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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #363530
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: A global assessment of added value in the SMAP Level-4 soil moisture product relative to its baseline land surface model

Author
item DONG, J. - US Department Of Agriculture (USDA)
item Crow, Wade
item REICHLE, R. - Goddard Space Flight Center
item LIU, Q. - Goddard Space Flight Center
item LEI, F. - US Department Of Agriculture (USDA)
item Cosh, Michael

Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2019
Publication Date: 6/7/2019
Citation: Dong, J., Crow, W.T., Reichle, R., Liu, Q., Lei, F., Cosh, M.H. 2019. A global assessment of added value in the SMAP Level-4 soil moisture product relative to its baseline land surface model. Geophysical Research Letters. https://doi.org/10.1029/2019GL083398.
DOI: https://doi.org/10.1029/2019GL083398

Interpretive Summary: Soil moisture is an important climate variable because of its impact on the land surface water, energy, and nutrient cycles. For example, soil moisture partly controls how much of the water from any given rainfall event is stored in the soil and becomes available for natural and agricultural plant growth, and how much of that water runs off into creeks, streams, lakes, and reservoirs. Satellite observations that are suitable to derive global estimates of soil moisture are available from the NASA Soil Moisture Active Passive (SMAP) satellite mission, which was launched in early 2015. This paper introduces and applies a new technique for summarizing this added value of SMAP soil moisture estimates, relative to existing soil moisture estimates available from numerical land surface models. The approach is noteworthy in that it does not rely on ground-based soil moisture observations and, as a result, can be applied globally. Results from this paper will be used to refine future versions of SMAP soil moisture data products and enhance their value for the global monitoring of agricultural drought.

Technical Abstract: The Soil Moisture Active Passive (SMAP) Level-4 product provides enhanced soil moisture estimates based on the assimilation of SMAP brightness temperature observations into a land surface model. Here, we develop a novel correlation-based analysis to quantify the relative skill of SMAP Level-4 and model-only surface soil moisture using one additional, independent surface 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 small impact in data-rich areas including the United States and Europe. In contrast, much larger improvement is found in data-sparse regions of Africa, South American and Asia, where model-only simulations are disproportionately impacted by low-quality model forcing. Consequently, ground validation conducted in data-rich areas does not adequately sample the added value of SMAP data assimilation for data-sparse regions (constituting approximately 80% of global land areas) and substantially underestimates the added skill provided by the SMAP Level-4 system.