LAUSR

Corn is the most widely grown crop in the U.S. and makes up a significant part of the American diet. Under the pressure of feeding a growing population, accurate and timely estimation of corn yield before the harvest is of great importance to supply chain management and regional food security. Recently, machine learning (ML) in conjunction with satellite remote sensing has been used for developing corn yield prediction models. Despite the success, a major bottleneck of training a reliable supervised ML model is the need for representative ground truth labels (e.g., yield records), which may be limited or even not available due to financial and manpower reasons. Also, due to domain shift, an ML model trained with labeled data from a label-rich region (i.e., source domain) could experience a significant performance decrease when directly applied to the region of interest (i.e., target domain). To address this issue, we proposed a Bayesian domain adversarial neural network (BDANN) for unsupervised domain adaptation (UDA) on county-level corn yield prediction. By applying adversarial learning and Bayesian inference, BDANN was trained to reduce domain shift and accurately predict corn yield by extracting domain-invariant and task-informative features from both source and target domains. Moreover, the results also demonstrated that the BDANN model generalized well on small training sets. Experiments in two ecoregions in the U.S. corn belt have shown the effectiveness of the proposed BDANN and its superiority over other state-of-the-art methods.