance modelling similarly performed well in comparison with in situ measurements at 4 of the transects with regards to r2 values (>0.6) but had moderate to high RMSE (4.5-19%). Poor downscaling results were attributed to fine scale (within 1 km) surface heterogeneity while poor model performance was attributed to soil hydrological and rainfall heterogeneity within the study areas.

L'humidité du sol (SM) contrôle les bilans d’eau et d’énergie des surfaces continentales et joue ainsi un rôle clé dans les domaines de la météorologie, l'hydrologie et l'écologie. La communauté scientifique en télédétection micro-ondes a fait des efforts considérables pour établir des bases de données globales de l’humidité du sol en surface (SSM) découlant d'instruments micro-ondes actifs et passifs. Parmi ces instruments, SMOS (Soil Moisture and Ocean Salinity), lancé en 2009, est le premier satellite passif conçu spécifiquement pour mesurer SSM à partir d’observations en bande L (1.4 GHz) à l'échelle globale. La validation des données SMOS SSM sur différentes régions climatiques et pour des conditions environnementales variées est une étape indispensable avant qu’elles soient utilisées de manière opérationnelle. En effet, une meilleure connaissance de la précision des estimations de SSM et des incertitudes associées permettra non seulement d'améliorer les produits SMOS SSM, mais aussi d'optimiser les approches de fusion de données utilisées pour créer des produits multi-capteurs long terme. De tels produits sont développés dans le cadre du programme Climate Change Initiative (CCI) de l'Agence spatiale européenne (ESA) pour l’ensemble des variables climatiques essentielles (ECV), dont SSM. A la suite des chapitres d'introduction I à III, les résultats de cette thèse sont présentés en trois chapitres. Le chapitre IV présente une comparaison des produits SSM issus des capteurs passifs SMOS (bande L) et AMSR-E (bande C) en prenant pour référence les estimations SSM du système d'assimilation SM-DAS-2 du Centre Européen pour les Prévisions Météorologiques à Moyen Terme (CEPMMT). Cette évaluation est menée sur la période d’observation commune à SMOS et AMSR-E (2010- 2011), en utilisant des indicateurs classiques (corrélation, RMSD, Biais). En parallèle, le chapitre V présente une comparaison des produits SMOS SSM avec les produits SSM issus du capteur actif ASCAT en bande C en utilisant comme référence les simulations SSM d’un modèle des surfaces continentales (MERRA-Land), et en utilisant des indicateurs classiques, des méthodes statistiques avancées (triple collocation), et des diagrammes de Hovmöller sur la période 2010-2012. Ces deux évaluations ont montré que la densité de la végétation (paramétrée ici par l’indice foliaire LAI) est un facteur clé pour interpréter la cohérence entre le produit SMOS et les produits AMSR-E et ASCAT. Cet effet de la végétation a été quantifié pour la première fois à l’échelle globale pour les trois capteurs micro-ondes. Ces deux chapitres ont également montré que les trois capteurs SMOS, AMSR-E et ASCAT ont des performances complémentaires selon la densité de végétation et qu’il y a ainsi un potentiel intéressant en terme de fusion des jeux de données micro-ondes passifs et actifs. Dans le chapitre VI, avec l’objectif général d’étendre vers le passé les séries de données SSM de SMOSL3 et de développer un jeu de données SSM homogène sur 2003-2014, nous avons évalué l’utilisation d’une approche de régression linéaire multiple appliquée aux mesures de températures de brillance de AMSR-E (2003 - 2011). Les coefficients de régression ont été calibrés avec les produits SSM issus de SMOS sur 2010-2011. Le produit SSM résultant, qui fusionne les observations SMOS et AMSR-E, a été évalué par comparaison avec un produit SSM AMSR-E et les produits SSM MERRA-Land sur 2007-2009. Ces résultats préliminaires montrent que la méthode de régression linéaire est une approche simple et robuste pour construire un produit SSM réaliste en termes de variations temporelles et de valeurs absolues. En conclusion, cette thèse a montré que le potentiel de synergie entre les systèmes micro-ondes passifs (AMSR-E et SMOS) et actifs (ASCAT) est très prometteur pour le développement et l'amélioration de longues séries temporelles SSM à l'échelle mondiale, telles que celles produites dans le cadre du programme CCI de l'ESA
Soil moisture (SM) plays a key role in meteorology, hydrology, and ecology as it controls the evolution of various hydrological and energy balance processes. The community of scientists involved in the field of microwave remote sensing has made considerable efforts to build accurate estimates of surface SM (SSM), and global SSM datasets derived from active and passive microwave instruments have recently become available. Among them, SMOS (Soil Moisture and Ocean Salinity), launched in 2009, was the first ever passive satellite specifically designed to measure the SSM, at L-band (1.4 GHz), at the global scale. Validation of the SMOS SSM datasets over different climatic regions and environmental conditions is extremely important and a necessary step before they can be used. A better knowledge of the skill and uncertainties of the SSM retrievals will help not only to improve the individual products, but also to optimize the fusion schemes required to create long-term multi-sensor products, like the essential climate variable (ECV) SSM product generated within the European Space Agency’s (ESA's) Climate Change Initiative (CCI) program. After the introductory Chapters I to III, this dissertation consists of three main parts. Chap. IV of the dissertation evaluates the passive SMOS level 3 (SMOSL3) SSM products at L-band against the passive AMSR-E SSM at C-band by comparing them with a Land Data Assimilation System estimates (SM-DAS-2) produced by the European Centre for Medium Range Weather Forecasts (ECMWF). This was achieved over the common period 2010-2011 between SMOS and AMSR-E, using classical metrics (Correlation, RMSD, and Bias). In parallel, Chap. V of the dissertation evaluates the passive SMOSL3 products against the active ASCAT SSM at C-band by comparing them with land surface model simulations (MERRA-Land) using classical metrics, advanced statistical methods (triple collocation), and the Hovmöller diagram over the period 2010-2012. These two evaluations indicated that vegetation density (parameterized here by the leaf area index LAI) is a key factor to interpret the consistency between SMOS and the other remotely sensed products. This effect of the vegetation has been quantified for the first time at the global scale for the three microwave sensors. These two chapters also showed that both SMOS and ASCAT (AMSR-E) had complementary performances and, thus, have a potential for datasets fusion into long-term SSM records. In Chap. VI of the dissertation, with the general purpose to extend back the SMOSL3 SSM time series and to produce an homogeneous SM product over 2003-2014 based on SMOS and AMSR-E, we investigated the use of a multiple linear regression model based on bi-polarization (horizontal and vertical) brightness temperatures (TB) observations obtained from AMSR-E (2003 - 2011). The regression coefficients were calibrated using SMOSL3 SSM as a reference over the 2010-2011 period. The resulting merged SSM dataset was evaluated against an AMSR-E SSM retrievals and modelled SSM products (MERRA-Land) over 2007-2009. These first results show that the multi-linear regression method is a robust and simple approach to produce a realistic SSM product in terms of temporal variation and absolute values. In conclusion, this PhD showed that the potential synergy between the passive (AMSR-E and SMOS) and active (ASCAT) microwave systems at global scale is very promising for the development of improved, long-term SSM time series at global scale, such as those pursued by the ESA’s CCI program. It also provides new ideas on the way to merge the different SSM datasets with the aim of producing the CCI (phase 2) long-term series (a coherent "SMOS-AMSR-E" SSM time series for the period 2003 -2014), that will be evaluated further in the framework of on-going ESA projects

Drought is a natural hazard characterized by a high degree of complexity thus its investigation could be best performed only through a complex analysis involving a set of environmental variables and their intricate relationships. Therefore, the development of an effective early drought detection and monitoring system requires integration of observations on vegetation condition, climate-based drought index data, and several biophysical and social variables of the environment. One of the fundamental drought variables is of course soil moisture, a key parameter determining crop yield potential in drought-affected parts of the world like in the developing nations of the Horn of Africa. Hence soil moisture deficit can be regarded as an important component of, if not synonymous with especially agricultural drought. However, to date model components taken into consideration in the existing drought detection and monitoring systems are only data on precipitation and vegetation condition. Cognizant of this fact, the current work targets integrating historical and real-time monitoring of soil moisture conditions in existing prediction and warning systems to make efforts of early detection of drought events more efficient and reliable. The dissertation addressed the gap through investigation of spatial and temporal soil moisture dynamics in the Horn of Africa using data from microwave observations, and the subsequent definition of monitoring procedures and/or triggers suitable to drought early warning activities. For these purposes, satellite based soil moisture long-term time-series data, obtained from the Water Cycle Multi-Mission Observation Strategy (WACMOS), has been processed, investigated and analyzed using proper statistical methodologies. The specific objectives were investigating the historical time-series soil moisture spatio-temporal dynamics and assess interactions with land cover/vegetation types; to identify historical soil moisture anomalies and establish the relationships with historical agricultural drought events in the Horn of Africa and to explore the relationships between soil moisture and vegetation conditions, a commonly used drought variable. Results of the study has clearly revealed that the WACMOS soil moisture data set and methodology implemented proved useful to identify the behaviors of different vegetation types; the soil moisture index developed (SMCI), is an effective tool in identifying historical drought events; the highly significant correlation between the vegetation and soil moisture data observed at the time lag -1, justifies the potential use of the soil moisture data for drought detection purposes, in order to complement NDVI analysis for an effective drought early warning. Further quantitative validation of the results is possible and helpful if sufficient geospatial data about drought distribution in time and space are available. Eventually, potential of the satellite based WAMOS soil moisture data for detection and monitoring of drought can still be improved if the following points are taken into consideration: Guaranteeing that the soil moisture data retrieved from the different microwave instruments are physically consistent and data available in near-real time, the level of missing data which account for low accuracy need refining and development and the low spatial resolution of the data set is a limitation compromising the level of details many investigations require.

The European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite was launched on November, 2, 2009 from the Russian cosmodrome of Plesetsk. Its objective is to globally and regularly collect measurements of soil moistre and Sea Surface Salinity (SSS). To do that, a pioneering instru- ment has been developed: the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS), the rst space-borne, 2-D interferometric radiometer ever built; it operates at L-band, with a central frequency of 1.4135 GHz, and consists of 69 antennas arranged in a Y shape array. MIRAS' output are brightness temperature maps, from which SSS can be derived through an iterative algorithm, and using auxiliary information. For each overpass of the satellite an SSS map is produced, with an estimated accuracy of 1 psu (rmse). According to the Global Ocean Data Assimilation Experiment (GODAE) the mission requirement is instead speci ed as 0.1 psu after av- eraging in a 10-day and 2 2 spatio-temporal boxes. In previuos works ((Sabia et al., 2010), or more extensively in Dr. Sabia's Ph.D. thesis (Sabia, 2008)) the main error sources in retrieving SSS from SMOS measurements were determined as: 1. Scene-dependent bias in the simulated measurements, 2. L-band forward modeling de nition, 3. Radiometric sensitivity and accuracy, 4. Constraints in the cost function, and 5. Spatio-temporal averaging. This Ph.D. thesis, is an attempt of reducing part of the aforementioned errors (the relative to the one-overpass SSS (1 - 4)) by a more sophisticated data processing. Firstly, quasi-realistic brightness temperatures have been simulated using the SMOS End-to-end Performance Simulator (SEPS) in its full mode and an ocean model, as provider for geophysical parameters. Using this data set the External Brightness Temperature Calibration technique has been tested to mitigate the scene-dependent bias, while the error introduced by inaccuracies in the L-band forward models has been accounted for by the application of the External Sea Surface Salinity Calibration. Apart from simulated brightness temperatures, both External Brightness Temperature Calibration and External Sea Surface Salinity Calibration have been tested using real synthetic-aperture brightness temperatures, collected by the Helsinki University of Technology HUT-2D radiometer during the SMOS Calibration and Validation Rehearsal Campaign in August 2007 and ten days of data acquired by the SMOS satellite between July 10 and 19, 2010. Finally, a study of the cost function used to derive SSS has been performed: the correlation between measurement mis ts has been estimated and the e ect of including it in the processing have been assessed. As an outcome of a 3-month internship at the Laboratoire LOCEAN in Paris, France, a theoretical review of the e ect of the rain on the very top SSS vertical pro le has been carried out and is presented as Appendix.

