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estimating surface soil moisture from smap observations using a neural network techniqueA Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m(exp. 3)m(exp. -3), 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m(exp. 3)m(exp. -3), 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.
Document ID
20170011218
Document Type
Accepted Manuscript (Version with final changes)
Authors
J Kolassa
(Universities Space Research Association Columbia, Maryland, United States)
R H Reichle
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Q Liu
(Science Systems and Applications (United States) Lanham, Maryland, United States)
S H Alemohammad
(Columbia University New York, New York, United States)
P Gentine
(Columbia University New York, New York, United States)
K Aida
(University of Tsukuba Tsukuba, Ibaraki, Japan)
J Asanuma
(University of Tsukuba Tsukuba, Ibaraki, Japan)
S Bircher
(Universities Space Research Association Columbia, Maryland, United States)
T Caldwell
(The University of Texas at Austin Austin, Texas, United States)
A Colliander
(Jet Propulsion Lab La Cañada Flintridge, California, United States)
M Cosh
(United States Department of Agriculture Washington D.C., District of Columbia, United States)
C Holifield Collins
(United States Department of Agriculture Washington D.C., District of Columbia, United States)
T J Jackson
(United States Department of Agriculture Washington D.C., District of Columbia, United States)
J Martinez-Fernandez
(Universidad de Salamanca Salamanca, Spain)
H McNairn
(Ministry of Agriculture, Food and Rural Affairs Guelph, Ontario, Canada)
A Pacheco
(Ministry of Agriculture, Food and Rural Affairs Guelph, Ontario, Canada)
M Thibeault
(National Space Activities Commission Buenos Aires, Argentina)
J P Walker
(Monash University Melbourne, Victoria, Australia)
Date Acquired
November 22, 2017
Publication Date
November 11, 2017
Publication Information
Publication: Remote Sensing of Environment
Volume: 204
ISSN: 0034-4257
Subject Category
Earth Resources and Remote Sensing
Numerical Analysis
Report/Patent Number
GSFC-E-DAA-TN48959
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
CONTRACT_GRANT: NNX15AB30G
CONTRACT_GRANT: NNG17HP01C
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Keywords
Soil Moisture Remote Sensing

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