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Global-Scale Comparison of Passive (SMOS) and Active (ASCAT) Satellite Based Microwave Soil Moisture Retrievals with Soil Moisture Simulations (MERRA-Land)Global surface soil moisture (SSM) datasets are being produced based on active and passive microwave satellite observations and simulations from land surface models (LSM). This study investigates the consistency of two global satellite-based SSM datasets based on microwave remote sensing observations from the passive Soil Moisture and Ocean Salinity (SMOS;SMOSL3 version 2.5) and the active Advanced Scatterometer (ASCAT; version TUWien- WARP 5.5) with respect to LSM SSM from the MERRA-Land data product. The relationship between the global-scale SSM products was studied during the 2010-2012 period using (1) a time series statistics (considering both original SSM data and anomalies), (2) a space-time analysis using Hovmoller diagrams, and (3) a triple collocation error model. The SMOSL3 and ASCAT retrievals are consistent with the temporal dynamics of modeled SSM (correlation R (is) greater than 0.70 for original SSM) in the transition zones between wet and dry climates, including the Sahel, the Indian subcontinent, the Great Plains of North America, eastern Australia, and southeastern Brazil. Over relatively dense vegetation covers, a better consistency with MERRA-Land was obtained with ASCAT than with SMOSL3. However, it was found that ASCAT retrievals exhibit negative correlation versus MERRA-Land in some arid regions (e.g., the Sahara and the Arabian Peninsula). In terms of anomalies, SMOSL3 better captures the short term SSM variability of the reference dataset (MERRA-Land) than ASCAT over regions with limited radio frequency interference (RFI) effects (e.g., North America, South America, and Australia). The seasonal and latitudinal variations of SSM are relatively similar for the three products, although the MERRALand SSM values are generally higher and their seasonal amplitude is much lower than for SMOSL3 and ASCAT. Both SMOSL3 and ASCAT have relatively comparable triple collocation errors with similar spatial error patterns: (i) lowest errors in arid regions (e.g., Sahara and Arabian Peninsula), due to the very low natural variability of soil moisture in these areas, and Central America, and (ii) highest errors over most of the vegetated regions (e.g., northern Australia, India, central Asia, and South America). However, the ASCAT SSM product is prone to larger random errors in some regions (e.g., north-western Africa, Iran, and southern South Africa). Vegetation density was found to be a key factor to interpret the consistency with MERRA-Land between the two remotely sensed products (SMOSL3 and ASCAT) which provides complementary information on SSM. This study shows that both SMOS and ASCAT have thus a potential for data fusion into long-term data records.
Document ID
20150011648
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
Authors
A Al-Yaari
(Institut National de la Recherche Agronomique Villenave d'Ornon, France)
J P Wigneron
(Institut National de la Recherche Agronomique Villenave d'Ornon, France)
A Ducharne
(Paris VI University France)
Y H Kerr
(University of Toulouse Toulouse, Midi-Pyrénées, France)
W Wagner
(Technische Hochschule Vienna, Austria)
G De Lannoy
(Universities Space Research Association Columbia, Maryland, United States)
R Reichle
(Goddard Space Flight Center Greenbelt, Maryland, United States)
A Al Bitar
(University of Toulouse Toulouse, Midi-Pyrénées, France)
W Dorigo
(Technische Hochschule Vienna, Austria)
P Richaume
(University of Toulouse Toulouse, Midi-Pyrénées, France)
A Mialon
(University of Toulouse Toulouse, Midi-Pyrénées, France)
Date Acquired
June 26, 2015
Publication Date
August 13, 2014
Publication Information
Publication: Remote Sensing of Environment
Publisher: Elsevier
Volume: 152
Issue Publication Date: September 1, 2014
ISSN: 0034-4257
URL: https://www.sciencedirect.com/science/article/pii/S0034425714002612
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN21513
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Portions of document may include copyright protected material.
Keywords
ASCAT
MERRA-Land
SMOS
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