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标题: "SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring"
原文链接: "https://arxiv.org/abs/2608.11142"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.11142"
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## Computer Science > Computer Vision and Pattern Recognition

## Title:SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

Authors:[Moti Rattan Gupta](https://arxiv.org/search/cs?searchtype=author&query=Gupta,+M+R), [Anupam Sobti](https://arxiv.org/search/cs?searchtype=author&query=Sobti,+A)

[View PDF](https://arxiv.org/pdf/2608.11142) [HTML (experimental)](https://arxiv.org/html/2608.11142v1)

> 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.

| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| --- | --- |
| Cite as: | [arXiv:2608.11142](https://arxiv.org/abs/2608.11142) \[cs.CV\] |
|  | (or [arXiv:2608.11142v1](https://arxiv.org/abs/2608.11142v1) \[cs.CV\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.11142](https://doi.org/10.48550/arXiv.2608.11142) |

## Submission history

From: Moti Rattan Gupta \[[view email](https://arxiv.org/show-email/dc70a2d4/2608.11142)\]  
**\[v1\]** Tue, 11 Aug 2026 17:01:26 UTC (28,979 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.11142) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
