Enhancing Remote Sensing Based Yield Forecasting: Application to Winter Wheat in United States

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1 km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national and state level yield of winter wheat in the United States from 2001 to 2016.