Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation Indices for Estimating Corn and Soybean Yields Over the U.S.

摘要: This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and/or vegetation characteristics within agricultural regions the contiguous United States (CONUS). These have been developed using a variety techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey/in-field collection. The objectives are to assess relative utility each dataset for monitoring crop yield variability, quantitatively their capacity predicting end-of-season corn soybean yields, examine evolution yield-index correlations during growing season. analysis is unique both with regards number examined predictor detailed assessment water availability timing on production Correlation results indicate that over CONUS, at state-level moisture ET indices can provide better information forecasting yields than vegetation-based such as normalized difference index. strength correlation strongly depends interannual variability in measured given location. In this case study, some remotely derived skill comparable in-situ field survey-based data—further demonstrating these remote sensing-based approaches estimating yield.