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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 #328377

Research Project: Leveraging Remote Sensing, Land Surface Modeling and Ground-based Observations ... Variables within Heterogeneous Agricultural Landscapes

Location: Hydrology and Remote Sensing Laboratory

Title: Inter-comparison of soil moisture, water use and vegetation indices for estimating corn and soybean yields over the U.S.

Author
item MLADENOVA, I. - National Aeronautics And Space Administration (NASA)
item BOLTEN, J. - National Aeronautics And Space Administration (NASA)
item Crow, Wade
item Anderson, Martha
item HAIN, C. - University Of Maryland
item MUELLER, RICK - National Agricultural Statistical Service (NASS, USDA)
item JOHNSON, D. - National Agricultural Statistical Service (NASS, USDA)

Submitted to: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2016
Publication Date: 4/1/2017
Citation: Mladenova, I., Bolten, J., Crow, W.T., Anderson, M.C., Hain, C., Mueller, R., Johnson, D. 2017. Inter-comparison of soil moisture, water use and vegetation indices for estimating corn and soybean yields over the U.S. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(4):1328-1343.

Interpretive Summary: Monitoring crop status and health via ground-based observations is an expensive and impractical activity when attempted over broad geographic areas. Satellite remote sensing offers a viable alternative to such intensive ground-based monitoring. A large range of remote sensing variables (e.g., vegetation greenness, soil moisture availability, surface temperature and evapotranspiration) can be related to the current health - and future yield - of an agricultural crop. Within the past five years, substantial progress has been made in the remote retrieval of a number of these variables. This paper offers a systematic comparison between these remotely-sensed based variables and U.S. county-level corn and soybean yields within the past decade. By objectively inter-comparing the ability of various remotely-sensed variables to predict yield, we able to determine which combination of variables offers the greatest potential for improving corn and soybean yield forecasting within the United States. Eventually, the USDA National Agricultural Statistics Service will use these results to enhance their ability to predict the impact of summertime agricultural drought on domestic corn and soybean production.

Technical Abstract: In water-limited agricultural regions, root-zone soil moisture is a significant factor controlling agricultural crop condition and yield. The rate at which soil moisture is used by crops (evapotranspiration, ET) is an indicator of crop stage growth and overall vegetation health. Therefore, routine, spatially distributed data on soil moisture and water use can significantly benefit operational crop condition assessments and yield forecasting efforts, both within the U.S. and globally. This paper presents an inter-comparative study of twelve large-scale datasets (nine soil moisture/ET- and three vegetation-related parameters) that are produced operationally, and quantitatively assesses their capacity for predicting end-of-season corn and soybean yields within the contiguous U.S (CONUS). These datasets are all well-known to the science community and have been developed using a variety of techniques, including: hydrologic modeling, satellite-based retrievals, data assimilation, and survey/in-field data collection. The objective is to assess the relative utility of each dataset for monitoring crop yield variability and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at the state level, soil moisture and ET indices can provide better information for forecasting corn and soybean yields than do vegetation-based indices such as NDVI. The strength of correlation with corn and soybean yields strongly depends on the inter-annual variability in yield measured at a given location. Correlations reach a maximum around the month of July and August, when corn and soybean yield production is maximally sensitive to moisture deficiencies in the root zone. In this case study, some of the remotely derived datasets examined offered comparable skill to in situ field survey-based data, further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.