Crop Yield Analysis and Forecasting

Developing a crop model data assimilation framework to incorporate MODIS ET observation into a crop model to increase its forecasting skill. The ensemble data assimilation approach is employed to explore the benefit of remotely sensed actual ET to improve the simulations of the widely used Priestley-Taylor ET model while accounting for uncertainties. Link to paper

Integrating the ConvLSTM layers with the 3- Dimensional CNN (3DCNN) for a more accurate and reliable spatiotemporal feature extraction and crop yield forecasting in the US. The model is trained by using county-based historical yield data and MODIS Land Surface Temperature (LST), Surface Reflectance (SR), and Land Cover (LC) data over 1836 primary soybean growing counites in the Contiguous United States (CONUS). Link to paper

Integrating multiple Deep Neural Nets by taking advantage of Copula-based Bayesian Model Averaging for probabilistic estimate of soybean crop yield over a hundred counties across three states in the United States. Link to paper