Academic literature on the topic 'Microwave Soil Moisture Retrieval Algorithm'
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Journal articles on the topic "Microwave Soil Moisture Retrieval Algorithm"
1
Karthikeyan, Lanka, Ming Pan, Dasika Nagesh Kumar, and Eric F. Wood. "Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm." Sensors 20, no. 4 (February 24, 2020): 1225. http://dx.doi.org/10.3390/s20041225.
Full textAbstract:
Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.
2
Burke, E. J., W. J. Shuttleworth, and A. N. French. "Using vegetation indices for soil-moisture retrievals from passive microwave radiometry." Hydrology and Earth System Sciences 5, no. 4 (December 31, 2001): 671–78. http://dx.doi.org/10.5194/hess-5-671-2001.
Full textAbstract:
Abstract. Surface soil moisture and the nature of the overlying vegetation both influence microwave emission from land surfaces significantly. One widely discussed but underused method for allowing for the effect of vegetation on soil-moisture retrievals from microwave observations is to use remotely sensed vegetation indices. This paper explores the potential for using the Normalised Difference Vegetation Index (NDVI) in soil-moisture retrievals from L-band (1.4 GHz) aircraft data gathered during the Southern Great Plains '97 (SGP97) experiment. A simplified version of MICRO-SWEAT, a soil vegetation atmosphere transfer (SVAT) scheme coupled with a microwave emission model, was used as the retrieval algorithm. Estimates of the optical depth of the vegetation, the parameter that describes the effect of the vegetation on microwave emission, were obtained by calibrating this retrieval algorithm against measurements of soil moisture at 15 field sites. A significant relationship was found between the optical depth so obtained and the observed NDVI at these sites, although this relationship changed with the resolution of the microwave brightness temperature observations used. Soil-moisture estimates made with the retrieval algorithm using the empirical relationship between optical depth and NDVI applied at two additional sites not used in the calibration show good agreement with field measurements. Keywords: NDVI, soil moisture, passive microwave, SGP97
3
Tong, Cheng, Hongquan Wang, Ramata Magagi, Kalifa Goïta, Luyao Zhu, Mengying Yang, and Jinsong Deng. "Soil Moisture Retrievals by Combining Passive Microwave and Optical Data." Remote Sensing 12, no. 19 (September 28, 2020): 3173. http://dx.doi.org/10.3390/rs12193173.
Full textAbstract:
This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.
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Moradizadeh, Mina, Prashant K. Srivastava, and George P. Petropoulos. "Synergistic Evaluation of Passive Microwave and Optical/IR Data for Modelling Vegetation Transmissivity towards Improved Soil Moisture Retrieval." Sensors 22, no. 4 (February 10, 2022): 1354. http://dx.doi.org/10.3390/s22041354.
Full textAbstract:
Vegetation cover and soil surface roughness are vital parameters in the soil moisture retrieval algorithms. Due to the high sensitivity of passive microwave and optical observations to Vegetation Water Content (VWC), this study assesses the integration of these two types of data to approximate the effect of vegetation on passive microwave Brightness Temperature (BT) to obtain the vegetation transmissivity parameter. For this purpose, a newly introduced index named Passive microwave and Optical Vegetation Index (POVI) was developed to improve the representativeness of VWC and converted into vegetation transmissivity through linear and nonlinear modelling approaches. The modified vegetation transmissivity is then applied in the Simultaneous Land Parameters Retrieval Model (SLPRM), which is an error minimization method for better retrieval of BT. Afterwards, the Volumetric Soil Moisture (VSM), Land Surface Temperature (LST) as well as canopy temperature (TC) were retrieved through this method in a central region of Iran (300 × 130 km2) from November 2015 to August 2016. The algorithm validation returned promising results, with a 20% improvement in soil moisture retrieval.
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Lee, K., Eleanor J. Burke, W. Shuttleworth, and R. Harlow. "Influence of vegetation on SMOS mission retrievals." Hydrology and Earth System Sciences 6, no. 2 (April 30, 2002): 153–66. http://dx.doi.org/10.5194/hess-6-153-2002.
