An inter-comparison of soil moisture data products from satellite remote sensing and a land surface model |
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| Institution: | 1. Department of Civil and Environmental Engineering, Hanyang University, Seoul 133-791, Republic of Korea;2. Water Resources and Remote Sensing Laboratory, Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University, Suwon 440-746, Republic of Korea;3. Department of Geodesy and Geo-information, Vienna University of Technology, Vienna, Austria;1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena CA91109, USA;2. USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA;3. University of Tsukuba, Tsukuba, Japan;4. University of Guelph, Canada;5. USDA ARS Southeast Watershed Research, Tifton, GA, USA;6. USDA ARS Southwest Watershed Research, Tucson, AZ, USA;7. University of Texas at Austin, TX, USA;8. Princeton University, USA;9. Kuwait University, Kuwait;10. University of Valencia, Spain;11. Instituto Hispano Luso de Investigaciones Agrarias (CIALE), Universidad de Salamanca, Spain;12. USDA ARS National Soil Erosion Research Lab, West Lafayette, IN, USA;13. Agriculture and Agri-food Canada, Canada;14. University of Southern California, California, USA;15. Institute of Bio- and Geosciences: Agrosphere (IBG-3), Research Center Juelich, Germany;p. European Academy of Bozen/Bolzano (EURAC), Italy;q. University of Grenoble, France;r. USDA ARS National Laboratory for Agriculture and the Environment, Ames, IA, USA;s. Finnish Meteorological Institute, Finland;t. Universidad Nacional Autónoma de México, Mexico;u. USDA ARS Northwest Watershed Management Research, Boise, ID, USA;v. USDA ARS Grazinglands Research Laboratory, El Reno, OK, USA;w. University of Twente, The Netherlands;x. Comisión Nacional de Actividades Espaciales (CONAE), Argentina;y. Vienna University of Technology, Austria;z. Monash University, Australia;11. NASA Goddard Space Flight Center, Greenbelt, MD, USA;12. Massachusetts Institute of Technology, MA, USA;1. Department of Civil Engineering, Monash University, Clayton Campus, VIC 3800, Australia;2. Fenner School of Environment and Society, The Australian National University, Canberra, Australia;3. U.S. Department of Agriculture ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA;1. School of Civil and Environmental Engineering, University of New South Wales, Sydney, Australia;2. ARC Centre of Excellence for Climate Systems Science & Climate Change Research Centre, University of New South Wales, Sydney, Australia;3. Earth and Climate Cluster, Department of Earth Sciences, VU University Amsterdam, Amsterdam, Netherlands |
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| Abstract: | Significant advances have been achieved in generating soil moisture (SM) products from satellite remote sensing and/or land surface modeling with reasonably good accuracy in recent years. However, the discrepancies among the different SM data products can be considerably large, which hampers their usage in various applications. The bias of one SM product from another is well recognized in the literature. Bias estimation and spatial correction methods have been documented for assimilating satellite SM product into land surface and hydrologic models. Nevertheless, understanding the characteristics of each of these SM data products is required for many applications where the most accurate data products are desirable. This study inter-compares five SM data products from three different sources with each other, and evaluates them against in situ SM measurements over 14-year period from 2000 to 2013. Specifically, three microwave (MW) satellite based data sets provided by ESA's Climate Change Initiative (CCI) (CCI-merged, -active and -passive products), one thermal infrared (TIR) satellite based product (ALEXI), and the Noah land surface model (LSM) simulations. The in-situ SM measurements are collected from the North American Soil Moisture Database (NASMD), which involves more than 600 ground sites from a variety of networks. They are used to evaluate the accuracies of these five SM data products. In general, each of the five SM products is capable of capturing the dry/wet patterns over the study period. However, the absolute SM values among the five products vary significantly. SM simulations from Noah LSM are more stable relative to the satellite-based products. All TIR and MW satellite based products are relatively noisier than the Noah LSM simulations. Even though MW satellite based SM retrievals have been predominantly used in the past years, SM retrievals of the ALEXI model based on TIR satellite observations demonstrate skills equivalent to all the MW satellite retrievals and even slightly better over certain regions. Compared to the individual active and passive MW products, the merged CCI product exhibits higher anomaly correlation with both Noah LSM simulations and in-situ SM measurements. |
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| Keywords: | Soil moisture product Validation Active and passive microwave Thermal infrared remote sensing |
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