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基于物理模型的LAI估计,该模型结合了空中LiDAR波形和Sentinel-2图像

Zixi Shi¹, Shuo Shi^1,2,3, Wei Gong^1,3,4

¹State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China.

Frontiers in plant science

|October 16, 2023

概括

这项研究引入了一种新的物理模型,用于使用融合的光谱和LiDAR数据来估计叶面积指数 (LAI). 数据融合显著提高了森林生态系统的LAI反转精度.

科学领域:

生态学和远程传感技术
林业和环境监测林业和环境监测

背景情况:

叶面积指数 (LAI) 对于评估森林健康和生态系统动态至关重要.
单一来源的遥感数据对准确的LAI逆转有局限性.
整合多源遥感数据,特别是主动 (LiDAR) 和被动 (光谱) 的数据,是改善LAI估计的关键趋势.

研究的目的:

利用光谱图像和LiDAR波形数据,开发和评估基于物理模型的叶面积指数 (LAI) 逆转的数据融合策略.
探索整合多来源遥感数据的有效性,以提高森林生态系统的LAI估计准确度.
通过解决波形分解和天花板/地面反射率计算的局限性来提高LAI反转的准确性.

主要方法:

基于几何光学和辐射转移 (GORT) 物理模型开发了LAI反转的融合策略.
实施了基于约束的EM波形分解方法,以提高数据处理的准确性.
提出了天花板/地面反射率的动态计算策略,以考虑空间异质性.

主要成果:

基于约束的EM波形分解提高了分解精度,平均减少了12%的RMSE.
动态天花板/地面反射率策略提高了反转精度,将相关性提高了5%-10%,R2增加了62.5%-132.1%.
使用融合光谱和LiDAR数据 (相关性=0.81,R2=0.65,RMSE=1.01) 的LAI反转显著优于单独使用任何数据源.

结论:

在拟议的物理模型策略的指导下,光谱图像和LiDAR波形数据的数据融合有效地提高了LAI反转精度.
开发的波形分解和天花板/地面反射率计算方法对于实现高精度的LAI估计至关重要.
这项研究为大规模,高精度的LAI监测提供了强有力的反转策略,支持森林生态系统评估和研究.

关键词:

在GORT模型模型中.叶片面积指数 (LAI) 叶片面积指数数据融合数据融合森林的树冠是森林的树冠.全波形激光雷达 (LiDAR) 是一种全波形雷达.物理模型物理模型远程传感是一种遥感技术.

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