Full textAbstract:
Abstract. Using the proposed Soil Moisture and Ocean Salinity (SMOS) mission as a case study, this paper investigates how the presence and nature of vegetation influence the values of geophysical variables retrieved from multi-angle microwave radiometer observations. Synthetic microwave brightness temperatures were generated using a model for the coherent propagation of electromagnetic radiation through a stratified medium applied to account simultaneously for the emission from both the soil and any vegetation canopy present. The synthetic data were calculated at the look-angles proposed for the SMOS mission for three different soil-moisture states (wet, medium wet and dry) and four different vegetation covers (nominally grass, crop, shrub and forest). A retrieval mimicking that proposed for SMOS was then used to retrieve soil moisture, vegetation water content and effective temperature for each set of synthetic observations. For the case of a bare soil with a uniform profile, the simpler Fresnel model proposed for use with SMOS gave identical estimates of brightness temperatures to the coherent model. However, to retrieve accurate geophysical parameters in the presence of vegetation, the opacity coefficient (one of two parameters used to describe the effect of vegetation on emission from the soil surface) used within the SMOS retrieval algorithm needed to be a function of look-angle, soil-moisture status, and vegetation cover. The effect of errors in the initial specification of the vegetation parameters within the coherent model was explored by imposing random errors in the values of these parameters before generating synthetic data and evaluating the errors in the geophysical parameters retrieved. Random errors of 10% result in systematic errors (up to 0.5°K, 3%, and ~0.2 kg m-2 for temperature, soil moisture, and vegetation content, respectively) and random errors (up to ~2°K, ~8%, and ~2 kg m-2 for temperature, soil moisture and vegetation content, respectively) that depend on vegetation cover and soil-moisture status. Keywords: passive microwave, soil moisture, vegetation, SMOS, retrieval
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Lindau, Ralf, and Clemens Simmer. "Derivation of a root zone soil moisture algorithm and its application to validate model data." Hydrology Research 36, no. 4-5 (August 1, 2005): 335–48. http://dx.doi.org/10.2166/nh.2005.0026.
Full textAbstract:
A retrieval algorithm for soil moisture within the uppermost metre of soil is presented. As calibration data, longtime soil moisture measurements from the former Soviet Union are used. The retrieval works in two steps. First, the distribution of longtime mean soil moisture is derived by using precipitation, soil texture, vegetation density and terrain slope. In a second step, the temporal variability at each location is deduced by using microwave radiation measurements available from satellite together with precipitation and air temperature data. This soil moisture algorithm is applied in Northern and Central Europe to validate a climate simulation from the regional model REMO.
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Gao, H., E. F. Wood, T. J. Jackson, M. Drusch, and R. Bindlish. "Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 23–38. http://dx.doi.org/10.1175/jhm473.1.
Full textAbstract:
Abstract Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.
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Ghilain, Nicolas, Alirio Arboleda, Okke Batelaan, Jonas Ardö, Isabel Trigo, Jose-Miguel Barrios, and Francoise Gellens-Meulenberghs. "A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation." Remote Sensing 11, no. 17 (August 21, 2019): 1968. http://dx.doi.org/10.3390/rs11171968.
Full textAbstract:
Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).
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Anh, Vo Thi Lan, Doan Minh Chung, Ngo Tuan Ngoc, and K. G. Kostov. "Research of Soil Moisture Retrieval Algorithms for Processing Radiometry Data." Communications in Physics 25, no. 3 (March 3, 2016): 283. http://dx.doi.org/10.15625/0868-3166/25/3/5561.
Full textAbstract:
Since 2012, the experts of Space Technology Institute have carried out the field experiments to obtain a high-resolution dataset of microwave radiometers for land surface parameters (soil moisture, soil temperature, vegetation water content), in order to improve the soil moisture retrieval methodology. L-band radiometers were used for measuring the brightness temperature of the bare soil. Field experiments for passive microwave remote sensing of soil moisture were carried out in Hoai Duc District in 2012. L-band microwave radiometers were used for measuring the microwave emission of bare agricultural fields. The radiometers, which are used for soil moisture measurement, worked well during the experimental campaign and produced volumetric soil moisture estimates that compared well with the ground-truth measurements. Explanations for the observed discrepancies are presented. The experimental results showed that the model of Choudhury et al. for surface roughness correction provides a better fit to radiometric data over the angular range between 20° and 50° for \(n = 0\) (i.e., the \(\cos ^{2}\theta\) factor in the exponential in (15) is suppressed). Based on the results of the experiments conducted over two experimental sites with different soils, namely sandy loam at Hoai Duc Agrometeorologyl Center, it may be concluded that the testing of both the radiometric equipment and the method for soil moisture retrieval was very successful, and the main goal of the experiments was fulfilled.
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Gao, Huilin, Eric F. Wood, Matthias Drusch, and Matthew F. McCabe. "Copula-Derived Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil Moisture into Land Surface Models." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 413–29. http://dx.doi.org/10.1175/jhm570.1.
Full textAbstract:
Abstract Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.
