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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 #367049
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: Observational assessment of the relationship between the Evaporative Stress Index and soil moisture and temperature across the United States

Author
item ZHONG, Y. - University Of Wisconsin
item OTKIN, J. - University Of Wisconsin
item Anderson, Martha
item HAIN, C. - Nasa Marshall Space Flight Center

Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/13/2020
Publication Date: 7/1/2020
Citation: Zhong, Y., Otkin, J., Anderson, M.C., Hain, C. 2020. Observational assessment of the relationship between the Evaporative Stress Index and soil moisture and temperature across the United States. Journal of Hydrometeorology. 21(7):1469–1484. https://doi.org/10.1175/JHM-D-19-0205.1.
DOI: https://doi.org/10.1175/JHM-D-19-0205.1

Interpretive Summary: The Evaporative Stress Index (ESI) is a satellite-based index of vegetation water use and health, and has become a widely used indicator of agricultural drought. This paper investigates the relationships between the ESI and soil moisture and temperature conditions measured with the U.S. Climate Reference Network to better understand seasonal ESI responses in different climatic regions. The study found strong relationships between ESI and soil moisture conditions, particularly in the southern Great Plains and the north-central Corn Belt. These results indicate that satellite remote sensing can provide valuable information about crop and rangeland conditions and response to drought for the United States.

Technical Abstract: The Evaporative Stress Index (ESI) is a useful tool for monitoring agricultural and ecological droughts and, therefore, it is highly desirable to predict changes in the ESI and the subsequent land surface conditions over sub-seasonal timescales. To explore the potential predictability in the ESI, lead-lag correlation analysis is employed to examine the cause-and-effect relationships between the satellite-derived ESI and soil moisture and soil temperature as measured by the US Climate Reference Network (USCRN) from 2010 to 2017. The ESI responses to changes in soil conditions are further quantified with the covariance of soil moisture and temperature and a lagging ESI. The role of vegetation state and cover for the ESI variability is not examined explicitly, but is fundamental in explaining the ESI-soil temperature relationships.The analyses reveal strong seasonality and regional characteristics of the ESI-subsoil condition relationships across the US, with the largest impacts on the ESI found in the central US. In the southern Great Plains, which exhibits the longest memory in the ESI, the ESI responds significantly to changes in soil moisture from spring to early summer, and then to changes in soil temperature (which serve as a proxy for surface temperature) during the rest of the growing season. Likewise, the ESI in the north-central US responds both to changes in soil moisture and to changes in soil temperature, with the response coefficients among the largest found across the US. In the northern Great Plains, the ESI responds more often to changes in soil temperature than changes in soil moisture through the growing season. The significant ESI responses to soil moisture across the US conform to a first-order inverse relationship with background soil moisture. These findings can contribute to predicting changes in the ESI as well as model simulation of the ESI-land surface interactions at a process level.