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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 #357968

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: Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment in 2016

Author
item Cosh, Michael
item White, William - Alex
item COLLIANDER, A. - Jet Propulsion Laboratory
item Jackson, Thomas
item Prueger, John
item HORNBUDDE, B. - University Of Iowa
item Hunt, Earle - Ray
item MCNAIRN, H. - Agriculture And Agri-Food Canada
item POWRES, J. - Agriculture And Agri-Food Canada
item WALKER, V.A. - University Of Iowa

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2019
Publication Date: 2/12/2019
Citation: Cosh, M.H., White, W.A., Colliander, A., Jackson, T.J., Prueger, J.H., Hornbudde, B., Hunt Jr, E.R., McNairn, H., Powres, J., Walker, V. 2019. Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment in 2016. Journal of Applied Remote Sensing (JARS). 13(1):014516. https://doi.org/10.1117/1.JRS.13.014516.
DOI: https://doi.org/10.1117/1.JRS.13.014516

Interpretive Summary: Vegetation water content is a valuable parameter for soil moisture remote sensing products because of its influence on the brightness temperature signal. Dense agricultural canopies are a challenge for remote sensing because of the dynamic character of crops throughout the growth cycle. A field experiment conducted in Iowa and Manitoba in the summer of 2016 was a good opportunity to test the ability of optical and near infrared satellites, such as Landsat 8, to estimate vegetation water content for peak biomass/moisture conditions. Ground sampling was conducted and regression equations developed for major crop types in the northern great plains which will be useful for future crop water content estimation.

Technical Abstract: Vegetation biomass and water content (VWC) are important land surface parameters for estimating surface soil moisture from microwave satellite platforms. Determining crop type specific equations for water content is a key element in the advancement of vegetation parameterization for further development of the satellite algorithms. As a part of the calibration and validation program for NASA’s Soil Moisture Active Passive (SMAP) Mission, a field experiment was conducted in northern central Iowa and southern Manitoba to investigate the performance of the SMAP products for these intensive agricultural regions. Early indications from the calibration and validation program reveal poor performance in both domains, which served as core validation sites for the mission. Landsat 8 data was used to compute a Normalized Difference Water Index (NDWI) for the entire summer of 2016 and extensive vegetation water content sampling was conducted to determine how to best characterize daily estimates of microwave scale vegetation water content for improved algorithm implementation. In Iowa, regression equations for corn and soybean were developed with rmse values of 1.37 and 1.10 kg/m2, respectively. In Manitoba, corn and soybean equations were developed with an rmse of 0.55 and 0.25 kg/m2. Additional crop equations which were developed included winter wheat (rmse of 0.07 kg/m2), canola (rmse of 0.90 kg/m2), oats (rmse of 0.74 kg/m2), and black beans (rmse of 0.31 kg/m2). It was observed during the experiment that the soybean crop had an exceptionally high biomass as a result of significant rainfall and weather conditions. Future implementation of these equations into algorithm development for satellite and airborne radiative transfer modeling will improve the overall performance in agricultural domains.