L’oceanografia per satèl•lit ha esdevingut una integració consolidada de les tècniques convencionals de monitorització in situ dels oceans. Un coneixement precís dels processos oceanogràfics i de la seva interacció és fonamental per tal d’entendre el sistema climàtic. En aquest context, els camps de salinitat mesurats regularment constituiran directament una ajuda per a la caracterització de les variacions de la circulació oceànica global. La salinitat s’utilitza en models oceanogràfics predictius, pero a hores d’ara no és possible mesurar-la directament i de forma global. La missió Soil Moisture and Ocean Salinity (SMOS) (en català, humitat del sòl i salinitat de l’oceà) de l’Agència Espacial Europea pretén omplir aquest buit mitjançant la implementació d’un satèl•lit capaç de proveir aquesta informació sinòpticament i regular. Un nou instrument, el Microwave Imaging Radiometer by Aperture Synthesis (MIRAS) (en català, radiòmetre d’observació per microones per síntesi d’obertura), ha estat desenvolupat per tal d’observar la salinitat de la superfície del mar (SSS) als oceans a través de l’adquisició d’imatges de la radiació de microones emesa al voltant de la freqüència de 1.4 GHz (banda L). SMOS portarà el primer radiòmetre orbital, d’òrbita polar, interferomètric 2D i es llençarà a principis de 2009. Així com a qualsevol altra estimació de paràmetres geofísics per teledetecció, la recuperació de la salinitat és un problema invers que implica la minimització d’una funció de cost. Per tal d’assegurar una estimació fiable d’aquesta variable, la resta de paràmetres que afecten a la temperatura de brillantor mesurada s’ha de tenir en compte, filtrar o quantificar. El producte recuperat seran doncs els mapes de salinitat per a cada passada del satèl•lit sobre la Terra. El requeriment de precisió proposat per a la missió és de 0.1 ‰ després de fer el promig en finestres espaciotemporals de 10 dies i de 20x20. En aquesta tesi de doctorat, diversos estudis s’han dut a terme per a la determinació del balanç d’error de la salinitat de l’oceà en el marc de la missió SMOS. Les motivacions de la missió, les condicions de mesura i els conceptes bàsics de radiometria per microones es descriuen conjuntament amb les principals característiques de la recuperació de la salinitat. Els aspectes de la recuperació de la salinitat que tenen una influència crítica en el procés d’inversió són: • El biaix depenent de l’escena en les mesures simulades, • La sensibilitat radiomètrica (soroll termal) i la precisió radiomètrica, • La definició de la modelització directa banda L • Dades auxiliars, temperatura de la superfície del mar (SST) i velocitat del vent, incerteses, • Restriccions en la funció de cost, particularment en el terme de salinitat, i • Promig espacio-temporal adequat. Un concepte emergeix directament de l’enunciat del problema de recuperació de la salinitat: diferents ajustos de l’algoritme de minimització donen resultats diferents i això s’ha de tenir en compte. Basant-se en aquesta consideració, la determinació del balanç d’error s’ha aproximat progressivament tot avaluant l’extensió de l’impacte de les diferents variables, així com la parametrització en termes d’error de salinitat. S’ha estudiat l’impacte de diverses dades auxiliars provinents de fonts diferents sobre l’error SSS final. Això permet tenir una primera impressió de l’error quantitatiu que pot esperar-se en les mesures reals futures, mentre que, en un altre estudi, s’ha investigat la possibilitat d’utilitzar senyals derivats de la reflectometria per tal de corregir les incerteses de l’estat del mar en el context SMOS. El nucli d’aquest treball el constitueix el Balanç d’Error SSS total. S’han identificat de forma consistent les fonts d’error i s’han analitzat els efectes corresponents en termes de l’error SSS mig en diferents configuracions d’algoritmes. Per una altra banda, es mostren els resultats d’un estudi de la variabilitat horitzontal de la salinitat, dut a terme utilitzant dades d’entrada amb una resolució espacial variable creixent. Això hauria de permetre confirmar la capacitat de la SSS recuperada per tal reproduir característiques oceanogràfiques mesoscàliques. Els principals resultats i consideracions derivats d’aquest estudi contribuiran a la definició de les bases de l’algoritme de recuperació de la salinitat.
Satellite oceanography has become a consolidated integration of conventional in situ monitoring of the oceans. Accurate knowledge of the oceanographic processes and their interaction is crucial for the understanding of the climate system. In this framework, routinely-measured salinity fields will directly aid in characterizing the variations of the global ocean circulation. Salinity is used in predictive oceanographic models, but no capability exists to date to measure it directly and globally. The European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) mission aims at filling this gap through the implementation of a satellite that has the potential to provide synoptically and routinely this information. A novel instrument, the Microwave Imaging Radiometer by Aperture Synthesis, has been developed to observe the sea surface salinity (SSS) over the oceans by capturing images of the emitted microwave radiation around the frequency of 1.4 GHz (L-band). SMOS will carry the first-ever, polar-orbiting, space-borne, 2-D interferometric radiometer and will be launched in early 2009. Like whatsoever remotely-sensed geophysical parameter estimation, the retrieval of salinity is an inverse problem that involves the minimization of a cost function. In order to ensure a reliable estimation of this variable, all the other parameters affecting the measured brightness temperature will have to be taken into account, filtered or quantified. The overall retrieved product will thus be salinity maps in a single satellite overpass over the Earth. The proposed accuracy requirement for the mission is specified as 0.1 ‰ after averaging in a 10-day and 2ºx2º spatio-temporal boxes. In this Ph.D. Thesis several studies have been performed towards the determination of an ocean salinity error budget within the SMOS mission. The motivations of the mission, the rationale of the measurements and the basic concepts of microwave radiometry have been described along with the salinity retrieval main features. The salinity retrieval issues whose influence is critical in the inversion procedure are: • Scene-dependent bias in the simulated measurements, • Radiometric sensitivity (thermal noise) and radiometric accuracy, • L-band forward modeling definition, • Auxiliary data, sea surface temperature (SST) and wind speed, uncertainties, • Constraints in the cost function, especially on salinity term, and • Adequate spatio-temporal averaging. A straightforward concept stems from the statement of the salinity retrieval problem: different tuning and setting of the minimization algorithm lead to different results, and complete awareness of that should be assumed. Based on this consideration, the error budget determination has been progressively approached by evaluating the extent of the impact of different variables and parameterizations in terms of salinity error. The impact of several multi-sources auxiliary data on the final SSS error has been addressed. This gives a first feeling of the quantitative error that should be expected in real upcoming measurements, whilst, in another study, the potential use of reflectometry-derived signals to correct for sea state uncertainty in the SMOS context has been investigated. The core of the work concerned the overall SSS Error Budget. The error sources are consistently binned and the corresponding effects in terms of the averaged SSS error have been addressed in different algorithm configurations. Furthermore, the results of a salinity horizontal variability study, performed by using input data at increasingly variable spatial resolution, are shown. This should assess the capability of retrieved SSS to reproduce mesoscale oceanographic features. Main results and insights deriving from these studies will contribute to the definition of the salinity retrieval algorithm baseline.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2009.
Includes bibliographical references (p. 469-488).
Soil moisture is a critical hydrosphere state variable that links the global water, energy, and carbon cycles. Knowledge of soil moisture at scales of individual hillslopes (10's to 100's of meters) is critical to advancing applications such as landslide prediction, rainfall-runoff modeling, and wildland fire fuel load assessment. This thesis develops a data assimilation framework that employs the ensemble Kalman Filter (EnKF) to estimate the spatial distribution of soil moisture at hillslope scales by combining uncertain model estimates with noisy active and passive L-band microwave observations. Uncertainty in the modeled soil moisture state is estimated through Monte Carlo simulations with an existing spatially distributed ecohydrology model. Application of the EnKF to estimate hillslope-scale soil moisture in a watershed critically depends on: (1) identification of factors contributing to uncertainty in soil moisture, (2) adequate representation of the sources of uncertainty in soil moisture, and (3) formulation of an observing system to estimate the geophysically observable quantities based on the modeled soil moisture. Uncertainty in the modeled soil moisture distribution arises principally from uncertainty in the hydrometeorological forcings and imperfect knowledge of the soil parameters required as input to the model. Three stochastic models are used in combination to simulate uncertain hourly hydrometeorological forcings for the model. Soil parameter sets are generated using a stochastic approach that samples low probability but potentially high consequence parameter values and preserves correlation among the parameters. The observing system recognizes the role of the model in organizing the factors effecting emission and reflection of L-band microwave energy and emphasizes the role of topography in determining the satellite viewing geometry at hillslope scales.
(cont.) Experiments in which true soil moisture conditions were simulated by the model and used to produce synthetic observations at spatial scales significantly coarser than the model resolution reveal that sequential assimilation of observations improves the hillslope-scale near-surface moisture estimate. Results suggest that the data assimilation framework is an effective means of disaggregating coarse-scale observations according to the model physics represented by the ecohydrology model. The thesis concludes with a discussion of contributions, implications, and future directions of this work.
by Alejandro Nicolas Flores.
Ph.D.

The Soil Moisture and Ocean Salinity (SMOS) mission is an Earth Explorer Opportunity mission from the European Space Agency (ESA). It was a direct response to the global observations of soil moisture and ocean salinity. Its goal is to produce global of these parameters using a dual-polarization L-band interferometric radiometer the Microwave Imaging Radiometer by Aperture Synthesis (MIRAS). This instrument is a new polarimetric two-dimensional (2-D) Y-shaped synthetic aperture interferometric radiometer based on the techniques used in radio-astronomy to obtain high resolution avoiding large antenna structures. MIRAS measures remotely the brightness temperature (TB) emitted by the Earth's surface, which is not isotropic, since it depends on the incidence angle and polarization, the Soil Moisture (SM) or the Sea Surface (SSS), the surface roughness etc. among others. The scope of this doctoral thesis is the study of some potential improvements could eventually be implemented in future interferometric radiometers. To validate improvements a ground-based instrument concept demonstrator the Passive Advanced Unit Synthetic Aperture or (PAU-SA) has being designed and implemented. Both MIRAS and PAU-SA are Y-shaped array, but the receiver topology and the processing unit are different. This Ph.D. thesis has been developed in the frame of The European Investigator Awards (EURYI) 2004 project entitled "Passive Advanced Unit (PAU): Hybrid L-band Radiometer, GNSS Refectometer and IR-Radiometer for Passive Sensing of the Ocean", and supported by the European Science Foundation (ESF).

