用对比学习分析作物NDRE轨迹，无标签识别生长期相关胁迫等级。
Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs
- 将NDRE时间序列嵌入对比学习，结合生理周期分阶段识别胁迫。
- 在未重新训练情况下跨区域测试，聚类效果优于多个基线模型。
- 输出可解释的预警图和优先级建议，适合智能农业决策系统使用。
及时检测作物胁迫对应对日益频发的干旱至关重要，但传统植被指数阈值或基于图像的聚类常无法捕捉胁迫进展，难以支持农田决策。为此，我们提出EigenCL——一种基于生理学引导的对比学习框架，从哨兵-2 NDRE时间序列中对作物胁迫进行分级，旨在为决策支持系统（DSS）提供可解释且可迁移的诊断结果。EigenCL在2020年受旱影响的爱荷华州玉米田10,000个NDRE样本上训练，于2023年内布拉斯加州田块上无重训练测试，验证结合了土壤湿度记录、美国干旱监测图和县级产量数据。模型生成四个生理上一致的胁迫簇（健康、轻度、中度、严重），显著优于K-Means、SimCLR、ProtoCLR及消融模型（轮廓系数0.748，DBI=0.35，CHI=49,624）。聚类与玉米生育期一致，严重胁迫峰值出现在吐丝期（VT-R1），该阶段已知导致产量损失；同时，模型聚类与0–14天滞后土壤湿度呈强相关（最大rho=0.72），并与干旱县的产量异常匹配。通过将NDRE动态嵌入对比学习，EigenCL实现早期胁迫预警和可解释的DSS输出（如热力图、巡查优先级、区域风险指数），突破单时相NDRE阈值局限，支持气候智慧型农业的规模化监测。
原文摘要 · Abstract (English)
Timely detection of crop stress is critical for sustaining yields under increasing drought frequency, yet conventional vegetation index thresholds or image-based clustering often fail to capture stress progression, limiting their value for farm decision-making. To address this gap, we present EigenCL, a physiology-guided contrastive learning framework that stages crop stress from Sentinel-2 NDRE trajectories, with the goal of providing interpretable and transferable stress diagnostics for decision support systems (DSS). EigenCL was trained on 10,000 maize NDRE patches from drought-affected Iowa fields in 2020 and tested on Nebraska fields in 2023 without retraining, with validation incorporating soil-moisture records, U.S. Drought Monitor maps, and county-level yield statistics. The model produced four physiologically coherent stress clusters (Healthy, Mild, Moderate, Severe), significantly outperforming baselines including K-Means, SimCLR, ProtoCLR, and an ablation model (Silhouette = 0.748, DBI = 0.35, CHI = 49,624). Clusters aligned with maize growth stages, with severe stress peaking around tasseling-silking (VT-R1), a stage known to drive yield loss; moreover, EigenCL clusters correlated with soil moisture at 0-14-day lags (rho up to 0.72) and matched yield anomalies in drought-affected counties. By embedding NDRE trajectory dynamics into contrastive learning, EigenCL enables early stress alerts and interpretable DSS outputs (e.g., heatmaps, scouting priorities, regional risk indices), extending beyond single-date NDRE thresholds and supporting scalable monitoring for climate-smart agronomy.
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