The SMOS Soil Moisture Retrieval Algorithm
Yann Kerr,Philippe Waldteufel,P. Richaume,Jean-Pierre Wigneron,Paolo Ferrazzoli,A. Mahmoodi,Ahmad Al Bitar,Francois Cabot,C. Gruhier,S. Juglea,Delphine Leroux,Arnaud Mialon,Steven Delwart +12 more
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TLDR
A retrieval algorithm to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3 is given, discusses the caveats, and provides a glimpse of the Cal Val exercises.Abstract:
The Soil Moisture and Ocean Salinity (SMOS) mission is European Space Agency (ESA's) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint program between ESA Centre National d'Etudes Spatiales (CNES) and Centro para el Desarrollo Tecnologico Industrial. SMOS carries a single payload, an L-Band 2-D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere, and hence the instrument probes the earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. The goal of the level 2 algorithm is thus to deliver global soil moisture (SM) maps with a desired accuracy of 0.04 m3/m3. To reach this goal, a retrieval algorithm was developed and implemented in the ground segment which processes level 1 to level 2 data. Level 1 consists mainly of angular brightness temperatures (TB), while level 2 consists of geophysical products in swath mode, i.e., as acquired by the sensor during a half orbit from pole to pole. In this context, a group of institutes prepared the SMOS algorithm theoretical basis documents to be used to produce the operational algorithm. The principle of the SM retrieval algorithm is based on an iterative approach which aims at minimizing a cost function. The main component of the cost function is given by the sum of the squared weighted differences between measured and modeled TB data, for a variety of incidence angles. The algorithm finds the best set of the parameters, e.g., SM and vegetation characteristics, which drive the direct TB model and minimizes the cost function. The end user Level 2 SM product contains SM, vegetation opacity, and estimated dielectric constant of any surface, TB computed at 42.5°, flags and quality indices, and other parameters of interest. This paper gives an overview of the algorithm, discusses the caveats, and provides a glimpse of the Cal Val exercises.read more
Citations
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High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scale Spatial Variability Across the United States
TL;DR: In this article , the authors reveal the striking variability of local-scale soil moisture (SM) spatiotemporal variability across the United States using SMAP-HydroBlocks, a satellite-based surface SM data set at 30-m resolution.
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Evaluation of satellite-model proxies for hydro-meteorological services in the upper Zambezi
Mark R. Jury,Mark R. Jury +1 more
TL;DR: In this article, satellite and model estimates of hydro-meteorology conditions using data in the period 1998-2015, to supplement the very limited operational reports coming from the region.
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Assimilating SMOS Brightness Temperature for Hydrologic Model Parameters and Soil Moisture Estimation with an Immune Evolutionary Strategy
TL;DR: The results demonstrate that assimilating TB has tremendous potential as an approach to improve the estimation of distributed model parameters and SMs of surface and root zone at a grid scale, and the immune evolution strategy is effective for increasing the accuracy of approximation in sampling.
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The soil moisture data bank: The ground-based, model-based, and satellite-based soil moisture data
TL;DR: In this article, the authors provide an overview of the state-of-the-art open-access soil moisture datasets at various spatial and temporal scales, which can be used for forecasting weather and climate variability, monitoring the influence of climate change on an ecosystem, drought monitoring and prediction, water resources management, agricultural production, and more.
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Long-Term Spatiotemporal Variations in Soil Moisture in North East China Based on 1-km Resolution Downscaled Passive Microwave Soil Moisture Products
TL;DR: A spatial fusion downscaling model (SFDM) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is constructed to overcome the deficiencies of passive microwave soil moisture products with low resolution, and a time series reconstruction of the difference decomposition (TSRDD) method is developed to create long-term multisensor soil moisture datasets.
References
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TL;DR: In this article, the authors present a model of a MICROWAVE REMOTE SENSING FUNDAMENTALS and RADIOMETRY, which is based on the idea of surface scattering.
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The Soil Moisture Active Passive (SMAP) Mission
Dara Entekhabi,Eni G. Njoku,Peggy O'Neill,Kent Kellogg,Wade T. Crow,W. Edelstein,Jared Entin,Shawn D Goodman,Thomas J. Jackson,Joel T. Johnson,John S. Kimball,Jeffrey R. Piepmeier,Randal D. Koster,Neil R.W. Martin,Kyle C. McDonald,Mahta Moghaddam,Susan Moran,Rolf H. Reichle,Jiancheng Shi,Michael W. Spencer,Samuel W Thurman,Leung Tsang,Jakob van Zyl +22 more
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Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models
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Journal ArticleDOI
The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle
Yann Kerr,Philippe Waldteufel,Jean-Pierre Wigneron,Steven Delwart,Francois Cabot,Jacqueline Boutin,Maria-José Escorihuela,Jordi Font,Nicolas Reul,C. Gruhier,S. Juglea,Mark R. Drinkwater,Achim Hahne,Manuel Martin-Neira,Susanne Mecklenburg +14 more
TL;DR: The SMOS satellite was launched successfully on November 2, 2009, and will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe.
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