L'estimació dels recursos hídrics de les superfícies continentals a escala regional i global és fonamental per a una bona gestió dels recursos hídrics. Aquesta estimació cobreix una àmplia gamma de temes i camps, incloent-hi la caracterització dels sòls i dels recursos hídrics a l’escala de la conca, la modelització hidrològica i la predicció i la cartografia d'inundacions. En aquest context, la caracterització dels estats de la superfície continental, per a obtenir millors paràmetres d’entrada als models hidrològics, és essencial per millorar la precisió en la simulació de cabals, sequeres i inundacions. L’estimació del contingut d’aigua en el sistema, incloses les diferents masses d’aigua i l’aigua lliure en el sòl, és especialment necessària per a una descripció precisa dels processos hidrològics i, en general, del cicle de l’aigua a les superfícies continentals. Per caracteritzar millor els processos hidrològics, les intervencions antropogèniques no es poden negligir. L'home influeix en el cicle de l'aigua, principalment mitjançant el reg i la construcció de preses, fet que s’ha de quantificar correctament. L’objectiu de la tesi és la millora de l’estimació remota dels recursos hídrics, incloent-hi la quantificació dels factors antròpics, mitjançant l’ús de diversos sensors llançats recentment, aprofitant recents desenvolupaments en la tecnologia de teledetecció. Amb l'arribada de les constel·lacions Sentinel (Sentinel-1, 2, 3), disposem de millors eines per estimar els recursos hídrics, incloent-hi els impactes humans, amb una major precisió i cobertura. Aquest treball de tesi consta principalment de dues línies de recerca on s’estimen les intervencions humanes en el cicle hidrològic: la cartografia del reg (com a aplicació en humitat del sòl), i el forçament d’embassaments en simulacions hidrològiques (com a aplicació de l’altimetria). En la primera linia s’estima la humitat del sòl a partir de l’anàlisi estadística de les dades SAR de Sentinel-1. Es desenvolupen dues metodologies per obtenir la humitat del sòl amb una resolució espacial de 100 m basant-se en la interpretació de les dades de Sentinel-1 obtingudes amb la polarització VV (vertical-vertical), que es combina amb dades òptiques Sentinel-2 per a l'anàlisi dels efectes de la vegetació. Com aplicació de la humitat del sòl, es cartografia el reg en diverses condicions meteorològiques, i amb una alta resolució espacial i temporal. Es proposa una metodologia per a la cartografia del reg mitjançant dades SAR obtingudes en polaritzacions VV (vertical-vertical) i VH (vertical-horitzontal). A partir de la sèrie temporal Sentinel-1, s’analitzen diferents estadístiques i mètriques, incloent-hi el valor mitjà, la variància del senyal, la longitud de la correlació i la dimensió fractal, a partir dels quals es classifiquen els arbres irrigats, els cultius irrigats i els cultius no irrigats. En la segona línia, s’estima el nivell dels embassaments a partir de les dades d’altimetria de Sentinel-3, amb l’altímetre SAR (SRAL), basant-se en diferents algorismes per millorar la precisió. Aquest estudi presenta tres algorismes especialitzats o retrackers destinats a obtenir el nivell de la superfície dels cossos d’aigua estudiats, minimitzant la contaminació de les formes d’ona degut al sòl que els envolta. Es compara el rendiment del mètode proposat de selecció de la porció d’ona amb tres retrackers, és a dir, un retracker de llindar, el retracker del centre de gravetat (OCOG) i un retracker de base física de dos passos. S’obtenen sèries temporals del nivell de la làmina d’aigua d’embassaments situats a la conca del riu Ebre (Espanya). Com aplicació, les sèries de nivell dels embassaments obtingudes s’utilitzen per a forçar els embassaments en simulacions hidrològiques.
La estimación de los recursos hídricos de las superficies continentales a escala regional y global es fundamental para una buena gestión de los recursos hídricos. Esta estimación cubre una amplia gama de temas y campos, incluyendo la caracterización de los suelos y de los recursos hídricos a escala de cuenca, la modelización hidrológica y la predicción y la cartografía de inundaciones. En este contexto, la caracterización de los estados de la superficie continental, para obtener mejores parámetros de entrada para los modelos hidrológicos, es esencial para mejorar la precisión en la simulación de caudales, sequías e inundaciones. La estimación del contenido de agua en el sistema, incluidas las diferentes masas de agua y el agua libre en el suelo, es especialmente necesaria para una descripción precisa de los procesos hidrológicos y, en general, del ciclo del agua en las superficies continentales. Una caracterización precisa de los procesos hidrológicos requiere no descuidar las intervenciones humanas. El hombre influye en el ciclo del agua, principalmente mediante el riego y la construcción de embalses, lo que se debe cuantificar correctamente. El objetivo de la tesis es la mejora de la estimación remota de los recursos hídricos, incluyendo la cuantificación de los factores humanos, mediante el uso de varios sensores lanzados recientemente, aprovechando recientes desarrollos en la tecnología de teledetección. Con la llegada de las constelaciones Sentinel (Sentinel-1, 2, 3), disponemos de mejores herramientas para estimar los recursos hídricos, incluyendo los impactos humanos, con una mayor precisión y cobertura. Este trabajo de tesis consta principalmente en dos ejes de investigación donde se estiman las intervenciones humanas en el ciclo hidrológico: la cartografía del riego (como aplicación en humedad del suelo), y el forzamiento de embalses en simulaciones hidrológicas (como aplicación de la altimetría). En relación al primer eje, se estima la humedad del suelo a partir del análisis estadístico de los datos SAR de Sentinel-1. Se desarrollan dos metodologías para obtener la humedad del suelo con una resolución espacial de 100 m basándose en la interpretación de los datos de Sentinel-1 obtenidas con la polarización VV (vertical-vertical), que se combina con datos ópticas Sentinel-2 para el análisis de los efectos de la vegetación. Como aplicación de la humedad del suelo, se cartografía el riego en diversas condiciones meteorológicas, y con una alta resolución espacial y temporal. Se propone una metodología para la cartografía del riego mediante datos SAR obtenidos en polarizaciones VV (vertical-vertical) y VH (vertical-horizontal). A partir de la serie temporal Sentinel-1, se analizan diferentes estadísticas y métricas, incluyendo el valor medio, la varianza de la señal, la longitud de la correlación y la dimensión fractal, a partir de los cuales se clasifican los árboles irrigados, los cultivos irrigados y los cultivos no irrigados. En el segundo eje, se estima el nivel de los embalses a partir de los datos de altimetría de Sentinel-3, con el altímetro SAR (SRAL), basándose en diferentes algoritmos para mejorar la precisión. Este estudio presenta tres algoritmos especializados o retrackers destinados a obtener el nivel de la superficie de los cuerpos de agua estudiados, minimizando la contaminación de las formas de onda debido al suelo que los rodea. Se compara el rendimiento del método propuesto de selección de la porción de onda con tres retrackers, es decir, un retracker de umbral, el retracker del centro de gravedad (OCOG) y un retracker de base física de dos pasos. Se obtienen series temporales del nivel de la lámina de agua de embalses situados en la cuenca del río Ebro (España). Como aplicación, las series de nivel de los embalses obtenidas se utilizan para forzar los embalses en simulaciones hidrológicas.
The estimation of the water resources of the continental surfaces at a regional and global scale is fundamental for good water resources management. This estimation covers a wide range of topics and fields, including the characterisation of soils and water resources at the basin scale, hydrological modelling and flood prediction and mapping. In this context, the characterisation of the states of the continental surface, to obtain better input parameters for hydrological models, is essential to improve the precision in the simulation of flows, droughts, and floods. The estimation of the water content in the system, including the different water bodies and the free water in the soil, is especially necessary for a precise description of the hydrological processes and, in general, of the water cycle on the continental surfaces. To better characterise hydrological processes, human interventions cannot be neglected. Humans influence the water cycle, mainly through irrigation and the construction of reservoirs, which must be correctly quantified. The objective of the thesis is the improvement of the remote estimation of water resources, including the quantification of human factors, using several sensors recently launched, taking advantage of recent developments in remote sensing technology. With the arrival of the Sentinel constellations (Sentinel-1, 2, 3), we have better tools to estimate water resources, including human impacts, with greater precision and coverage. This thesis consists mainly of two parts where human interventions in the water cycle are considered: irrigation cartography (as an application of soil moisture), and the forcing of reservoirs in hydrological simulations (as an application of altimetry). Firstly, soil moisture is estimated from the statistical analysis of Sentinel-1 SAR data. Two methodologies are developed to obtain soil moisture with a spatial resolution of 100 m based on the interpretation of Sentinel-1 data collected with the VV polarization (vertical-vertical), which is combined with optical data of Sentinel-2 for the analysis of the effects of vegetation. Secondly, irrigation is mapped under various meteorological conditions, including high spatial and temporal resolution. A methodology for irrigation mapping is proposed using SAR data obtained in VV (vertical-vertical) and VH (vertical-horizontal) polarizations. With Sentinel-1 time series, different statistics and metrics are analysed, including the mean value, the variance of the signal, the correlation length and the fractal dimension, based on which the classification of irrigated trees, irrigated crops, and non-irrigated crops are derived. Finally, the level of the reservoirs is estimated from the Sentinel-3 altimetry data, with the SAR altimeter (SRAL), based on different algorithms to improve the accuracy. This study presents three specialised algorithms or retrackers designed to obtain the level of the surface of the studied inland bodies of water, minimising the contamination of the waveforms due to the surrounding soil. The performance of the selection method of the proposed wave portion is compared with three retrackers, that is, the centre of gravity retracker (OCOG) and the two-step physical-based retracker. Temporal series of the water level of reservoirs located in the basin of the Ebro River (Spain) are obtained. As an application, the level series of the reservoirs obtained are used to force the reservoirs in hydrological simulations.
L'estimation et le suivi des ressources en eau des surfaces continentales aux niveaux régional et global est essentielle pour la gestion du bilan hydrique, particulièrement dans le contexte des changements climatiques et anthropiques. Ils couvrent un large éventail de thèmes et de domaines, incluant la caractérisation des ressources en eau à l'échelle du bassin, la modélisation hydrologique ainsi que la prévision et la cartographie des inondations. Dans ce contexte, la caractérisation des états de surface, en tant que paramètres d’entrée dans les modèles hydrologiques, est essentielle pour obtenir une meilleure précision de la simulation, qui est liée à la précision prévisionnelle des débits des cours d’eau et le suivi des sécheresses et des inondations. L'estimation de la teneur en eau des surfaces continentales, incluant l’état hydrique du sol et les niveaux des surfaces couvertes d’eau, est particulièrement nécessaire pour une description précise des processus hydrologiques et plus généralement du cycle de l'eau sur les surfaces continentales. Afin de mieux comprendre les processus hydrologiques, l'influence humaine (l’effet anthropique) sur le cycle de l'eau nécessite une évaluation fine. Elle est particulièrement liée à la gestion de l’irrigation et la construction de barrages. L'objectif de la thèse était d'améliorer l'estimation des ressources en eau et une meilleure caractérisation des interventions anthropiques à travers l'utilisation de nouveaux capteurs satellitaires multi-configurations du programme européen Copernicus. Avec le développement de la technologie de télédétection spatiale, et plus particulièrement avec l’arrivée des constellations Sentinel (Sentinel-1, 2, 3) à haute résolution spatiale et temporelle, il existe un meilleur outil pour estimer les états des surfaces continentales. Ce travail de thèse comprend principalement deux priorités liées à des interventions humaines dans le cycle hydrologique:la cartographie de l'irrigation en tant que action humaine liée directement à l'humidité du sol et le forçage des barrages dans un modèle de simulation de rivière en tant qu'application liée à l’estimation du niveau de l'eau libre. Un premier axe de recherche a été basé sur une analyse statistique des données SAR Sentinel-1 pour caractériser l’état hydrique du sol. Deux méthodes ont été développées pour estimer ce paramètre avec une résolution spatiale de 100 m. Elles sont basées sur des approches de détection de changement à partir des données Sentinel-1 acquises en polarisation VV (verticale-verticale), combinées aux données optiques Sentinel-2 pour corriger les effets de la végétation. L’application consistait à cartographier l'irrigation, avec des résolutions spatiale et temporelle élevées. Une méthodologie de cartographie de l'irrigation utilisant des données SAR Sentinel-1 a été proposée. Elle estbasée sur les acquisitions en polarisations VV (vertical-vertical) et VH (vertical-horizontal). A partir de la série temporelle des mesures Sentinel-1, des paramètres statistiques tel que la valeur moyenne, la variance du signal, la longueur de corrélation temporelle et la dimension fractale, sont analysées, en fonction du type de culture; cultures annuelles irriguées, arbres irrigués et cultures pluviales. Des classifications supervisées utilisant les approches Random Forest et SVM sont testées. En deuxième axe, l'estimation de la hauteur de la surface de l'eau à partir des données altimétriques de Sentinel-3 avec l’altimètre SAR (SRAL) a été réalisée à l'aide de différents algorithmes afin d'améliorer la précision sur des petites surfaces. Cette étude présente trois algorithmes spécialisés (ou retrackers) dédiées à la minimisation de la contamination des sols par les formes d’ondes permettant de récupérer les niveaux d’eau à partir de données altimétriques SAR sur des masses d’eaux intérieures. Les performances de la méthode de sélection de portion de forme d'onde proposée avec trois retrackers, à savoir, le retracker à seuil, le retracker à centre de gravité décalé (OCOG) et le retracker à base physique à 2 étapes, sont comparées. Des séries chronologiques de niveaux d'eau sont extraites pour les masses d'eau du bassin de l'Èbre (Espagne). Une application des produits altimétriques est proposée. Le produit de niveau d’eau a été utilisé comme paramètre d’entrée pour analyser l’effet tampon des barrages dans les simulations de débits fluviaux.

