Improving corn yield prediction across the US Corn Belt by replacing air temperature with daily MODIS land surface temperature

While canopy temperature has been extensively utilized for field-level crop health assessment, the application of satellite-based land surface temperature (LST) images for corn yield modeling has been limited. Furthermore, long term yield projections in the context of climate change have primarily employed air temperature (Tair) and precipitation, which may inadequately reflect crop stress. This study assessed potential benefits of satellite-derived LST for predicting annual corn yield across the US Corn Belt from 2010 to 2016. A novel killing degree day metric (LST KDD) was computed with daily LST images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and compared to the typically used Tair-based metric (Tair KDD). Our findings provide strong evidence that LST KDD is capable of predicting annual corn yield with less error than Tair KDD (R-2/RMSE of 0.65/15.3 Bu/Acre vs. 0.56/17.2 Bu/Acre). Even while adjusting for seasonal temperature and precipitation parameters, the R-2 and RMSE of the LST model were approximately 9% higher and 2.0 Bu/Acre lower than the Tair model, respectively. The superior performance of LST can be attributed to its ability to better incorporate evaporative cooling and water stress. We conclude that MODIS LST can improve yield forecasts several months prior to harvest, especially during extremely warm and dry growing seasons. Furthermore, the better performance of LST models over Tair and precipitation models suggest that subsequent long term yield projections should consider additional factors indicative of water stress.