将NDRE轨迹嵌入对比学习以实现无标签、感知生理特征的作物胁迫分期及DSS输出
Embedding NDRE Trajectories into Contrastive Learning for Label-Free, Physiology-Aware Crop-Stress Staging and DSS Outputs
中文总结 AI 辅助
该研究提出EigenCL框架，将NDRE轨迹嵌入对比学习实现无标签作物胁迫分期，性能优于基线模型，可提供可解释DSS输出，支持气候智能型农艺监测。
中文摘要 AI 辅助
及时检测作物胁迫对于在干旱频率增加的情况下维持产量至关重要，但传统的植被指数阈值或基于图像的聚类往往无法捕捉胁迫的进展，限制了其在农场决策中的应用价值。为解决这一差距，我们提出了EigenCL，这是一种受生理特征引导的对比学习框架，可通过Sentinel-2 NDRE轨迹对作物胁迫进行分期，目标是为决策支持系统（DSS）提供可解释且可迁移的胁迫诊断结果。EigenCL在2020年受干旱影响的爱荷华州农田的10000个玉米NDRE斑块上进行训练，并在2023年内布拉斯加州的农田上进行测试，未进行重新训练，验证过程结合了土壤湿度记录、美国干旱监测图以及县级产量统计数据。该模型生成了四个生理上一致的胁迫簇（健康、轻度、中度、重度），其性能显著优于包括K-Means、SimCLR、ProtoCLR在内的基线模型及一个消融模型（轮廓系数Silhouette=0.748，戴维森堡丁指数DBI=0.35，卡林斯基-哈拉巴斯指数CHI=49624）。这些簇与玉米生长阶段相吻合，其中重度胁迫在抽雄-吐丝期（VT-R1）达到峰值，该阶段是已知会导致产量损失的关键时期；此外，EigenCL生成的簇与滞后0-14天的土壤湿度相关（相关系数rho最高达0.72），并与受干旱影响的县的产量异常情况相匹配。通过将NDRE轨迹动态嵌入对比学习，EigenCL能够实现早期胁迫预警及可解释的DSS输出（如热图、巡查优先级、区域风险指数），超越了单一日期NDRE阈值的局限，为气候智能型农艺学提供了可扩展的监测支持。
英文摘要
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.