Spatial-temporal distribution of soil moisture in the vadose zone is an important aspect of the hydrological cycle that plays a fundamental role in water resources management, including modeling of water flow and mass transport. The vadose zone is a critical transfer and storage compartment, which controls the partitioning of energy and mass linked to surface runoff, evapotranspiration and infiltration. This dissertation focuses on integrating hydraulic characterization methods with remote sensing technologies to estimate the soil moisture distribution by modeling the spatial coverage of soil moisture in the horizontal and vertical dimensions with high temporal resolution. The methodology consists of using satellite images with an ultrafine 3-m resolution to estimate soil surface moisture content that is used as a top boundary condition in the hydrologic model, SWAP, to simulate transport of water in the vadose zone. To demonstrate the methodology, herein developed, a number of model simulations were performed to forecast a range of possible moisture distributions in the Everglades National Park (ENP) vadose zone. Intensive field and laboratory experiments were necessary to prepare an area of interest (AOI) and characterize the soils, and a framework was developed on ArcGIS platform for organizing and processing of data applying a simple sequential data approach, in conjunction with SWAP. An error difference of 3.6% was achieved when comparing radar backscatter coefficient (σ0) to surface Volumetric Water Content (VWC); this result was superior to the 6.1% obtained by Piles during a 2009 NASA SPAM campaign. A registration error (RMSE) of 4% was obtained between model and observations. These results confirmed the potential use of SWAP to simulate transport of water in the vadose zone of the ENP. Future work in the ENP must incorporate the use of preferential flow given the great impact of macropore on water and solute transport through the vadose zone. Among other recommendations, there is a need to develop procedures for measuring the ENP peat shrinkage characteristics due to changes in moisture content in support of the enhanced modeling of soil moisture distribution.

Le satellite altimétrique interférométrique SWOT, dont le lancement est prévu pour 2020, devrait pour la première fois permettre une couverture globale en quelques jours d'un radar proche-nadir en utilisant la bande de fréquence Ka. Or, l'utilisation d'une telle bande de fréquence est encore mal documentée pour l'hydrologie continentale. En particulier, le contraste du coefficient de rétrodiffusion sur les sols et sur les surfaces en eau pour des angles de visée nadir et proche nadir est une problématique majeure. C'est ce qui fait l'objet de ces travaux de thèse. Dans un premier temps, l'étude de la variation des coefficients de rétrodiffusion en provenance d'altimètres en bandes C et Ku et de diffusiomètres utilisant les mêmes bandes de fréquence sur l'Afrique de l'Ouest a montré qu'il existe un lien quantifiable entre l'humidité du sol et le coefficient de rétrodiffusion. En région semi-aride ce lien se manifeste via une hausse des coefficients de rétrodiffusions durant la saison humide par rapport à la saison sèche. L'analyse avec des données annexes d'humidité du sol et de précipitations a pu également montrer que le radar nadir détecte plus précisément les changements d'humidité du sol par rapport à la diffusiométrie radar à visée latérale. Dans le but de mieux comprendre la rétrodiffusion en bande Ka, très peu documentée, deux campagnes de mesures radar ont été réalisées, l'une sur des surfaces en eau à rugosité contrôlée, l'autre sur un terrain contrôlé en rugosité et humidité du sol. En parallèle, un programme de simulation de la rétrodiffusion altimétrique a été développé pour pouvoir analyser les effets d'un faible nombre de variables sur des sols réalistes, dans le but de simuler les variations du coefficient de rétrodiffusion. Ces mesures et ces simulations ont ensuite pu être comparées aux séries temporelles issues du satellite altimétrique AltiKa, fonctionnant en bande Ka et lancé en février 2013, sur différents sites représentatifs des régions bio-climatiques d'Afrique de l'Ouest. Il en ressort que la bande Ka présente une forte sensibilité aux changements d'humidité du sol. Il est également montré que les coefficients de rétrodiffusion en provenance d'AltiKa sur les sols et sur l'eau peuvent être similaires au nadir
The radar altimetry interferometry satellite SWOT, which is to be launched in 2020, should provide for the first time a global coverage of a close-to-nadir radar altimeter in a few days using the Ka-band. Yet, the use of such a frequency band for continental hydrology is still poorly documented. In particular, the contrast of the backscattering coefficient over soils and over water bodies for nadir and close-to-nadir angles is a major issue. This is the reason for this work. First, the study of the backscattering coefficients from C- and Ku- band altimeters and scatterometers over West Africa has shown that there is a link between the surface soil moistureand the backscattering coefficient. In semi-arid regions, this link is seen through a rise of the backscattering coefficients during the rainy season compared to the dry season. The analysis with ancillary data such as the surface soil moisture and the precipitation estimations has also shown that nadir-looking radars detect more precisely the changes in surface soil moisture compared to side-looking radars. Still with the purpose to better understand the Ka-band surface scattering, poorly documented, two measurement campaigns were led, on the one hand over water surface with controlled roughness, on the other hand over bare soils with monitored roughness and surface soil moisture. In parallel, an altimetry backscattering simulation program has been developed to analyze the effect of a low number of variables on realistic grounds, with the aim of simulating the backscattering coefficient variations. These measurements and simulations were then compared with time series from the satellite altimeter AltiKa, which has been launched in 2013 and works at Ka-band, over different sites representaing the bioclimatic areas of West Africa. It led to a high sensitivity of the Ka-band to changes in the surface soil moisture. It has also been shown that backscattering coefficients at nadir-looking angle from AltiKa over grounds and over water bodies can be similar

L’humidité du sol à été déclarée Essential Climate Variable (ECV) en 2010 par l’European Space Agency (ESA) en support du travail du Groupe d'experts Intergouvernemental sur l'Évolution du Climat (GIEC). Dans des zones vulnérables comme l’Afrique de l’Ouest (agriculture faiblement irriguée et de subsistance, températures extrêmes et forte variabilité des précipitations), la valeur ajoutée d’informations concernant l’humidité du sol est importante, surtout dans un contexte de changement climatique. La première partie de ces travaux de thèse concerne la représentation de l'humidité en profondeur à grande échelle en utilisant le triptyque mesures in-situ/télédétection/modélisation. Ces 3 méthodes présentent chacune des limites: (i) la faible densité des réseaux in-situ (3 sites de mesures sur toute l'Afrique de l'Ouest), (ii) les estimations de SMOS uniquement en surface (0-5 cm) et (iii) les incertitudes des forçages de précipitation temps-réel utilisés dans les modèles de surface. Afin de réduire ces limitations, une méthode d'assimilation (filtre particulaire) des données SMOS à été implémentée dans un modèle de surface empirique (API) et comparées aux mesures in-situ AMMA-CATCH. Les résultats montrent une amélioration des humidités modélisée après assimilation. La seconde partie concerne l'impact des variations d'humidité du sol sur les rendements de mil. Une relation statistique a tout d'abord été déterminée à partir de données de rendements mesurés sur 10 villages autour de Niamey. Les résultats montrent que les anomalies d'humidité du sol sur 20 jours début Juillet et fin Août - mi Septembre (période reproductive et période de remplissage du grain), à une profondeur d'environ 30 cm, expliquent les variations de rendement mesuré à R2=0.77 sur l'ensemble de 9 villages. Cette relation à ensuite été appliquée à l'échelle du Niger à partir de données de rendement issues de la FAO et de cartes d'humidité en profondeur développées dans la première partie de la thèse. Les résultats montrent une corrélation à R2=0.62 sur les années 1998-2014. Puis la méthode a été appliquée à 3 autres pays du Sahel, montrant une corrélation de 0.77. La dernière partie de ces travaux concerne l'exploitation des résidus du schéma d'assimilation afin de réduire les incertitudes sur les précipitations. Les produits de précipitations satellites CMORPH, TRMM et PERSIANN, dans leur version temps-réel ont été comparées à des pluviomètres avant et après assimilation. Le résultat de cette étude montre une nette amélioration des intensités estimées. La méthode a ensuite été appliquée à un produit de précipitation utilisé au centre régional AGRHYMET pour le suivi agricole, le produit TAMSAT.Ces travaux de thèse ont permis d'approfondir les recherches concernant le potentiel des données d'humidité par satellite pour des applications agronomiques. Les perspectives de ces travaux portent principalement sur : (i) l'utilisation d'autres capteurs (SMAP, ASCAT, AMSR) pour augmenter la fréquence des observations d'humidité dans l'assimilation, (ii) sur des méthodes de désagrégation des coefficients pour la correction des précipitations à plus haute résolution spatiale et (iii) sur l'utilisation de données multispectrales (indices de végétation, température du sol, ...) pour un meilleur suivi des rendements
Soil moisture was declared Essential Climate Variable (ECV) in 2010 by the European Space Agency (ESA) in support of the work of the Intergovernmental Panel on Climate Change (IPCC). In vulnerable areas such as West Africa (poorly irrigated and subsistence agriculture, extreme temperatures and high variability of rainfall), the added value of informations on soil moisture is important, especially in a changing climate. The first part of this thesis concerns the representation of root-zone soil moisture on a large scale using the triptych in-situ measurements / remote sensing / modeling. These 3 methods each have limitations: (i) the low density of in-situ networks (3 measurement sites throughout West Africa), (ii) SMOS estimates only at the surface (0-5 cm) and (iii) the uncertainties of the real-time precipitation forcing used in surface models. In order to reduce these limitations, an assimilation method (particle filter) of SMOS data has been implemented in an empirical surface model (API) and compared to AMMA-CATCH in-situ measurements. The results show an improvement of the humidities modeled after assimilation. The second part concerns the impact of soil moisture variations on millet yields. A statistical relationship was first determined from yield data measured in 10 villages around Niamey. The results show that the 20-day soil moisture anomalies at the beginning of July and the end of August - mid September (reproductive period and grain filling period), at a depth of about 30 cm, explain the variations in yield measured at R2=0.77. This relationship was then applied to the Nigerien scale from FAO yield data and in-depth moisture maps developed in the first part of the thesis. The results show a correlation at R2=0.62 over the years 1998-2015. Then, the method was apply to 3 other sahelian countries, showing a agreement of 0.77. The last part of this work concerns the exploitation of the residuals of the assimilation scheme in order to reduce the uncertainties on the precipitations. The satellite precipitation products CMORPH, TRMM and PERSIANN, in their real-time version, were compared to rain gauges before and after assimilation. The result of this study shows a marked improvement in the estimated precipitations intensities. The method was then applied to a precipitation product used at the AGRHYMET regional center for agricultural monitoring, the TAMSAT product.This thesis work has led to further research into the potential of satellite moisture data for agronomic applications. The perspectives of this work mainly concern: (i) the use of other sensors (SMAP, ASCAT, AMSR) to increase the frequency of the observations of humidity in the assimilation, (ii) on methods of disaggregation of the coefficients for the correction of precipitation at higher spatial resolution and (iii) the use of multispectral data (vegetation indices, soil temperature, ...) for a better monitoring of yields

