Relevant bibliographies by topics / Microwave Satellite Soil Moisture

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Academic literature on the topic 'Microwave Satellite Soil Moisture'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Microwave Satellite Soil Moisture"

1

Lei, X., Y. Wang, and T. Guo. "DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 355–60. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-355-2021.

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Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.
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Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. de Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 2, 2010): 6699–724. http://dx.doi.org/10.5194/hessd-7-6699-2010.

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Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.
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Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. De Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals." Hydrology and Earth System Sciences 15, no. 2 (February 1, 2011): 425–36. http://dx.doi.org/10.5194/hess-15-425-2011.

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Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.
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Rabin, Robert M., and Timothy J. Schmit. "Estimating Soil Wetness from the GOES Sounder." Journal of Atmospheric and Oceanic Technology 23, no. 7 (July 1, 2006): 991–1003. http://dx.doi.org/10.1175/jtech1895.1.

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Abstract In this note, the relationship between the observed daytime rise in surface radiative temperature, derived from the Geostationary Operational Environmental Satellites (GOES) sounder clear-sky data, and modeled soil moisture is explored over the continental United States. The motivation is to provide an infrared (IR) satellite–based index for soil moisture, which has a higher resolution than possible with the microwave satellite data. The daytime temperature rise is negatively correlated with soil moisture in most areas. Anomalies in soil moisture and daytime temperature rise are also negatively correlated on monthly time scales. However, a number of exceptions to this correlation exist, particularly in the western states. In addition to soil moisture, the capacity of vegetation to generate evapotranspiration influences the amount of daytime temperature rise as sensed by the satellite. In general, regions of fair to poor vegetation health correspond to the relatively high temperature rise from the satellite. Regions of favorable vegetation match locations of lower-than-average temperature rise.
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Zhu, Hongchun, Zhilin Zhang, and Aifeng Lv. "Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation." Water 12, no. 5 (May 2, 2020): 1292. http://dx.doi.org/10.3390/w12051292.

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Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).
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Barrett, Damian J., and Luigi J. Renzullo. "On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation." Journal of Hydrometeorology 10, no. 5 (October 1, 2009): 1109–27. http://dx.doi.org/10.1175/2009jhm1043.1.

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Abstract Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m3 m−3. As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.
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Yang, Kun, Toshio Koike, Ichirow Kaihotsu, and Jun Qin. "Validation of a Dual-Pass Microwave Land Data Assimilation System for Estimating Surface Soil Moisture in Semiarid Regions." Journal of Hydrometeorology 10, no. 3 (June 1, 2009): 780–93. http://dx.doi.org/10.1175/2008jhm1065.1.

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Abstract This study examines the capability of a new microwave land data assimilation system (LDAS) for estimating soil moisture in semiarid regions, where soil moisture is very heterogeneous. This system assimilates the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) 6.9- and 18.7-GHz brightness temperatures into a land surface model (LSM), with a radiative transfer model as an observation operator. To reduce errors caused by uncertainties of system parameters, the LDAS uses a dual-pass assimilation algorithm, with a calibration pass to estimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soil moisture. Validation data of soil moisture were collected in a Mongolian semiarid region. Results show that (i) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements, though the measured soil moistures were highly variable from site to site; (ii) the LSM-simulated soil moistures show less biases when the LSM uses LDAS-calibrated parameter values instead of default parameter values, indicating that the satellite-based calibration does contribute to soil moisture estimations; and (iii) compared to the LSM, the LDAS produces more robust and reliable soil moisture when forcing data become worse. The lower sensitivity of the LDAS output to precipitation is particularly encouraging for applying this system to regions where precipitation data are prone to errors.
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Wang, Guojie, Xiaowen Ma, Daniel Fiifi Tawia Hagan, Robin van der Schalie, Giri Kattel, Waheed Ullah, Liangliang Tao, Lijuan Miao, and Yi Liu. "Towards Consistent Soil Moisture Records from China’s FengYun-3 Microwave Observations." Remote Sensing 14, no. 5 (March 2, 2022): 1225. http://dx.doi.org/10.3390/rs14051225.

