DISCERN: Leveraging Knowledge Distillation to Generate High Resolution Soil Moisture Estimation from Coarse Satellite Data

Accurate estimation of soil moisture is crucial for efficient agricultural management and environmental monitoring. However, the task of predicting soil moisture levels becomes challenging in regions with limited data availability. In this study, we propose a knowledge distillation-based deep learning approach to enhance soil moisture prediction with machine learning apporach using the low resolution but wide coverage soil moisture Active Passive (SMAP) satellite data.Our framework leverages the knowledge distillation, where a high-capacity teacehr model (VGG13) which is pre-traineed on a large dataset (SMAP) and a lightweight student model (ResNet8) which is then trained on sensor-based highly accurate but extremely sparse station data. The student model benefits from the distilled knowledge of the teacher model, acquiring a deeper understanding of the underlying patterns and relationships in the data.The space-efficient student model significantly reduces the inference time with high prediction accuracy and demonstrates the potential benefit to agricultural management, water resource planning, and ecological studies by providing accurate and reliable soil moisture predictions in data-scarce regions. Our findings reveal how to identify performant settings for achieving the best trade-off between accuracy and model complexity.