Dans les régions de haute biodiversité, caractérisées par des paysages dynamiques, la cartographie détaillée de l'utilisation des sols et du couvert végétal est communément obtenue par la classification d'images satellite. Cependant, les cadres conceptuels d'estimation d'erreurs sur les cartes sont éprouvés pour les zones tempérées et hautement industrialisées. Une nouvelle méthode est proposée pour l'évaluation de la fiabilité des cartes et une autre méthode pour l'estimation des erreurs de classification par ambiguïtés entre classes sur images satellites. La première méthode comprend un nouveau mode d'échantillonnage et une estimation par ensembles flous des incertitudes positionnelles et thématiques. Elle a été testée sur l'Inventaire Forestier Mexicain de l'an 2000. La deuxième méthode s'appuie sur la simulation d'images satellites avec le modèle de transfert radiatif DART et a été testée sur des images IKONOS de six types de forêts au Mexique, sur terrain plat et en forte pente
In highly bio-diverse regions, characterized by dynamic landscapes, detailed land use and land cover maps are commonly generated through the classification of remote sensing imagery. Yet, frameworks for the estimation of errors on maps have focused on mainly temperate zones, in highly industrialized countries. We propose a new method for the accuracy assessment of maps and a new method for the systematic estimation of ambiguities on satellite imagery. The first method comprises a novel sampling scheme and a fuzzy sets based framework whereby degrees of positional and thematic tolerance are user defined. The second method is based on structural and optical measurements and the simulation of satellite imagery with the Discrete Anisotropic Radiative Transfer (DART) model. The first method was successfully tested on the Mexican National Forest Inventory map of year 2000 and the second method was successfully tested on real IKONOS imagery of forest cover types on flat and rugged terrain

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A observaÃÃo da superfÃcie terrestre por meio de satÃlites em Ãrbita de nosso planeta tornou-se corriqueira no mundo contemporÃneo. As inferÃncias de variÃveis ambientais diversas feitas a partir de imagens e dados fornecidos por satÃlites cada vez mais aumentam em qualidade e aplicabilidade de maneira que um nÃmero crescente de hidrologistas, meteorologistas, climatologistas e outros profissionais e leigos em geral fazem uso intensivo delas em estudos e pesquisas, em polÃticas governamentais ou na tomada de decisÃo. Uma dessas variÃveis à a umidade superficial do solo, que representa uma importante componente do ciclo hidrolÃgico terrestre, essencial em vÃrios processos naturais ambientais e cujo conhecimento à importante no gerenciamento dos recursos hÃdricos e terrestres, gerenciamento agrÃcola e na modelagem do meio ambiente e agrÃcola. As informaÃÃes derivadas de satÃlites, apesar de ainda apresentarem algumas limitaÃÃes tÃcnicas, podem facilitar bastante o monitoramento ambiental ao se tornarem, muitas vezes, mais Ãgeis e mais econÃmicas do que mediÃÃes locais in situ. Em paÃses em desenvolvimento e de limitados recursos financeiros, tais como o nosso, a informaÃÃo por satÃlites cresce em valor. No Estado do CearÃ, isso desponta ainda mais em virtude das suas dificuldades econÃmicas e sociais. Em vista disso, à proposta, neste trabalho, a aplicaÃÃo nesse estado do Nordeste de um mÃtodo de inferÃncia, por satÃlite, do estado geral da umidade superficial do solo expresso pelo Ãndice de Seca Temperatura-VegetaÃÃo (ISTV), que à indicativo do grau da umidade, estando a ela relacionado. Esse Ãndice à obtido a partir da combinaÃÃo de informaÃÃes da Temperatura da SuperfÃcie Continental (TSC) e do Ãndice de VegetaÃÃo por DiferenÃa Normalizada (IVDN), inferidos por meio de imagens no visÃvel e no infravermelho que podem ser fornecidas por satÃlites meteorolÃgicos operacionais, de Ãrbita polar, tais como os da sÃrie NOAA (National Oceanic and Atmospheric Administration, EUA). No mÃtodo, foi escolhido, da literatura cientÃfica, um algoritmo de cÃlculo da TSC que apresenta certa facilidade de uso, sendo diretamente dependente da FraÃÃo de Cobertura de VegetaÃÃo (FCV) e que pode fornecer boas inferÃncias dessa temperatura. Nesse algoritmo, foram testadas, de forma inÃdita, algumas diferentes formulaÃÃes da FCV encontradas na literatura especializada, representando uma delas o estado da arte no assunto. Foram usadas imagens provenientes do satÃlite NOAA-17, recepcionadas na FUNCEME, e um software especÃfico, dessa FundaÃÃo, para se processar as imagens e implementar a metodologia abordada. Alguns testes foram feitos para duas regiÃes relativamente pequenas do semiÃrido cearense, com destaque para uma delas englobando a Bacia Experimental de Aiuaba (BEA), comparando-se as informaÃÃes do satÃlite NOAA-17 com dados in situ (provenientes de sondas no solo) e com dados advindos dos satÃlites ambientais Terra (dados de TSC, disponÃveis na Internet) e Aqua (dados de umidade superficial do solo). Procurou-se mostrar as diferenÃas qualitativas entre os mapeamentos obtidos, de umidade superficial do solo, e entre estes e os oferecidos pela modelagem em geral. Os resultados encontrados mostraram-se promissores para a utilizaÃÃo no territÃrio cearense do ISTV (no modelo trapezoidal) por meio de satÃlites NOAA, com o algoritmo de Kerr para o cÃlculo da TSC e com a FCV dada pelo Scaled Difference Vegetation Index (SDVI), com o fim de se estimar o estado geral da umidade superficial do solo sobre grandes Ãreas. Entretanto, recomenda-se mais validaÃÃo local posterior do mÃtodo usado, para detecÃÃo de possÃveis erros ou limitaÃÃes nÃo vislumbradas nestes primeiros testes, visando sua definitiva aplicaÃÃo operacional no Cearà e mesmo no semiÃrido do Nordeste.

La télédétection par satellite est aujourd'hui une composante à part entière de l'océanographie. Elle permet d'effectuer des mesures de vents, de température de surface (SST), de couleur de l'eau, de topographie, ... avec des couvertures spatiales et temporelles bien supérieures à celles obtenues par des méthodes in situ. Cependant, il n'existe pas à l'heure actuelle de mesure satellitaire de salinité de surface des océans (SSS), et celle-ci reste sous échantillonnée à la fois spatialement et temporellement. La salinité étant un paramètre important pour la circulation des masses d'eau océaniques, son observation globale et régulière constituerait un apport conséquent à l'océanographie physique. C'est pourquoi de nombreuses équipes scientifiques à travers le monde relèvent actuellement le défi technologique de la télédétection de la SSS par satellite, et particulièrement en Europe grâce à la mission de l'Agence Spatiale Européenne « Soil Moisture and Ocean Salinity » (SMOS). Au cours de ma thèse, j'ai étudié la faisabilité de la mesure de la SSS à l'aide d'un radiomètre hyperfréquence en bande L (i.e. fréquence = 1.4 GHz <=> longueur d'onde = 21 cm), en estimant les sources d'incertitude sur la SSS qui sera restituée dans le cadre de la mission SMOS. Pour cela, j'ai codé un modèle direct, qui simule les processus physiques intervenant depuis la surface océanique jusqu'à l'antenne du radiomètre. Ce modèle est constitué d'un modèle d'émissivité de la mer à « deux échelles » (i.e. on distingue les vagues selon qu'elles soient « grandes » ou « petites » par rapport à la longueur d'onde du radiomètre), et d'un modèle de transfert radiatif à travers l'atmosphère. Le modèle d'émissivité m'a permis d'estimer la sensibilité de la température de brillance (Tb) de l'océan aux paramètres géophysique océanique (i.e. SSS, SST, et rugosité de surface induite par le vent ou la houle), ainsi que l'incertitude sur cette sensibilité en comparant les résultats obtenus à partir de paramétrisations différentes. J'ai conclu de ces études que la sensibilité de la Tb à la SSS est relativement bien connue (de l'ordre de quelques dixièmes de Kelvin par psu) mais que l'effet de la rugosité est très incertain à cause de l'imprécision des modèles de spectre des vagues, alors que cet effet ne semble pas être négligeable (la sensibilité de la Tb au vent étant comprise entre 0.12 à 0.25 K/(m/s) selon le modèle de spectre). Le modèle de transfert radiatif m'a permis d'estimer les différentes contributions de l'atmosphère (atténuation des rayonnements la traversant et émission propre), ainsi que la sensibilité de ces contributions aux paramètres atmosphériques (i.e. profils de température, pression et humidité relative). En bande L, l'atmosphère est quasiment transparente (épaisseur optique ~ 0.01 néper) et sa température de brillance est de l'ordre de 2 K. Ces effets sont peu sensibles aux paramètres atmosphériques, particulièrement à la vapeur d'eau. Je présente aussi dans la thèse des comparaisons du modèle avec des mesures radiométriques en bande L récentes (campagnes WISE 2000, WISE 2001 et EuroSTARRS) ainsi que les conclusions sur la validité des différents modèles de spectre de mer étudiés.

