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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 #367047

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: Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard

item LEI, F. - US Department Of Agriculture (USDA)
item Crow, Wade
item Kustas, William - Bill
item DONG, J - US Department Of Agriculture (USDA)
item YANG, Y. - US Department Of Agriculture (USDA)
item Knipper, Kyle
item Anderson, Martha
item Gao, Feng
item NOTARNICOLA, C. - Free University Of Bozen-Bolzano
item GREIFENEDER, F. - Free University Of Bozen-Bolzano
item McKee, Lynn
item Alfieri, Joseph
item HAIN, C. - Nasa Marshall Space Flight Center
item DOKOOZLIAN, N. - E & J Gallo Winery

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/20/2019
Publication Date: 3/15/2020
Citation: Lei, F., Crow, W.T., Kustas, W.P., Dong, J., Yang, Y., Knipper, K.R., Anderson, M.C., Gao, F.N., Notarnicola, C., Greifeneder, F., Mckee, L.G., Alfieri, J.G., Hain, C., Dokoozlian, N. 2020. Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard. Remote Sensing of Environment.

Interpretive Summary: It has been estimated that Californian wine growers over-irrigate their vineyards by as much as 30%. This excess water often degrades the quality of wine grapes and is a contributing factor to the overuse of groundwater resources in California’s Central Valley. However, wine growers consider it risky to reduce irrigation since under-irrigation can damage grape vines and reduce grape yields across multiple growing seasons. The key to minimizing vineyard irrigation without risk is the timely and accurate monitoring of root-zone soil moisture. To date, such monitoring has not been technically feasible. However, this paper describes the design and application of a new system that utilizes a combination of thermal and radar remote sensing techniques to estimate daily soil moisture at 30-m spatial resolution. Initial testing at an E&J Gallo vineyard in Lodi, California suggests that the approach has the potential to substantially improve Gallo’s ability to optimize irrigation application. Plans are currently being made to scale the approach up to multiple vineyard sites for further testing.

Technical Abstract: Efficient water use assessment and irrigation management is critical for the sustainability of irrigated agriculture, especially under changing climate conditions. Due to the impracticality of maintaining ground instrumentation over wide geographic areas, remote sensing and numerical model-based fine-scale mapping of soil water conditions have been applied for water resource applications at a range of spatial scales. Here, we present a prototype framework for integrating high-resolution thermal infrared (TIR) and synthetic aperture radar (SAR) remote sensing data into a soil-vegetation-atmosphere-transfer (SVAT) model with the aim of providing improved estimates of surface- and root-zone soil moisture that can support optimized irrigation management strategies. Specifically, remotely-sensed estimates of water stress (from TIR) and surface soil moisture retrievals (from SAR) are assimilated into a 30-m resolution SVAT model over a vineyard site in the Central Valley of California, U.S. The efficacy of our data assimilation algorithm is investigated via both the synthetic and real data experiments. Results demonstrate that a particle filtering approach is superior to an ensemble Kalman filter for handling the nonlinear relationship between model states and observations. In addition, biophysical conditions such as leaf area index are shown to impact the relationship between observations and states and must therefore be represented accurately in the assimilation model. Overall, both surface and root-zone soil moisture predicted via the SVAT model are enhanced through the assimilation of thermal and radar-based retrievals, suggesting the potential for improving irrigation management at the agricultural sub-field scale using a data assimilation strategy.