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Development and Assessment of the SMAP Enhanced Passive Soil Moisture ProductLaunched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.
Document ID
20180006523
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
External Source(s)
Authors
Steven K Chan (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Rajat Bindlish (Goddard Space Flight Center Greenbelt, Maryland, United States)
Peggy O'Neill (Goddard Space Flight Center Greenbelt, Maryland, United States)
Thomas Jackson (Agricultural Research Service Washington D.C., District of Columbia, United States)
Eni Njoku (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Scott Dunbar (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Julian Chaubell (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Jeffrey Piepmeier (Goddard Space Flight Center Greenbelt, Maryland, United States)
Simon Yueh (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Dara Entekhabi (Massachusetts Institute of Technology Cambridge, Massachusetts, United States)
Andreas Colliander (Jet Propulsion Lab La Cañada Flintridge, California, United States)
Fan Chen (Science Systems and Applications (United States) Lanham, Maryland, United States)
Michael H Cosh (Agricultural Research Service Washington D.C., District of Columbia, United States)
Todd Caldwell (The University of Texas at Austin Austin, Texas, United States)
Jeffrey Walker (Monash University Melbourne, Victoria, Australia)
Aaron Berg (University of Guelph Guelph, Ontario, Canada)
Heather McNairn (Ministry of Agriculture and Food Guelph, Ontario, Canada)
Marc Thibeault (National Space Activities Commission Buenos Aires, Argentina)
Jose Martínez-Fernández (Universidad de Salamanca Salamanca, Spain)
Frederik Uldall (Technical University of Denmark Kongens Lyngby, Hovedstaden, Denmark)
Mark Seyfried (Agricultural Research Service Washington D.C., District of Columbia, United States)
David Bosch (Agricultural Research Service Washington D.C., District of Columbia, United States)
Patrick Starks (Agricultural Research Service Washington D.C., District of Columbia, United States)
Chandra Holifiel Collins (Agricultural Research Service Washington D.C., District of Columbia, United States)
John Prueger (Agricultural Research Service Washington D.C., District of Columbia, United States)
Rogier van der Velde (Universiteit Twente Enschede, Netherlands)
Jun Asanuma (University of Tsukuba Tsukuba, Ibaraki, Japan)
Michael Palecki (National Climatic Data Center Asheville, NC, United States)
Eric E Small (University of Colorado Boulder Boulder, Colorado, United States)
Marek Zreda (University of Arizona Tucson, Arizona, United States)
Jean-Christophe Calvet (Centre National de Recherches Meteorologiques Toulouse, France)
Wade T Crow (United States Department of Agriculture Washington D.C., District of Columbia, United States)
Yann Kerr ORCID(Centre National D'Etudes Spatiales Paris, France)
Date Acquired
October 18, 2018
Publication Date
October 13, 2017
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 204
Issue Publication Date: January 1, 2018
ISSN: 0034-4257
e-ISSN: 1879-0704
DOI: doi:10.1016/j.rse.2017.08.025
Subject Category
Earth Resources And Remote Sensing
Numerical Analysis
Report/Patent Number
GSFC-E-DAA-TN46944
E-ISSN: 1879-0704
ISSN: 0034-4257
Report Number: GSFC-E-DAA-TN46944
Funding Number(s)
CONTRACT_GRANT: NNN12AA01C
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Enhanced
Validation
Retrieval
Assessment
Passive

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