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Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.
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Mavrovic, Alex, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, and Alexandre Roy. "Soil dielectric characterization during freeze–thaw transitions using L-band coaxial and soil moisture probes." Hydrology and Earth System Sciences 25, no. 3 (March 4, 2021): 1117–31. http://dx.doi.org/10.5194/hess-25-1117-2021.

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Abstract. Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (εr=ε/ε0=ε′-jε′′; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (εfrozen′=[3.5; 6.0], εfrozen′′=[0.46; 1.2]) and thawed (εthawed′=[6.5; 22.8], εthawed′′=[1.43; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.
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Zhu, Liming, Huifeng Wu, Min Li, Chaoyin Dou, and A.-Xing Zhu. "Estimation of Irrigation Water Use by Using Irrigation Signals from SMAP Soil Moisture Data." Agriculture 13, no. 9 (August 29, 2023): 1709. http://dx.doi.org/10.3390/agriculture13091709.

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Accurate irrigation water-use data are essential to agricultural water resources management and optimal allocation. The obscuration presented by ground cover in farmland and the subjectivity of irrigation-related decision-making processes mean that effectively identifying regional irrigation water use remains a critical problem to be solved. In view of the advantages of satellite microwave remote sensing in monitoring soil moisture, previous studies have proposed a method for estimating irrigation water use using the satellite microwave remote sensing of soil moisture. However, the method is affected by false irrigation signals from soil moisture increases caused by non-irrigation factors, causing irrigation water use to be overestimated. Therefore, the purpose of this study is to improve the estimation of irrigation water use in drylands by using irrigation signals from SMAP soil moisture data. In this paper, the irrigation water use in Henan Province is estimated by using the irrigation signals from SMAP (soil moisture active and passive) soil moisture data. Firstly, a method for recognizing irrigation signals in soil moisture data obtained by microwave satellite remote sensing was used. Then, an estimation model of the amount of irrigation water (SM2Rainfall model) was built on each data pixel of the satellite microwave remote sensing of soil moisture. Finally, the amount of irrigation water utilized in Henan Province was estimated by combining the irrigation signals and irrigation water-use estimation model, and the results were evaluated. According to the findings, this study improved the estimation accuracy of irrigation water use by using the irrigation signals in Henan Province. The result of this study is of great importance to accurately obtain irrigation water use in the region.
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Dissertations / Theses on the topic "Microwave Satellite Soil Moisture"

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Piles, Guillem Maria. "Multiscale soil moisture retrievals from microwave remote sensing observations." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/77910.

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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.
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Dall'Amico, Johanna Therese. "Multiscale analysis of soil moisture using satellite and aircraft microwave remote sensing, in situ measurements and numerical modelling." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-146263.

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Dall'Amico, Johanna Therese [Verfasser], and Wolfram [Akademischer Betreuer] Mauser. "Multiscale analysis of soil moisture using satellite and aircraft microwave remote sensing, in situ measurements and numerical modelling / Johanna Therese dall'Amico. Betreuer: Wolfram Mauser." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2012. http://d-nb.info/1025047079/34.

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Walden, Aleksi. "SMOS satellite hardware anomaly prediction methods based on Earth radiation environment data sets." Thesis, Luleå tekniska universitet, Rymdteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-59789.

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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.
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Kolassa, Jana. "Soil moisture retrieval from multi-instrument satellite observations." Paris 6, 2013. http://www.theses.fr/2013PA066392.

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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
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Soriano, Melissa. "Estimation of soil moisture in the southern united states in 2003 using multi-satellite remote sensing measurements." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3361.

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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.
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Manchikanti, Ujwala. "Evaluation of microwave sensor for soil moisture content determination." [Ames, Iowa : Iowa State University], 2007.

