An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data

The Midwestern United States is one of the world’s most important corn-producing regions. Monitoring and forecasting of corn yields in this intensive agricultural region are important activities to support food security, commodity markets, bioenergy industries, and formation of national policies. This study aims to develop forecasting models that have the capability to provide mid-season prediction of county-level corn yields for the entire Midwestern United States. We used multi-temporal MODIS NDVI (normalized difference vegetation index) 16-day composite data as the primary input, with digital elevation model (DEM) and parameter-elevation relationships on independent slopes model (PRISM) climate data as additional inputs. The DEM and PRISM data, along with three types of cropland masks were tested and compared to evaluate their impacts on model predictive accuracy. Our results suggested that the use of general cropland masks (e.g., summer crop or cultivated crops) generated similar results compared with use of an annual corn-specific mask. Leave-one-year-out cross-validation resulted in an average R² of 0.75 and RMSE value of 1.10 t/ha. Using a DEM as an additional model input slightly improved performance, while inclusion of PRISM climate data appeared not to be important for our regional corn-yield model. Furthermore, our model has potential for real-time/early prediction. Our corn yield esitmates are available as early as late July, which is an improvement upon previous corn-yield prediction models. In addition to annual corn yield forecasting, we examined model uncertainties through spatial and temporal analysis of the model's predictive error distribution. The magnitude of predictive error (by county) appears to be associated with the spatial patterns of corn fields in the study area.