本文面向农作物倒伏“灾前-灾后”一体化监测需求,针对现有方法自动化水平低、缺乏时空协同机制及系统性框架等问题,提出了一种基于农作物生长标准曲线的倒伏常态化自动监测方法——StandardCurve-iForest-RF。该方法利用Sentinel-2时间序列数据,通过Soft-DTW算法构建不受年度波动干扰的作物生长标准曲线,作为稳健的监测基准；结合孤立森林算法计算多特征异常得分累计值,并引入时空联合判定机制,有效识别真实倒伏事件,抑制因云噪声等导致的误判；最终实现从倒伏动态发现到范围精细检测的全自动化流程。在黑龙江省大庆市肇源县薄荷台乡的倒伏案例中,该方法成功检测出2020年9月15日的倒伏事件,整体精度达0.8036,Kappa系数为0.5923。结果表明,StandardCurve-iForest-RF方法具有自动化程度高、准确性好的优势,可为农业灾害监测与应急管理提供可靠的技术支持。

Objective: Crop lodging poses a significant threat to agricultural productivity and food security, yet existing monitoring approaches often suffer from low automation, insufficient integration of pre- and post-disaster data, and a lack of systematic spatiotemporal coordination. To overcome these limitations, we developed an automated framework, StandardCurve-iForest-RF, which aims to establish a resilient crop growth baseline, distinguish true lodging from noise, and enable precise spatiotemporal mapping for disaster management. Method: The approach utilizes time-series Sentinel-2 satellite data to construct a Crop Growth Standard Curve (CGSC) for the target crop. The Soft Dynamic Time Warping (Soft-DTW) algorithm is employed to create this curve, which serves as a resilient reference model capable of accommodating inter-annual climatic variations. To detect lodging, the method calculates cumulative multi-feature anomaly scores by comparing post-disaster satellite observations against the pre-established standard curve. An Isolation Forest (iForest) algorithm is applied for initial anomaly detection across multiple spectral features. Subsequently, a spatiotemporal joint decision mechanism is implemented to refine the results, effectively suppressing false alarms caused by persistent cloud cover, cloud shadows, and other environmental noise. Finally, a Random Forest (RF) classifier is used to accurately map the spatial extent and precise boundaries of the lodged areas, completing the fully automated workflow from dynamic detection to precise mapping. Result: The method was validated using a case study of a lodging event that occurred on September 15, 2020, in Bohetai Township, Zhaoyuan County, Daqing City, Heilongjiang Province. It successfully identified the lodging event, demonstrating its effectiveness in distinguishing actual crop damage from noise. The overall detection accuracy reached 80.36%, with a Kappa coefficient of 0.60, confirming a substantial agreement between the automated detection results and ground reference data. The results clearly showed that the integrated use of the standard curve, the anomaly scoring mechanism, and the spatiotemporal decision rules significantly enhanced the reliability of lodging identification and minimized false positives. The entire process, from data processing to the final generation of the lodging map, was executed automatically without manual intervention. Conclusion: The StandardCurve-iForest-RF framework presents a significant advancement in automated crop disaster monitoring. Its core innovation lies in the construction of a resilient growth standard curve and a sophisticated spatiotemporal analysis pipeline that effectively differentiates true lodging from interference. The successful application in a real-world case study confirms the method"s practical utility and accuracy. This framework provides a valuable tool for agricultural departments and emergency management agencies, enabling rapid assessment of crop damage extent and supporting timely disaster response and loss estimation. The methodology is adaptable and holds promise for application in other regions and for monitoring other types of abrupt agricultural disasters.