SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

2026-08-11

AI Summary

This work addresses the challenge of agricultural monitoring under complex temporal, phenological, and climatic dynamics, where existing approaches predominantly rely on optical imagery or multimodal data. The study proposes the first self-supervised learning framework that operates solely on SAR intensity images, introducing an enhanced temporal pretraining task integrated with a tailored masking strategy and curriculum learning to effectively capture phenological features for crop type identification without any optical data. Evaluated on the SICKLE benchmark, the method achieves an IoU of 84.9%, substantially outperforming optical-based baselines by 15.3 percentage points and surpassing current SAR-only approaches by 2.2 percentage points, thereby overcoming a critical bottleneck in all-weather representation learning for agricultural remote sensing.

Abstract

Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection. Existing multimodal remote sensing foundation models including TerraMind and CopernicusFM learn SAR representations by grounding them in optical imagery using joint encoding and contrastive learning techniques, while SAR-specific foundation models such as SAR-JEPA, SARMAE, and SAR-W-MixMAE primarily focus on target detection, flood mapping, and land cover classification applications. Recent work has introduced phenology inspired temporal pretext tasks with optical imagery which has shown strong performance on agricultural downstream tasks. In this work, we propose the first self-supervised learning pipeline focused on using only SAR intensity imagery for agricultural applications. We improve the temporal pretext tasks through masking and curriculum learning to enhance the pretraining pipeline's ability to capture phenological features from SAR. On the SICKLE benchmark, our final model achieves 84.9% IoU on crop type mapping, outperforming optical baselines (by 15.3 pt) and existing SAR baselines (by 2.2 pt), demonstrating the effectiveness of our proposed pipeline for pretraining SAR intensity encoders for agricultural monitoring.

Problem

• SAR intensity
• agricultural monitoring
• self-supervised learning
• phenological features
• crop type mapping

Innovation

• SAR intensity
• self-supervised learning
• phenological modeling
• agricultural monitoring
• curriculum learning