Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics

Abstract

Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Land Surface Water Index (LSWI), the best time to predict corn yields was 55–60 days after green-up date. LSWI showed the strongest correlation (R2=0.568), followed by EVI (R2=0.497) and NDWI (R2=0.495). The peak correlation between Wide Dynamic Range Vegetation Index (WDRVI) and yield was detected 85 days after green-up date (R2=0.506). The correlation was generally low for Normalized Difference Vegetation Index (NDVI) (R2=0.385) and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error (NRMSE) ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.