Li Xuan, Zhu Bingxue, Li Sijia, Liu Lushi, Song Kaishan, Liu Jiping
State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
College of Tourism and Geography, Jilin Normal University, Siping 136000, China.
Sensors (Basel). 2025 Apr 8;25(8):2345. doi: 10.3390/s25082345.
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R = 0.6-0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities.
叶绿素吸收光能并将其转化为化学能，使其成为监测植被健康、检测环境压力和预测生理状态的关键生化参数。准确、快速地估算冠层叶绿素含量对于评估植被动态、生态变化和生长模式至关重要。自2015年以来，遥感技术已成为监测植被叶绿素含量不可或缺的工具，每年发表超过50篇研究论文，形成了大量的案例研究。本综述讨论了目前用于估算植被叶绿素含量的遥感技术，重点关注四个关键方面：参考数据集的获取、最佳光谱变量的识别、估算模型的选择以及应用场景的分析。结果表明，可见光和红边区域的光谱波段（如530纳米、670纳米和705纳米）具有较高的预测精度。随机森林和支持向量回归等机器学习方法表现出优异的性能，决定系数（R）通常超过0.9，不过过拟合仍是一个问题。虽然辐射传输模型的精度略低（R = 0.6 - 0.8），但它们具有更强的可解释性。将机器学习与辐射传输相结合的混合模型在平衡精度和通用性方面显示出强大的潜力。未来的研究应提高模型对不同植被类型和环境条件的通用性，并整合多源遥感数据以提高空间和时间分辨率。将物理模型与人工智能等数据处理方法相结合，可以提高可扩展性、成本效益和实时监测能力。
