遥感植被指数时间序列预测方法及其在农业与生态监测中的应用

Remote sensing vegetation index time-series prediction methods and their applications in agricultural and ecological monitoring

关键词:  植被指数  时间序列预测  深度学习  农业监测  误差评价

Key words:  vegetation index  time-series prediction  deep learning  agricultural monitoring  error evaluation

摘要:

【目的】遥感植被指数时间序列预测是遥感时序分析由历史监测走向实时应用的重要基础，对物候反演、农情监测和生态预警等任务具有重要支撑作用。【方法】文章采用文献调研与归纳梳理的方法，系统回顾了遥感植被指数时间序列预测算法及应用的研究进展；分析了数据源差异、观测噪声、缺测重建与滤波平滑等基础环节对预测建模的影响；厘清了遥感植被指数时间序列预测算法的发展脉络，讨论了不同方法在不同预测场景下的适用性；归纳了预测结果的典型应用方向，并概述了面向数值精度、时序结构保持和任务适用性的综合评价框架。【结果】遥感植被指数时间序列预测已形成统计模型、传统机器学习、深度学习与机理约束混合建模并行发展的格局；预测结果可支撑缺测补全、物候提取、农情监测、生态预警和变化识别；其评价应由单一数值误差评价扩展为兼顾数值精度、结构保持、任务适用性和不确定性的综合判断。【结论】遥感植被指数时间序列预测已发展为受数据基础、模型算法、评价体系与实际应用共同约束的综合研究方向。未来需重点攻克高质量输入序列构建、长时距稳定预测、时空异质性处理和任务导向评价等方面。

Abstract:

[Purpose] Remote sensing vegetation index time-series prediction is an important foundation for shifting remote sensing time-series analysis from retrospective monitoring toward real-time and prospective applications, and it provides essential support for phenology retrieval, agricultural monitoring, and ecological early warning. [Method] Based on a literature review and systematic synthesis, this article reviewed the research progress in algorithms and applications for remote sensing vegetation index time-series prediction; analyzed the effects of data-source differences, observation noise, missing-value reconstruction, and filtering and smoothing on predictive modeling; clarified the development trajectory of prediction algorithms; discussed the applicability of different methods under different forecasting scenarios; summarized typical application directions of prediction results; and outlined an integrated evaluation framework oriented toward numerical accuracy, temporal-structure preservation, and task applicability. [Result] The review showed that remote sensing vegetation index time-series prediction formed a parallel development pattern involving statistical models, traditional machine learning, deep learning, and mechanism-constrained hybrid modeling. Prediction results could support gap filling, phenology extraction, agricultural monitoring, ecological early warning, and change identification. The evaluation of predicted sequences needed to shift from single numerical error metrics to an integrated assessment of numerical accuracy, structural preservation, task applicability, and uncertainty. [Conclusion] Remote sensing vegetation index time-series prediction is an integrated research field constrained by data foundations, modeling algorithms, evaluation systems, and practical applications. Future studies should focus on high-quality input sequence construction, stable long-horizon prediction, treatment of spatiotemporal heterogeneity, and task-oriented evaluation.