Assessment of the SMAP Level-4 Surface and Root-Zone Soil Moisture Product Using In Situ Measurements

Rolf H. Reichle NASA Goddard Space Flight Center, Greenbelt, Maryland

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Gabrielle J. M. De Lannoy KU Leuven, Heverlee, Belgium

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Qing Liu NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Inc., Lanham, Maryland

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Joseph V. Ardizzone NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Inc., Lanham, Maryland

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Andreas Colliander Jet Propulsion Laboratory, Pasadena, California

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Austin Conaty NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Inc., Lanham, Maryland

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Wade Crow Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

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Thomas J. Jackson Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

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Lucas A. Jones University of Montana, Missoula, Montana

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John S. Kimball University of Montana, Missoula, Montana

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Randal D. Koster NASA Goddard Space Flight Center, Greenbelt, Maryland

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Sarith P. Mahanama NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Inc., Lanham, Maryland

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Edmond B. Smith NASA Goddard Space Flight Center, Greenbelt, Maryland
Science Systems and Applications, Inc., Lanham, Maryland

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Aaron Berg Department of Geography, University of Guelph, Guelph, Ontario, Canada

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Simone Bircher CESBIO, University of Toulouse, CNES/CNRS/IRD/UPS, Toulouse, France

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David Bosch Southeast Watershed Research, Agricultural Research Service, USDA, Tifton, Georgia

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Todd G. Caldwell Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas

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Michael Cosh Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, Maryland

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Ángel González-Zamora University of Salamanca, Villamayor, Spain

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Chandra D. Holifield Collins Southwest Watershed Research Center, Agricultural Research Service, USDA, Tucson, Arizona

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Karsten H. Jensen Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

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Stan Livingston National Soil Erosion Research Laboratory, Agricultural Research Service, USDA, West Lafayette, Indiana

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Ernesto Lopez-Baeza University of Valencia, Valencia, Spain

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José Martínez-Fernández University of Salamanca, Villamayor, Spain

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Heather McNairn Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

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Mahta Moghaddam University of Southern California, Los Angeles, California

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Anna Pacheco Agriculture and Agri-Food Canada, Ottawa, Ontario, Canada

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Thierry Pellarin Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France

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John Prueger National Laboratory for Agriculture and the Environment, Agricultural Research Service, USDA, Ames, Iowa

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Tracy Rowlandson Department of Geography, University of Guelph, Guelph, Ontario, Canada

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Mark Seyfried Northwest Watershed Research Center, Agricultural Research Service, USDA, Boise, Idaho

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Patrick Starks Grazinglands Research Laboratory, Agricultural Research Service, USDA, El Reno, Oklahoma

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Zhongbo Su Faculty of Geo-Information Science and Earth Observations (ITC), University of Twente, Enschede, Netherlands

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Marc Thibeault Comisión Nacional de Actividades Espaciales, Buenos Aires, Argentina

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Rogier van der Velde Faculty of Geo-Information Science and Earth Observations (ITC), University of Twente, Enschede, Netherlands

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Jeffrey Walker Monash University, Clayton, Victoria, Australia

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Xiaoling Wu Monash University, Clayton, Victoria, Australia

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Yijian Zeng Faculty of Geo-Information Science and Earth Observations (ITC), University of Twente, Enschede, Netherlands

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Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Rolf H. Reichle, rolf.reichle@nasa.gov
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