Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt

Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups-time-series data and constant data. Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either convolutional neural network (CNN) or recurrent neural network (RNN). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data, soil property data, and the model outputs yield. We experimented with the model in U.S. Corn Belt states, and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops such as soybean and winter wheat to assist agricultural decision-making.