L’étude de la diffusion des ondes électromagnétiques par une surface rugueuse aléatoire est de première importance dans de nombreuses disciplines et conduit à diverses applications notamment pour le traitement des surfaces par télédétection. En connaissant les modes de rétrodiffusion, on peut détecter la présence de la rugosité aléatoire indésirable de la surface de réflection telle que le réflecteur d'antenne et par conséquent trouver un moyen de corriger ou compenser les erreurs de phase. Cette thèse porte sur l’obtention de l'humidité du sol de surface à partir de mesures radar. La description de la surface rugueuse de façon aléatoire est présentée, suivie par les interactions d'ondes électromagnétiques avec les média. En particulier, un modèle d'équation intégrale avancé (AIEM) est introduit. La validité du modèle AIEM, qui est adopté comme modèle de travail, se fait par une large comparaison avec des simulations numériques et des données expérimentales. On analyse également les caractéristiques des configurations radar bistatique et on étudie la sensibilité de la diffusion bistatique à l'humidité du sol et à la rugosité de surface et, dans le même temps, le cadre de la détermination de l'humidité du sol à partir de mesures radar utilisant un réseau de neurones à base de filtres de Kalman récurrents est présenté. La formation du réseau et l'inversion des données sont décrits
Electromagnetic waves scattering from a randomly rough surface is of palpable importance in many fields of disciplines and bears itself in various applications spanned from surface treatment to remote sensing of terrain and sea. By knowing the backscattering patterns, one may detect the presence of the undesired random roughness of the reflection surface such as antenna reflector and accordingly devise a means to correct or compensate the phase errors. Therefore, it has been both theoretically and practically necessary to study the electromagnetic wave scattering from the random surfaces. This dissertation focuses on the retrieval of surface soil moisture from radar measurements. The description of the randomly rough surface is presented, followed by the electromagnetic wave interactions with the media. In particular, an advanced integral equation model (AIEM) is introduced. The validity of the AIEM model, which is adopted as a working model, is made by extensive comparison with numerical simulations and experimental data. Also analyzes the characteristics of the bistatic radar configurations and dissects the sensitivity of bistatic scattering to soil moisture and surface roughness of soil surfaces. Meanwhile presents a framework of soil moisture retrieval from radar measurements using a recurrent Kalman filter-based neural network. The network training and data inversion are described in detail

Dans un contexte de développement durable, la gestion des sols et des ressources en eau est un enjeu primordial non seulement d’un point de vue environnemental mais aussi socio-économique. L’humidité, la rugosité, la composition et la structure du sol sont des variables clés pour la compréhension et la modélisation des catastrophes naturelles telles que l’érosion, la sécheresse ou les inondations. Pour des sols nus agricoles (très propices au ruissellement), de nombreuses études ont déjà montré le potentiel des données RADAR acquises en bande C pour la cartographie de l'humidité et la rugosité du sol. Cependant l’application de ces méthodes dans un cadre opérationnel était limitée.Dans ce contexte, les travaux de cette thèse présentent un premier volet sur l’analyse de la sensibilité aux états de surface (EDS) du sol du signal en bande X du capteur TerraSAR-X à très haute résolution spatiale et temporelle. Différentes configurations TerraSAR-X ont été analysées et les résultats ont permis de définir les configurations instrumentales optimales pour caractériser chaque paramètre d’EDS du sol. La comparaison de la sensibilité du capteur TerraSAR-X à celle des capteurs en bande C montre que le capteur TerraSAR-X est sans conteste le plus adapté pour estimer et cartographier l’humidité du sol à des échelles fines (50 m²).Le second volet était de développer une méthode permettant d’estimer et de cartographier l’humidité des sols nus agricoles. Dans ce but, les méthodes d'inversion généralement utilisées en bande C ont été testées sur les données en bande X. La précision sur les estimations d’humidité issues de l'algorithme d’inversion du signal TerraSAR-X a été déterminée et l’applicabilité de la méthode sur de nombreux sites d'étude a été testée avec succès. Une chaine de traitements cartographiques allant de la détection des sols nus à l’estimation de l’humidité et ne nécessitant qu’une seule image TerraSAR-X a été développée. Cette chaine innovante de traitements cartographiques « automatique et autonome » devrait permettre d’utiliser les données TerraSAR-X pour cartographier l’humidité du sol en mode opérationnel
In the context of sustainable development, soil and water resources management is a key issue from not only the environmental point of view, but also from a socioeconomic perspective. Soil moisture, roughness, composition, and slaking crusts are some key variables used to understand and model natural hazards, such as erosion, drought and floods. For agricultural bare soils (most subject to runoff), numerous studies have already shown the potential of C-band RADAR data for the mapping of soil moisture and roughness. However, the application of these methods in operational settings remained limited.In this context, the first objective of this thesis was to analyse the sensitivity of X-band TerraSAR-X sensors to soil surface characteristics (SSC) at high spatial and temporal resolutions. Different TerraSAR-X configurations were evaluated and results were used to define the optimal instrumental configuration for the characterization of each SSC parameter. The comparison of TerraSAR-X sensor sensitivity with equivalent levels recorded with the C-band sensor showed that the TerraSAR-X sensor is undoubtedly the most suitable of the two when estimating and mapping soil moisture at a fine scale (50 m²).The second objective of this work was to develop a method to estimate and map soil moisture levels of agricultural bare soil. To achieve this goal, methods that are commonly used to retrieve soil moisture from C-band, have been tested on X-band data. The accuracy of soil moisture estimations using an empirical algorithm was determined, and validated successfully over numerous study sites. A mapping process based uniquely on TerraSAR-X data, both for bare soil detection and for the estimation of soil moisture content, was developed. This innovative chain of « automatic and autonomous» mapping processing steps should enable the utilization of TerraSAR-X data for the mapping of soil moisture levels in operational conditions

Une etude theorique utilisant un modele de simulation, base sur l'approximation scalaire des champs, a permis d'estimer la sensibilite du coefficient de diffusion a une variation relative des parametres de surface, en fonction des caracteristiques radar. Les resultats ont montre que la configuration, jugee optimale, pour une mesure de l'humidite de surface necessite l'utilisation simultanee de deux polarisations (hh et vv), un angle d'incidence eleve (35**(o)) et une frequence de la bande x

Un regain d'intérêt pour la télédétection de la salinité de surface de l'océan (SSS) par radiométrie en bande-L (21cm) est apparu dans les années 1990 et a conduit au lancement des missions spatiales SMOS (nov. 2009) et Aquarius (juin 2011). Cependant, en raison du faible rapport signal sur bruit, l'inversion de la SSS à partir des mesures radiométriques en bande-L est très difficile. Ce travail porte sur l'étude de la signature radiométrique en bande-L des propriétés de la surface de l'océan (en particulier SSS et rugosité) à partir des mesures du radiomètre aéroporté en bande-L CAROLS, acquises dans le golfe de Gascogne en 2009 et 2010. Une première étude a montré que la SSS déduite des mesures du radiomètre CAROLS était précise à mieux que 0.3 pss dans une zone de forte variabilité spatio-temporelle avec une meilleure précision que les modèles océanographiques côtiers. La seconde étude qui combine les mesures passives (CAROLS) et active (diffusiomètre en bande-C STORM) a mis en évidence l'amélioration des nouveaux modèles de rugosité par rapport aux modèles pré-lancement satellitaires. Par ailleurs, l'étude a montré l'importance de la prise en compte des moyennes et grandes échelles de rugosité (> 20 cm) pour l'interprétation des mesures radiomé- triques loin du nadir.

Microwave remote sensing of the land surface processes has advantages over optical remote sensing such as least attenuation due to the atmosphere, and high temporal resolution among others. Soil moisture is an important land surface variable, which is present at land atmosphere interphase and influences the hydrologic, atmospheric, climate, agricultural, and carbon cycles. Given the challenges involved in measuring the soil moisture at local scale (heterogeneous nature of the soil, financial constraints etc.), microwave remote sensing has assisted the researchers in obtaining the soil moisture at the global scales. This year marks the four decades of research on microwave remote sensing of soil moisture. The microwave sensors can be categorized into two kinds passive (where the Sun acts as the source of energy) and active (where the sensor sends a microwave signal and records the response) sensors. Each has their own pros and cons. This thesis comes under the purview of microwave remote sensing of hydrology. The thesis can be broadly divided into two parts. In the first part, a synthesis of four decades of research and development on the passive and active microwave soil moisture retrieval algorithms is provided. The algorithms associated with passive sensors use the radiometer brightness temperatures, while active sensors use the radar backscatter measurements to retrieve soil moisture. The physics of both algorithm classes are since the microwave measurements at lower frequencies are influenced by the soil dielectric property, which acts as a proxy for the surface soil moisture content. In this review effort, the emphasis is laid on the physical models of the passive and the active retrieval algorithms. These algorithms facilitate to obtain the individual radiative contributions from soil, vegetation, and atmosphere that reach satellite sensors after mixing (roughness), scattering, and attenuation. In the process, the current research efforts to improve individual aspects of the algorithms, followed by a description of different retrieval procedures are investigated. Following a comprehensive review of retrieval algorithms, an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture is presented. The evolution of the products of various sensors over the last four decades is assessed in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (Scanning Multichannel Microwave Radiometer – SMMR, Special Sensor Microwave Imager – SSM/I, Tropical Rainfall Measuring Mission (TRMM) Microwave Imager – TMI, Advanced Microwave Scanning Radiometer – Earth Observing System – AMSR-E, WinSAT, Advanced Microwave Scanning Radiometer 2 – AMSR2, Soil Moisture Ocean Salinity – SMOS and Soil Moisture Active Passive – SMAP), two active sensors (Active Microwave Instrument – Wind Scatter meter ERS, Advanced Scatter meter – ASCAT), and one active/passive merged soil moisture product (European Space Agency-Climate Change Initiative – ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. In addition, inter-satellite comparisons are carried out to study the roles of sensor design and algorithms on the retrieval accuracy. The analysis indicates that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. It is concluded that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences. In the second part of the thesis, the focus is primarily on passive microwave remote sensing of land surface variables. The passive microwave soil moisture retrieval algorithms are in general based on the zeroth order radiative transfer model. If a microwave sensor can measure the brightness temperatures in dual polarizations, they can be used in tandem with the radiative transfer equations to estimate another important land surface variable called the Vegetation Optical Depth (VOD), which closely relates to the water content in foliage and woody components of the above ground biomass. So, mathematically, the dual polarized radiative transfer equations can be inverted to derive the VOD, which in turn acts as an input in the soil moisture retrieval process. It is identified that there exist multiple ways – thus resulting in multiple analytical solutions – by which the radiative transfer equations can be inverted resulting in the problem of equifinality in soil moisture retrieval algorithm. In this thesis, a new analytical solution coined as the Karthikeyan’s solution is proposed, which is used to derive a new set of VOD and soil moisture products based on the X-band (10.65 GHz) AMSR-E brightness temperature observations. The products thus obtained are compared with two products, which in turn are derived using two existing analytical solutions from the literature. Apart from the problem of equifinality, the results from the above work indicate that the VOD retrievals are influenced by high-frequency variability, which may dampen the true signal emitted from vegetation, rendering the VOD retrievals unusable for vegetation related research. In addition to the VOD, specifying appropriately the soil surface roughness parameter is also important while retrieving the soil moisture. Until recently, this parameter was a global constant in the operational retrieval algorithms. With these challenges, the succeeding work in the thesis is designed wherein a new retrieval algorithm, coined as the Simultaneous Parameter Retrieval Algorithm (SPRA), is developed to simultaneously retrieve the VOD, the surface roughness parameter, and the soil moisture at global scale using the Level 3 0.25° X-band brightness temperatures of the AMSR-E sensor. The methodology is based on the premise that the vegetation dynamics undergo slower temporal changes than the soil moisture, an assumption, which has previously been successfully used for microwave radiometric retrievals at lower frequencies. Results indicate that the SPRA produces the VOD retrievals with reduced high-frequency noise when compared to the baseline Land Parameter Retrieval Algorithm (LPRM) retrievals. This effect assisted in identifying the influence of precipitation and cropping patterns on the temporal dynamics of the VOD. The surface roughness parameter indicated a strong dependence on vegetation, followed by the topographic complexity. A precipitation-based validation of SPRA and LPRM soil moisture retrievals over India indicated a better skill of the SPRA product. The improvement in skill is a result of using the new VOD and spatially varying surface roughness retrievals. Given the constraints associated with soil moisture, validating the satellite retrievals are still considered a challenging area of research. To validate the satellite soil moisture products at continental/global scales, a novel validation technique, which validates microwave soil moisture retrievals using precipitation data is proposed. It is based on the concept that the expectation of precipitation conditioned on soil moisture follows a sigmoidal convex-concave shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between soil moisture and precipitation. On this basis, with an emphasis on distribution free – non-parametric computations, a new measure coined as the Copula–Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check the accuracy of any two-soil moisture products (here AMSR-E sensor’s VUA-NASA and U. Montana products) over India using precipitation (IMD) data. The proposed technique yields a ‘best-choice soil moisture product’ map, which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that, in general, VUA-NASA product has performed better over U. Montana’s product for India. The best-choice soil moisture map is then integrated with land use land cover and elevation information using a novel PDF based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (APHRODITE) dataset over the best choice soil moisture product map is also analysed. The proposed methodology assists researchers and practitioners in selecting appropriate soil moisture product for various assimilation strategies at both basin and continental scales. The soil moisture products obtained from the Karthikeyan’s solution and the SPRA are validated over India using the proposed method