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Ramnath, Vinod. "Estimation of soil moisture using active microwave remote sensing." Master's thesis, Mississippi State : Mississippi State University, 2003.

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Das, Narendra N. "Soil moisture modeling and scaling using passive microwave remote sensing." Texas A&M University, 2005. http://hdl.handle.net/1969.1/4881.

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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.
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Baban, Serwan M. J. "The derivations of hydrological variables (including soil moisture) from satellite imagery." Thesis, University of East Anglia, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292298.

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Books on the topic "Microwave Satellite Soil Moisture"

1

Center, Langley Research, ed. A conceptual thermal design study of an electronically scanned thinned array radiometer. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1995.

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Behari, Jitendra. Microwave dielectric behavior of wet soils. New York: Springer, 2005.

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Microwave radiometry of vegetation canopies. Dordrecht: Springer, 2006.

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Dąbrowska-Zielińska, Katarzyna. Szacowanie ewapotranspiracji wilgotności gleb i masy zielonej łąk na podstawie zdjęć satelitarnych NOAA =: Assessment of evapotranspiration, soil moisture and green biomass of grassland using NOAA satellite images. Wrocław: Wydawn. Continuo, 1995.

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H, Staelin David, and United States. National Aeronautics and Space Administration., eds. Comparative analysis of alternate MHS configurations. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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H, Staelin David, and United States. National Aeronautics and Space Administration., eds. Comparative analysis of alternate MHS configurations. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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H, Staelin David, and United States. National Aeronautics and Space Administration., eds. Comparative analysis of alternate MHS configurations. [Washington, D.C: National Aeronautics and Space Administration, 1995.

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United States. National Aeronautics and Space Administration., ed. An investigation of satellite sounding products for the remote sensing of the surface energy balance and soil moisture. Madison, WI: Cooperative Institute for Meteorological Satellite Studies, Space Science and Engineering Center, University of Wisconsin-Madison, 1990.

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Földes, Péter. A design study for the use of a multiple aperture deployable antenna for soil moisture remote senisng satellite applications. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1986.

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Center, Langley Research, ed. A design study for the use of a multiple aperture deployable antenna for soil moisture remote sensing satellite applications. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1986.

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Book chapters on the topic "Microwave Satellite Soil Moisture"

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Kuenzer, Claudia, Ursula Gessner, and Wolfgang Wagner. "Soil Moisture from Thermal Infrared Satellite Data: Synergies with Microwave Data." In Thermal Infrared Remote Sensing, 315–30. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6639-6_16.

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Burke, Eleanor J., R. Chawn Harlow, and W. James Shuttleworth. "Using Coupled Land Surface and Microwave Emission Models to Address Issues in Satellite-Based Estimates of Soil Moisture." In Spatial Modelling of the Terrestrial Environment, 59–77. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470094001.ch4.

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Fung, Adrian K. "Microwave Remote Sensing of Soil Moisture." In From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems, 21–59. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-1620-8_2.

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Varotsos, Costas A., and Vladimir F. Krapivin. "Microwave Remote Sensing of Soil Moisture." In Microwave Remote Sensing Tools in Environmental Science, 121–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45767-9_4.

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Schmugge, Thomas. "Microwave Remote Sensing of Soil Moisture." In Applications of Remote Sensing to Agrometeorology, 257–84. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2235-8_11.

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Schmugge, Thomas, and Thomas Jackson. "Passive Microwave Remote Sensing of Soil Moisture." In Land Surface Processes in Hydrology, 239–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60567-3_14.

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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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K., Anush Kumar, Raj Setia, Dharmendra Kumar Pandey, Deepak Putrevu, Arundhati Misra, and Brijendra Pateriya. "Soil Moisture Retrieval Techniques Using Satellite Remote Sensing." In Geospatial Technologies for Crops and Soils, 357–85. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6864-0_10.

