Yield Estimation from SAR Data Using Patch-Based Deep Learning and Machine Learning Techniques

This study demonstrates how the availability of frequent Synthetic Aperture Radar (SAR) observations has transformed crop yield prediction, a critical component of food security and agricultural practices. SAR observations along with the climatic variables are integrated into an advanced deep learning technique for predicting crop yield. Capitalizing on the unique advantages of high-resolution SAR images, including consistent acquisition schedule, and not being affected by cloud cover and variations between day and night, this research explores new potential in agriculture. Deep learning due to its ability to discern both spatial and temporal relationships within SAR data, captures the salient features from SAR observations to predict the yield of Michigan’s non-irrigated Corn, Soybean, and Winter Wheat.We employed advanced deep learning and established machine learning techniques including patch-based 3D Convolutional Neural Networks (3D-CNNs), Random Forest, Support Vector Machine, and XGBoost to significantly improve the accuracy of yield estimation.Spanning eight years from 2016 to 2023, our analysis underscores the exceptional potential of VH channel of Sentinel-1 SAR data for near accurate yield prediction. Among the methods tested, XGBoost consistently surpassed others in crop yield estimating accuracy, particularly in scenarios with limited reference data. Patch-based 3D-CNNs also demonstrated a remarkable ability to approximate XGBoost’s performance, albeit with a streamlined set of input features. Our study further illuminates the delicate balance required in selecting SAR data resolution, demonstrating the need for careful compromise between reducing noise and preserving crucial data intricacies. Notably, our predictive models showcased formidable precision, predicting yields with a mere 7.5% margin of error a full month prior to harvest. These compelling findings signal the need for continued innovation and integration of deep learning technologies, calling for the enrichment of yield datasets to realize more comprehensive and pinpoint-accurate yield predictions.