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A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model
Author(s) -
Dong Jianzhi,
Crow Wade,
Reichle Rolf,
Liu Qing,
Lei Fangni,
Cosh Michael H.
Publication year - 2019
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2019gl083398
Subject(s) - environmental science , forcing (mathematics) , water content , data assimilation , moisture , brightness , brightness temperature , atmospheric sciences , remote sensing , climatology , meteorology , geology , geography , geotechnical engineering , physics , optics
The Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, a quantitative estimate of the relative skill of SMAP Level‐4 and model‐only surface soil moisture (vs. true soil moisture) is derived using only one additional noisy (but independent) soil moisture product. The method is applied globally and verified using high‐quality, ground‐based measurements where available. Results demonstrate that assimilating SMAP brightness temperature has relatively little impact in data‐rich areas like the United States and Europe. In contrast, much larger improvement is observed in data‐sparse regions, including much of Africa and central Australia, where model‐only simulations are disproportionately impacted by low‐quality model forcing. Therefore, ground validation conducted in data‐rich areas does not adequately sample the added value of SMAP data assimilation for data‐sparse regions and substantially underestimates the added skill provided by the SMAP level 4 system.