使用遥感方法对作物叶绿素绘制进行全面审查:成就,局限性和未来前景
Xuan Li1,2, Bingxue Zhu1, Sijia Li1
1State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
遥感精确估计植被叶绿素含量使用可见和红边光谱带. 机器学习模型的准确性很高,但未来的研究应该提高概括性,并整合多源数据以更好地监测.
科学领域:
- 植物生理学和遥感科学.
- 生态监测和环境压力检测.
背景情况:
- 叶绿素对于光合作用至关重要,也是植物健康和压力的关键指标.
- 遥感提供了一种快速而准确的方法来估计树冠中的叶绿素含量,自2015年以来进行了广泛的研究.
研究的目的:
- 审查目前的遥感技术,以估计植被中的叶绿素含量.
- 专注于参考数据采集,光谱变量选择,模型选择和应用场景.
主要方法:
- 在可见和红边区域 (例如,530 nm,670 nm,705 nm) 分析频谱带.
- 机器学习模型 (随机森林,支持向量回归) 和辐射转移模型的评估.
- 探索结合机器学习和辐射转移的混合模型.
主要成果:
- 可见和红边光谱带为叶绿素含量提供了高的预测准确性.
- 机器学习模型实现了高性能 (R2>0.9),但面临着过度匹配的挑战.
- 辐射转移模型提供可解释性 (R2 = 0.6-0.8),而混合模型平衡准确性和通用性.
结论:
- 优化光谱带和采用先进的模型,如机器学习是准确的叶绿素估计的关键.
- 未来的研究需要改善在多样化的植被和环境中模型的通用性.
- 将多源遥感和人工智能与物理模型集成,可以提高可扩展性和实时监控.
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