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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 #349828

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: The added value of assimilating remotely sensed soil moisture for analysis of soil moisture-air temperature interactions

item DONG, J. - US Department Of Agriculture (USDA)
item Crow, Wade

Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/8/2018
Publication Date: 7/21/2018
Citation: Dong, J., Crow, W.T. 2018. The added value of assimilating remotely sensed soil moisture for analysis of soil moisture-air temperature interactions. Water Resources Research. 54:6072-6084.

Interpretive Summary: Air temperature is often significantly impacted by the local availability of water in the near-surface soil column. Increased soil water leads to more evaporation along the earth’s surface which, in turn, produces lower air temperatures. Therefore, understanding this relationship is important for improving our ability to forecast air temperature extremes associated with agricultural drought. This paper demonstrates, for the first time, how the availability of remotely-sensed surface soil moisture estimates can be leveraged to improve our global understanding of the local coupling between soil moisture and air temperature extremes. In particular, it demonstrates how past efforts to estimate this coupling (which did not utilize remotely-sensed soil moisture) systematically underestimated its strength (and thus its importance for climate and weather forecasting). The results of this study will eventually be used by climate and weather prediction modelers to improve their ability to forecast air temperature extremes in agricultural regions.

Technical Abstract: To date, the direct use of long-term remote-sensing soil moisture datasets for examining surface/atmosphere interaction issues has been hampered by the presence of significant random errors and data gaps in these products. This study investigates the potential for obtaining an improved representation of soil moisture - air temperature interaction analysis via the assimilation of long-term, satellite-based soil moisture datasets into a simple, prognostic model driven by observed rainfall. In particular, we utilize simultaneous scatterometer and radiometer-based soil moisture products obtained from the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product and a triple collocation analysis (TCA) approach to estimate the variance of modeling and observation errors (required as input by a data assimilation system). Results show that assimilating remotely sensed soil moisture leads to stronger interaction strength estimates than those obtained from modeled or remotely sensed soil moisture alone. This information is attributed to the improved signal-to-noise characteristics of the assimilation analysis. In addition, we demonstrate the importance of TCA for optimizing the performance of the data assimilation system.