Dissertations / Theses on the topic "Microwave Soil Moisture Retrieval Algorithm"
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Talone, Marco. "Contributrion to the improvement of the soil moisture and ocean salinity (SMOS) sea surface salinity retrieval algorithm." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/48633.
Full textAbstract:
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.
2
Chai, Soo See. "An artificial neural network approach for soil moisture retrieval using passive microwave data." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/1416.
Full textAbstract:
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.
3
Lee, Khil-Ha. "Effect of vegetation characteristics on near soil moisture retrieval using microwave remote sensing technique." Diss., The University of Arizona, 2002. http://hdl.handle.net/10150/280028.
Full textAbstract:
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.)
4
Sabia, Roberto. "Sea surface salinity retrieval error budget within the esa soil moisture and ocean salinity mission." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/30542.
Full textAbstract:
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.
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Rötzer, Kathrina [Verfasser]. "Statistical analysis and combination of active and passive microwave remote sensing methods for soil moisture retrieval / Kathrina Rötzer." Bonn : Universitäts- und Landesbibliothek Bonn, 2016. http://d-nb.info/1113688300/34.
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Sat, Kumar *. "Soil Moisture Modelling, Retrieval From Microwave Remote Sensing And Assimilation In A Tropical Watershed." Thesis, 2012. http://etd.iisc.ernet.in/handle/2005/2508.
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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.
Book chapters on the topic "Microwave Soil Moisture Retrieval Algorithm"
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Ciabatta, Luca, Stefania Camici, Christian Massari, Paolo Filippucci, Sebastian Hahn, Wolfgang Wagner, and Luca Brocca. "Soil Moisture and Precipitation: The SM2RAIN Algorithm for Rainfall Retrieval from Satellite Soil Moisture." In Advances in Global Change Research, 1013–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35798-6_27.
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Laachrate, Hibatoullah, Abdelhamid Fadil, and Abdessamad Ghafiri. "Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications." In Geospatial Technology, 31–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24974-8_3.
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Jahan, Nasreen, and Thian Yew Gan. "Soil Moisture Retrieval from Microwave (RADARSAT-2) and Optical Remote Sensing (MODIS) Data Using Artificial Intelligence Techniques." In Remote Sensing of the Terrestrial Water Cycle, 255–75. Hoboken, NJ: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118872086.ch16.
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Wang, Hongquan. "Soil Moisture Retrieval from Microwave Remote Sensing Observations." In Soil Moisture. IntechOpen, 2019. http://dx.doi.org/10.5772/intechopen.81476.
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Muñoz-Sabater, J., A. Al Bitar, and L. Brocca. "Soil Moisture Retrievals Based on Active and Passive Microwave Data." In Satellite Soil Moisture Retrieval, 351–78. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-803388-3.00018-8.
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Mattar, C., A. Santamaría-Artigas, J. A. Sobrino, and J. C. Jiménez-Muñoz. "Soil Moisture Retrieved From a Combined Optical and Passive Microwave Approach." In Satellite Soil Moisture Retrieval, 135–58. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-803388-3.00007-3.
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Akbar, R., N. Das, D. Entekhabi, and M. Moghaddam. "Active and Passive Microwave Remote Sensing Synergy for Soil Moisture Estimation." In Satellite Soil Moisture Retrieval, 187–207. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-803388-3.00010-3.
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Gupta, D. K., R. Prasad, P. K. Srivastava, and T. Islam. "Nonparametric Model for the Retrieval of Soil Moisture by Microwave Remote Sensing." In Satellite Soil Moisture Retrieval, 159–68. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-803388-3.00008-5.
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Piles, M., and N. Sánchez. "Spatial Downscaling of Passive Microwave Data With Visible-to-Infrared Information for High-Resolution Soil Moisture Mapping." In Satellite Soil Moisture Retrieval, 109–32. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-803388-3.00006-1.
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Smith, Anne M. "Active Microwave Systems for Monitoring Drought Stress." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0015.
