Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics

We used data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) in association with county-level data from the United States Department of Agriculture (USDA) to develop empirical models predicting maize and soybean yield in the Central United States. As part of our analysis we also tested the ability of MODIS to capture inter-annual variability in yields. Our results show that the MODIS two-band Enhanced Vegetation Index (EVI2) provides a better basis for predicting maize yields relative to the widely used Normalized Difference Vegetation Index (NDVI). Inclusion of information related to crop phenology derived from MODIS significantly improved model performance within and across years. Surprisingly, using moderate spatial resolution data from the MODIS Land Cover Type product to identify agricultural areas did not degrade model results relative to using higher-spatial resolution crop-type maps developed by the USDA. Correlations between vegetation indices and yield were highest 65-75 days after greenup for maize and 80 days after greenup for soybeans. EVI2 was the best index for predicting maize yield in non-semi-arid counties (R-2 = 0.67), but the Normalized Difference Water Index (NDWI) performed better in semi-arid counties (R-2 = 0.69), probably because the NDWI is sensitive to irrigation in semi-arid areas with low-density agriculture. NDVI and EVI2 performed equally well predicting soybean yield (R-2 = 0.69 and 0.70, respectively). In addition, EVI2 was best able to capture large negative anomalies in maize yield in 2005 (R-2 = 0.73). Overall, our results show that using crop phenology and a combination of EVI2 and NDWI have significant benefit for remote sensing-based maize and soybean yield models. (C) 2013 Elsevier B.V. All rights reserved.