碩士
國立屏東科技大學
森林系所
105
Soil moisture is an important feature of hydrological circle that affect the tree growth and vegetation distribution. For this reason, estimation of soil moisture dynamics and distribution patterns of forest land with a long period can provide important information for forest management as well as a reference basis. However, the data which from meteorological station in-situ or field surveys can’t represent dynamic of soil moisture and spatial distribution. Taiwan region was selected as the study area. The satellite images of MODIS were collected from 2008, 2010, 2012, 2014, and 2016, each year were divided into Spring (March to May), Summer (June to August), Autumn (September to October), Winter (November to January), so we had 20 periods images in total. Land surface temperature (LST) and Normalized difference vegetation index (NDVI) were used to estimate Temperature vegetation dryness index (TVDI), which represented the soil moisture index of dryness in this study. After the verification of precipitation and terrain on temporal and space, it is feasible that TVDI could represent soil moisture. Soil moisture of ecological zones in Taiwan was estimated by TVDI. The results showed that the spatial patterns of TVDI in the ecological zones of Taiwan were included the tropical dry forest that is the driest area in order to the low vegetation cover and the moistest area is temperature mountain system. The others ecological zone, the orders of soil moister degree were subtropical mountain systems, tropical moist deciduous forest, tropical rain forest and the tropical moisture deciduous forest that located in Eastern Hengchun Peninsula and the lowland of Kaohsiung.

The knowledge of soil moisture is of pronounced importance in various applications e.g. flood control, agricultural production and effective water resources management. These applications require the knowledge of spatial and temporal variation of the soil moisture in the watershed. There are three approaches of estimating/measuring soil moisture namely,(i) in-situ measurements,(ii) remote sensing, and(iii) hydrological modelling. The in situ techniques of measurement provide relatively accurate information at point scale but are not feasible to gather in large numbers relevant for a watershed. The soil moisture can be simulated by hydrological models at the desired spatial and temporal resolution, but these simulations would often be affected by the uncertainties in the model physics, parameters, forcing, initial and boundary conditions. The remote sensing provides an alternative to retrieve the soil moisture of the surface (top few centimeters ) layer, but even this data is limited by the spatial or temporal resolution, which is satellite dependant. Hydrological models could be improved by assimilating remotely sensed soil moisture, which requires a retrieval algorithm. In order to develop a retrieval algorithm the satellite data need to be calibrated/validated with the in-situ ground measurements. The retrieval of surface soil moisture from microwave remote sensing is sensitive to surface conditions, and hence requires calibration/validation specific to a site/region. The improvement in the hydrological variables/fluxes is sensitive to the framework adopted during the assimilation of remotely sensed data. The main focus of the study was to assess the retrieval algorithm for the surface soil moisture from both active (ENVISAT,RADARSAT-2)and passive(AMSR-E) microwave satellites in a semi-arid tropical watershed of South India. Further, the usefulness of these retrieved remotely sensed products for the estimation of recharge was investigated by developing a coupled hydrological model and an assimilation framework. A brief introduction was made in Chapter 1 on the importance of surface soil moisture and evapotranspiration in hydrology, and the feasible options available for the retrieval from microwave remote sensing. A detailed review of the literature is presented in Chapter 2 to establish the state-of-the-art on the following:(i) retrieval algorithms for the surface soil moisture from active and passive microwave remote sensing,(ii) estimation of actual evapotranspiration from optical remote sensing(MODIS),(iii) coupled surface-ground water hydrological models,(iv) estimation of soil hydraulic properties with their uncertainties, and(v) assimilation framework specific to hydrological modelling. To calibrate/validate the retrieval algorithms and to test the coupled model and the assimilation framework developed, field measurements were carried out in the BerambadI experimental watershed located in the Kabini river basin. The surface soil moisture in 50 field plots, profile soil moisture up to 1m depth in 20 field plots, and ground water level in 200 bore wells were measured. Twelve images of ENVISAT, seven teen images of RADARSAT-2, along with AMSR-E and MODIS data were used. These data pertained to different durations during the period 2008 to 2011,the details of which are given in Chapter 3. The approach for the retrieval of surface soil moisture and the associated uncertainty from active and passive microwave remote sensing is given in Chapter 4. Surface soil moisture was retrieved for six vegetation classes using the linear regression model and copulas. Three types of copulas(Clayton, Frank and Gumbel) were investigated. It was found that the ensemble mean simulated using the linear regression model and three copulas was nearly same. The copulas were found to be superior than the linear regression model when comparing the distributions of the mean of the generated ensemble. Among the copulas it was observed that the Clayton copula performed better in the lower and middle ranges of backscatter coefficient, while the Gumbel and Frank copulas were found to be superior in the upper ranges of backscatter coefficients. The range of RMSE was approximatively 4cm3cm−3 indicating that the retrieval from ENVISAT/RADARSAT-2 was good. ACDF based approach was proposed to retrieve the surface soil moisture map for the watershed with a spatial resolution of 100m x 100m ( i.e one hectare). The map of the uncertainty in the retrieved surface soil moisture was also prepared using the Clayton copula. The AMSR-E surface soil moisture product was calibrated for the watershed during the period 2008 to 2011, using the map generated from the ENVISAT/RADARSAT data. They Clayton copula was used to generate the ensemble of the corrected AMSR-E surface soil moisture. The standard deviation of the generated ensemble varied from 0.01 to 0.03cm3cm−3 ,hence the derived surface soil moisture product for Berambadi was found to be good. In the Chapter 5, a one dimensional soil moisture model was developed based on the numerical solution of the Richards’ equation using finite difference method and inverse modeling was carried out using the Generalized Likelihood Uncertainty Estimation(GLUE) approach for estimating the soil hydraulic parameters of the van Genuchten(VG) model and their uncertainty. The parameters were estimated from the two field sites(Berambadi and Wailapally watershed in South India) and from laboratory evaporation experiment for the Wailapally site. It was found that the GLUE approach was able to provide good uncertainty bounds for the soil hydraulic parameters. The uncertainty in the estimates from the field experiment was found to be higher than from the laboratory evaporation experiment for both water retention and hydraulic conductivity curves. The saturated soil moisture(θs )and shape parameter (n) of VG model estimated from the laboratory evaporation and field experiment were found to be the same, and further more they showed a lower uncertainty from both the experiments. Moreover, the residual soil moisture (θr), inverse of capillary fringe thickness (α) and saturated hydraulic conductivity( KS) showed a relatively higher uncertainty. In the Berambadi watershed ,the inverse modeling was performed in three bare field plots, and it was found that field plots which had higher θs showed a relatively higher actual evapotranspiration (AET) and lower potential recharge. In Chapter 6, the retrieval of profile soil moisture up to 2m by assimilation of surface soil moisture was investigated by performing synthetic experiments on six soil types. The measured surface soil moisture over top 5cm depth was assimilated into the one dimensional soil moisture model to retrieve the profile soil moisture. Even though the assimilation of surface soil moisture helped in improving the profile soil moisture for the six soil types, the bias was observed. To reduce the bias, pseudo observations of profile soil moisture were generated and used in addition to the surface soil moisture in the assimilation altogether. These pseudo observations were generated using the linear relationship existing between the surface and profile soil moisture. A significant bias reduction was found to be feasible by using this method when pseudo observations beyond 75cm depth were used then there was no significant improvement. A coupled surface-ground water model was developed, which had 5 layers for the vadose zone and one layer for the ground water zone, in order to consider the major hydrological processes from ground surface to ground water table in a semi-arid watershed. The details of the coupled model were described in Chapter 7. The major aim of this model was to be able to use remotely sensed data of surface soil moisture and evapotranspiration to simulate recharge. The model was tested by applying in a lumped framework to the field data set in the Berambadi watershed for the year 2010 to 2011. The performance of the model was evaluated with the measured watershed average root zone soil moisture and ground water levels. The watershed average root zone soil moisture was obtained by averaging the field measurements from 20 plots and average ground water level was obtained by averaging the field measurement from 200 bore wells. In order to assimilate the AET into the coupled model, the daily AET at a spatial resolution of 1km was estimated from MODIS data. The AET was validated in one forested and four agricultural sites in the watershed. The validation was based on the comparison with AET simulated from water balance models. For agricultural plots the STICS (crop model) and for the forested site the COMFORT (hydrological) model were used. The AET from the MODIS showed a reasonably good match with both the forested and agricultural plots at the annual scale (for the crop model approximately 4-5 months). Model simulations were carried out with and without assimilating the remotely sensed data and the performance was evaluated. It was found that the assimilation helped in capturing the trends in deeper layer soil moisture and groundwater level. At the end, in Chapter 8 the major conclusions drawn from the various chapters are summarized.

This thesis examines the feasibility of assimilating space borne remotely-sensed microwave data into WATClass using the ensemble Kalman filter. WATClass is a meso-scale gridded hydrological model used to track water and energy budgets of watersheds by way of real-time remotely sensed data. By incorporating remotely-sensed soil moisture estimates into the model, the model’s soil moisture estimates can be improved, thus increasing the accuracy of the entire model. Due to the differences in scale between the remotely sensed data and WATClass, and the need of ground calibration for accurate soil moisture estimation from current satellite-borne active microwave remote sensing platforms, the spatial variability of soil moisture must be determined in order to characterise the dependency between the remotely-sensed estimates and the model data and subsequently to assimilate the remotely-sensed data into the model. Two sets of data – 1996-1997 Grand River watershed data and 2002-2003 Roseau River watershed data – are used to determine the spatial variability. The results of this spatial analysis however are found to contain too much error due to the small sample size. It is therefore recommended that a larger set of data with more samples both spatially and temporally be taken. The proposed algorithm is tested with simulated data in a simulation of WATClass. Using nominal values for the estimated errors and other model parameters, the assimilation of remotely sensed data is found to reduce the absolute RMS error in soil moisture from 0.095 to approximately 0.071. The sensitivities of the improvement in soil moisture estimates by using the proposed algorithm to several different parameters are examined.