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Remote sensing can provide timely and economical monitoring of large areas. It provides the ability to generate information on a variety of spatial and temporal scales. Generally, remote sensing is divided into passive and active depending on the sensor system. The majority of remote-sensing studies concerned with drought monitoring have involved visible–infrared sensor systems, which are passive and depend on the sun’s illumination. Radar (radio detection and ranging) is an active sensor system that transmits energy in the microwave region of the electromagnetic spectrum and measures the energy reflected back from the landscape target. The energy reflected back is called backscatter. The attraction of radar over visible– infrared remote sensing (chapters 5 and 6) is its independence from the sun, enabling day/night operations, as well as its ability to penetrate cloud and obtain data under most weather conditions. Thus, unlike visible–infrared sensors, radar offers the opportunity to acquire uninterrupted information relevant to drought such as soil moisture and vegetation stress. Drought conditions manifest in multiple and complex ways. Accordingly, a large number of drought indices have been defined to signal abnormally dry conditions and their effects on crop growth, river flow, groundwater, and so on (Tate and Gustard, 2000). In the field of radar remote sensing, much work has been devoted to developing algorithms to retrieve geophysical parameters such as soil moisture, crop biomass, and vegetation water content. In principle, these parameters would be highly relevant for monitoring agricultural drought. However, despite the existence of a number of radar satellite systems, progress in the use of radar in environmental monitoring, particularly in respect to agriculture, has been slower than anticipated. This may be attributed to the complex nature of radar interactions with agricultural targets and the suboptimal configuration of the satellite sensors available in the 1990s (Ulaby, 1998; Bouman et al., 1999). Because most attention is still devoted to the problem of deriving high-quality soil moisture and vegetation products, there have been few investigations on how to combine such radar products with other data and models to obtain value-added agricultural drought products.
Conference papers on the topic "Microwave Soil Moisture Retrieval Algorithm"
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Mao, K., Z. Qin, M. Li, L. Zhang, B. Xu, and L. Jiang. "An Algorithm for Surface Soil Moisture Retrieval Using the Microwave Polarization Difference Index." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.777.
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Liu, Jicheng, Xiwu Zhan, and Thomas J. Jackson. "Soil moisture retrieval from WindSat using the single channel algorithm toward a blended global soil moisture product from multiple microwave sensors." In Optical Engineering + Applications, edited by Mitchell D. Goldberg, Hal J. Bloom, Philip E. Ardanuy, and Allen H. Huang. SPIE, 2008. http://dx.doi.org/10.1117/12.795065.
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Kurum, Mehmet, Roger H. Lang, Peggy E. ONeill, Alicia Joseph, Tom Jackson, and Mike Cosh. "Estimation of canopy attenuation for active/passive microwave soil moisture retrieval algorithms." In 2008 Microwave Radiometry and Remote Sensing of the Environment (MICRORAD 2008). IEEE, 2008. http://dx.doi.org/10.1109/micrad.2008.4579490.
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Gao, Ying, Andreas Colliander, Mariko S. Burgin, Jeffrey P. Walker, Chunsik Chae, Emmanuel Dinnat, Michael H. Cosh, Todd Caldwell, Aaron Berg, and Jose Martinez-Fernandez. "L-, C- and X-Band Passive Microwave Soil Moisture Retrieval Algorithm Parameterization Using in Situ Validation Sites." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519001.
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Zeng, Jiangyuan, Zhen Li, Quan Chen, Haiyun Bi, and Ping Zhang. "A physically-based algorithm for surface soil moisture retrieval in the Tibet Plateau using passive microwave remote sensing." In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723382.
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Santi, E., S. Paloscia, S. Pettinato, and G. Fontanelli. "A prototype ann based algorithm for the soil moisture retrieval from l- band in view of the incoming SMAP mission." In 2014 Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad). IEEE, 2014. http://dx.doi.org/10.1109/microrad.2014.6878897.
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Brogioni, M., G. Macelloni, S. Paloscia, P. Pampaloni, S. Pettinato, and E. Santi. "Two operational algorithms for the retrieval of snow depth and soil moisture content from AMSR-E data." In 2010 11th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment (MicroRad 2010). IEEE, 2010. http://dx.doi.org/10.1109/microrad.2010.5559586.
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Fu, Haoyang, Lingjia Gu, and Ruizhi Ren. "Salinity and soil moisture retrieval algorithms in western Jilin Province of China using passive microwave remote sensing data." In SPIE Optical Engineering + Applications, edited by Wei Gao, Ni-Bin Chang, and Jinnian Wang. SPIE, 2015. http://dx.doi.org/10.1117/12.2186282.
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Lu, Hui, Toshio Koike, Tetsu Ohta, David Ndegwa Kuria, Hiroyuki Tsutsui, Tobias Graf, Hideyuki Fujii, and Katsunori Tamagawa. "Development of a soil moisture retrieval algorithm for spaceborne passive microwave radiometers and its application to AMSR-E and SSM/I." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423014.
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O'Neill, Peggy, Roger Lang, Mehmet Kurum, Alicia Joseph, Michael Cosh, and Thomas Jackson. "Microwave Soil Moisture Retrieval Under Trees." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4778786.
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