A Global Assessment of Added Value in the SMAP Level 4 Soil Moisture Product Relative to Its Baseline Land Surface Model. Issue 12 (27th June 2019)

• Record Type: Journal Article
• Authors: Dong, Jianzhi; Crow, Wade; Reichle, Rolf; Liu, Qing; Lei, Fangni; Cosh, Michael H.
• Is Part Of: Geophysical research letters. Volume 46:Issue 12(2019)
• Page Start: 6604
• Page End: 6613
• Publication Date: 2019-06-27
• Subjects: soil moisture -- SMAP -- SMAP L4 -- error evaluation -- data assimilation; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2019GL083398
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: 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. Plain Language Summary: Soil Moisture Active Passive (SMAP) level 4 product merges land surface model estimates and SMAP observations using an ensemble Kalman filter. This study demonstrates that the accuracy of the level 4 product relative to its baseline land surface model at the global scale can be solved using only one independent remote sensing soil moisture product. Results demonstrate that the added value of SMAP data assimilation depends on the quality of precipitation inputs into the baseline model. Generally, assimilating SMAP observations has limited benefits in data‐rich regions but strongly improves soil moisture accuracy in data‐sparse regions. Given that in situ soil moisture observations are typically confined to data‐rich regions, evaluation based on these observations strongly underestimates the true added value of SMAP data assimilation for global soil moisture estimation. Key Points: A single noisy but independent soil moisture product can be used to globally quantify the added value of soil moisture data assimilation The SMAP L4 product provides the most value versus a model‐only baseline in data‐poor and lightly vegetated regions Because previous SMAP L4 assessments focused only on data‐rich regions, they underestimated the contribution of SMAP data assimilation