The knowledge of soil moisture is of pronounced importance in various applications e.g. flood control, agricultural production and effective water resources management. These applications require the knowledge of spatial and temporal variation of the soil moisture in the watershed. There are three approaches of estimating/measuring soil moisture namely,(i) in-situ measurements,(ii) remote sensing, and(iii) hydrological modelling. The in situ techniques of measurement provide relatively accurate information at point scale but are not feasible to gather in large numbers relevant for a watershed. The soil moisture can be simulated by hydrological models at the desired spatial and temporal resolution, but these simulations would often be affected by the uncertainties in the model physics, parameters, forcing, initial and boundary conditions. The remote sensing provides an alternative to retrieve the soil moisture of the surface (top few centimeters ) layer, but even this data is limited by the spatial or temporal resolution, which is satellite dependant. Hydrological models could be improved by assimilating remotely sensed soil moisture, which requires a retrieval algorithm. In order to develop a retrieval algorithm the satellite data need to be calibrated/validated with the in-situ ground measurements. The retrieval of surface soil moisture from microwave remote sensing is sensitive to surface conditions, and hence requires calibration/validation specific to a site/region. The improvement in the hydrological variables/fluxes is sensitive to the framework adopted during the assimilation of remotely sensed data. The main focus of the study was to assess the retrieval algorithm for the surface soil moisture from both active (ENVISAT,RADARSAT-2)and passive(AMSR-E) microwave satellites in a semi-arid tropical watershed of South India. Further, the usefulness of these retrieved remotely sensed products for the estimation of recharge was investigated by developing a coupled hydrological model and an assimilation framework. A brief introduction was made in Chapter 1 on the importance of surface soil moisture and evapotranspiration in hydrology, and the feasible options available for the retrieval from microwave remote sensing. A detailed review of the literature is presented in Chapter 2 to establish the state-of-the-art on the following:(i) retrieval algorithms for the surface soil moisture from active and passive microwave remote sensing,(ii) estimation of actual evapotranspiration from optical remote sensing(MODIS),(iii) coupled surface-ground water hydrological models,(iv) estimation of soil hydraulic properties with their uncertainties, and(v) assimilation framework specific to hydrological modelling. To calibrate/validate the retrieval algorithms and to test the coupled model and the assimilation framework developed, field measurements were carried out in the BerambadI experimental watershed located in the Kabini river basin. The surface soil moisture in 50 field plots, profile soil moisture up to 1m depth in 20 field plots, and ground water level in 200 bore wells were measured. Twelve images of ENVISAT, seven teen images of RADARSAT-2, along with AMSR-E and MODIS data were used. These data pertained to different durations during the period 2008 to 2011,the details of which are given in Chapter 3. The approach for the retrieval of surface soil moisture and the associated uncertainty from active and passive microwave remote sensing is given in Chapter 4. Surface soil moisture was retrieved for six vegetation classes using the linear regression model and copulas. Three types of copulas(Clayton, Frank and Gumbel) were investigated. It was found that the ensemble mean simulated using the linear regression model and three copulas was nearly same. The copulas were found to be superior than the linear regression model when comparing the distributions of the mean of the generated ensemble. Among the copulas it was observed that the Clayton copula performed better in the lower and middle ranges of backscatter coefficient, while the Gumbel and Frank copulas were found to be superior in the upper ranges of backscatter coefficients. The range of RMSE was approximatively 4cm3cm−3 indicating that the retrieval from ENVISAT/RADARSAT-2 was good. ACDF based approach was proposed to retrieve the surface soil moisture map for the watershed with a spatial resolution of 100m x 100m ( i.e one hectare). The map of the uncertainty in the retrieved surface soil moisture was also prepared using the Clayton copula. The AMSR-E surface soil moisture product was calibrated for the watershed during the period 2008 to 2011, using the map generated from the ENVISAT/RADARSAT data. They Clayton copula was used to generate the ensemble of the corrected AMSR-E surface soil moisture. The standard deviation of the generated ensemble varied from 0.01 to 0.03cm3cm−3 ,hence the derived surface soil moisture product for Berambadi was found to be good. In the Chapter 5, a one dimensional soil moisture model was developed based on the numerical solution of the Richards’ equation using finite difference method and inverse modeling was carried out using the Generalized Likelihood Uncertainty Estimation(GLUE) approach for estimating the soil hydraulic parameters of the van Genuchten(VG) model and their uncertainty. The parameters were estimated from the two field sites(Berambadi and Wailapally watershed in South India) and from laboratory evaporation experiment for the Wailapally site. It was found that the GLUE approach was able to provide good uncertainty bounds for the soil hydraulic parameters. The uncertainty in the estimates from the field experiment was found to be higher than from the laboratory evaporation experiment for both water retention and hydraulic conductivity curves. The saturated soil moisture(θs )and shape parameter (n) of VG model estimated from the laboratory evaporation and field experiment were found to be the same, and further more they showed a lower uncertainty from both the experiments. Moreover, the residual soil moisture (θr), inverse of capillary fringe thickness (α) and saturated hydraulic conductivity( KS) showed a relatively higher uncertainty. In the Berambadi watershed ,the inverse modeling was performed in three bare field plots, and it was found that field plots which had higher θs showed a relatively higher actual evapotranspiration (AET) and lower potential recharge. In Chapter 6, the retrieval of profile soil moisture up to 2m by assimilation of surface soil moisture was investigated by performing synthetic experiments on six soil types. The measured surface soil moisture over top 5cm depth was assimilated into the one dimensional soil moisture model to retrieve the profile soil moisture. Even though the assimilation of surface soil moisture helped in improving the profile soil moisture for the six soil types, the bias was observed. To reduce the bias, pseudo observations of profile soil moisture were generated and used in addition to the surface soil moisture in the assimilation altogether. These pseudo observations were generated using the linear relationship existing between the surface and profile soil moisture. A significant bias reduction was found to be feasible by using this method when pseudo observations beyond 75cm depth were used then there was no significant improvement. A coupled surface-ground water model was developed, which had 5 layers for the vadose zone and one layer for the ground water zone, in order to consider the major hydrological processes from ground surface to ground water table in a semi-arid watershed. The details of the coupled model were described in Chapter 7. The major aim of this model was to be able to use remotely sensed data of surface soil moisture and evapotranspiration to simulate recharge. The model was tested by applying in a lumped framework to the field data set in the Berambadi watershed for the year 2010 to 2011. The performance of the model was evaluated with the measured watershed average root zone soil moisture and ground water levels. The watershed average root zone soil moisture was obtained by averaging the field measurements from 20 plots and average ground water level was obtained by averaging the field measurement from 200 bore wells. In order to assimilate the AET into the coupled model, the daily AET at a spatial resolution of 1km was estimated from MODIS data. The AET was validated in one forested and four agricultural sites in the watershed. The validation was based on the comparison with AET simulated from water balance models. For agricultural plots the STICS (crop model) and for the forested site the COMFORT (hydrological) model were used. The AET from the MODIS showed a reasonably good match with both the forested and agricultural plots at the annual scale (for the crop model approximately 4-5 months). Model simulations were carried out with and without assimilating the remotely sensed data and the performance was evaluated. It was found that the assimilation helped in capturing the trends in deeper layer soil moisture and groundwater level. At the end, in Chapter 8 the major conclusions drawn from the various chapters are summarized.

Soil moisture influences the water and energy cycles of terrestrial environments, and thus plays an important climatic role. However, the behavior of soil moisture at large scales, including its impact on atmospheric processes such as precipitation, is not well characterized. Satellite remote sensing allows for indirect observation of large-scale soil moisture, but validation of these data is complicated by the difference in scales between remote sensing footprints and direct ground-based measurements. To address this problem, a method, based on information theory (specifically, mutual information), was developed to determine the useful information content of satellite soil moisture records using precipitation observations. This method was applied to three soil moisture datasets derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements over the contiguous U.S., allowing for spatial identification of the algorithm with the least inferred error. Ancillary measures of biomass and topography revealed a strong dependence between algorithm performance and confounding surface properties. Next, statistical causal identification methods (i.e. Granger causality) were used to examine the link between AMSR-E soil moisture and the occurrence of next day precipitation, accounting for long term variability and autocorrelation in precipitation. The probability of precipitation occurrence was modeled using a probit regression framework, and soil moisture was added to the model in order to test for statistical significance and sign. A contrasting pattern of positive feedback in the western U.S. and negative feedback in the east was found, implying a possible amplification of drought and flood conditions in the west and damping in the east. Finally, observations and simulations were used to demonstrate the pitfalls of determining causality between soil moisture and precipitation. It is shown that ignoring long term variability and precipitation autocorrelation can result in artificial positive correlation between soil moisture and precipitation, unless explicitly accounted for in the analysis. In total, this dissertation evaluates large-scale soil moisture measurements, outlines important factors that can cloud the determination of land surface-atmosphere hydrologic feedback, and examines the causal linkage between soil moisture and precipitation at large scales.

碩士
國立聯合大學
環境與安全衛生工程學系碩士班
107
Microwave heating can offer a faster processing rate than conventional heating techniques. In this study, microwave is applied in remediation of soil and groundwater contamination. The quartz sand is used to simulate the sandy soil. Under different soil water content, the phenomenon of microwave energy transmission and the effect of contamination removal are discussed. This study is discussed in two parts：The first part discusses the transmission of microwaves in quartz sand with soil water content of 0 % to 24 % . The results show that at all kinds of soil water content, the temperature is proportional to the power of microwave and launch time, and the higher the temperature increases as the intensity of fire power increases. In the soil water content of 4 %, the maximum increase in sand temperature and the highest water temperature at a distance of 15 cm , microwave transmission distance is the furthest , and energy is transmitted and used more effectively. In addition, when the soil water contains manganese ion concentration of 0.5 to 10 mg/L, the temperature change trend is similar to that of quartz sand without addition of manganese ions. The second part discusses the effectiveness of microwave treatment of Toluene. The Toluene removal rate resulted in a higher water-containing soil than dry sand. The maximum Toluene removal rate is 70.96 %.

碩士
國立中央大學
土木工程研究所
100
Soil moisture is an important factor for the exchange of water between the land surface and plant transpiration. It has tremendous effects on agriculture, the environment and climate. It is hard to evaluate long term land surface dryness by field investigation or ground survey. Using remote sensing technology can get soil moisture information extensively. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provide global soil moisture product, the spatial resolution is 25km. The spatial resolution is not good enough to satisfy the demand for agricultural planning or drought monitoring. In the literary, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite image to observe land surface water content is feasible. A land surface drought index called Normalized Multi-Band Drought Index (NMDI) based on two short wave infrared (SWIR) channel in MODIS as the soil moisture sensitive band, is used for estimating land surface soil moisture, and the spatial resolution is up to 1km. The main objective of this study is to estimate soil moisture conditions of the Central American region using MODIS and AMSR-E data in 2010 and 2011 dry season.