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Shi, Jiancheng, Tianjie Zhao, Qian Cui, and Panpan Yao. "Airborne and Spaceborne Passive Microwave Measurements of Soil Moisture." In Observation and Measurement of Ecohydrological Processes, 71–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-48297-1_3.

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Arya, K. V., and Suggula Jagadeesh. "Time Series Forecasting of Soil Moisture Using Satellite Images." In Communications in Computer and Information Science, 385–97. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07005-1_33.

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Conference papers on the topic "Microwave Satellite Soil Moisture"

1

Mattikalli, Nandish M., Edwin T. Engman, Laj Ahuja, and Thomas J. Jackson. "Estimating soil properties from microwave measurements of soil moisture." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227172.

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Troch, Peter A., Zhongbo Su, P. Colombo, and Federico De Masi. "Active microwave soil moisture sensing under vegetation cover." In Satellite Remote Sensing III, edited by Giovanna Cecchi, Guido D'Urso, Edwin T. Engman, and Preben Gudmandsen. SPIE, 1997. http://dx.doi.org/10.1117/12.264257.

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Schmugge, Thomas J., Andre Chanzy, Yann H. Kerr, and Peter van Oevelen. "Microwave radiometer observations of soil moisture in HAPEX-SAHEL." In Satellite Remote Sensing, edited by Eric Mougin, K. Jon Ranson, and James A. Smith. SPIE, 1995. http://dx.doi.org/10.1117/12.200781.

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Kulemin, Gennady P., Andrei A. Kurekin, Vladimir V. Lukin, and Alexander A. Zelensky. "Soil moisture and erosion degree estimation from multichannel microwave remote sensing data." In Satellite Remote Sensing II, edited by Edwin T. Engman, Gerard Guyot, and Carlo M. Marino. SPIE, 1995. http://dx.doi.org/10.1117/12.227178.

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Hnatushenko, Volodymyr, Igor Garkusha, and Volodymyr Vasyliev. "Creating soil moisture maps based on radar satellite imagery." In Active and Passive Microwave Remote Sensing for Environmental Monitoring, edited by Claudia Notarnicola, Nazzareno Pierdicca, and Emanuele Santi. SPIE, 2017. http://dx.doi.org/10.1117/12.2278040.

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Santi, E., S. Paloscia, P. Pampaloni, S. Pettinato, and M. Brogioni. "Retrieval of soil moisture with airborne and satellite microwave sensors." In 2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418252.

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Owe, Manfred, Thomas R. H. Holmes, and Richard A. M. De Jeu. "Spatial distributions of global soil moisture retrievals from satellite microwave observations." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Ben T. Gouweleeuw, and Anne M. Jochum. SPIE, 2004. http://dx.doi.org/10.1117/12.565257.

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Owe, Manfred, Adriaan A. Van de Griend, Richard A. de Jeu, Jorrit de Vries, and E. Seyhan. "Satellite microwave estimates of soil moisture and applications for desertification studies." In Remote Sensing, edited by Edwin T. Engman. SPIE, 1998. http://dx.doi.org/10.1117/12.332765.

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Engman, Edwin T. "Development of long-wave microwave satellite systems for measuring soil moisture." In Remote Sensing, edited by Hiroyuki Fujisada. SPIE, 1998. http://dx.doi.org/10.1117/12.333668.

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"Error characterization of microwave satellite soil moisture data sets using Fourier analysis." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l19.su.

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Reports on the topic "Microwave Satellite Soil Moisture"

1

Pradhan, Nawa Raj. Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42128.

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A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Canyonlands National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, June 2023. http://dx.doi.org/10.36967/2299619.

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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects—such as impacts to forage, habitat, fire potential, and erosion—as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Canyonlands National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Capitol Reef National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, March 2023. http://dx.doi.org/10.36967/2297289.

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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects, such as impacts to forage, habitat, fire potential, and erosion, as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Capitol Reef National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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