On optimizing a MODIS-based framework for in-season corn yield forecast

Abstract

Abstract Accurate forecast of corn yields is important for decision making regarding food and energy management strategies. In this work, we developed an unprecedented optimized framework for the MODIS-based mid-season corn yield forecasting over five producing states of the United States: Illinois, Indiana, Iowa, Nebraska, and Ohio. We evaluated Enhanced Vegetation Index (EVI)-based forecasts under different schemes accounting for: different machine learning techniques with a mid-season composite or multi-temporal composites as inputs, four (county-, district-, state-, and global-based) training domains, and 16-day composites versus daily interpolated composites involving the day of pixels as predictors. Under the best identified scheme, we compared the EVI-based forecasts with those based on Normalized Difference Vegetation Index (NDVI), Leaf area Index (LAI) and Fraction of Absorbed Photosynthetic Active Radiation (FPAR). EVI and NDVI were transformed to LAI (named as LAIEVI and LAINDVI) as predictors for producing EVI- and NDVI-based forecasts. We evaluated both county- and state-level forecasts using the percent error (PE), mean absolute PE (MAPE) and determination coefficient (R2). The linear regression models driven by the single latest composite in mid-season often outperformed elastic net and random forest models driven by multi-temporal composites. The forecast performance decreased with longer subsets of EVI composites being used. The performance under the different training domains varied by states and forecast level (county or state), although the changes within states were mostly non-significant except in Nebraska. The forecasts based on 16-day and daily composites performed similarly, indicating that the use of information about the day of pixel composite provides no additional benefit to the yield forecast. For the best EVI-based schemes, the medium annual MAPE (PE) at the county (state) level varied between 6.1% (2.4%) and 7.7% (5.3%) across states while the medium annual R2 (interannual R2) varied between 0.54 (0.59) and 0.82 (0.86). Results suggested that, while EVI was, in general, the best predictor for the Corn Belt as a whole, the adequacy of the EVI- and NDVI-based forecasts varied by states and largely exceeded that of the LAI- and FPAR-based forecasts. Compared with the EVI-based forecasts, the NDVI-based forecasts performed better in Iowa (MAPE’s 0.9% and 1.43% lower at the county and state level), similar in Nebraska and worse in the other states. Overall, the best state-level forecasts consistently outperformed concurrent National Agricultural Statistical Service (NASS) forecasts.