联合MODIS与MISR遥感数据估算叶面积指数

Abstract

多光谱传感器MODIS与多角度传感器MISR同时搭载在美国EOS观测计划的Terra卫星上,不同的观测方式使得两个传感器的数据组合后形成互补的多光谱多角度观测数据集,为地表参数的遥感估算提供了更多的对地观测信息.该文通过研究组合MODIS与MISR两种观测数据估算陆地表面植被覆盖区域叶面积指数的方法,发展了在物理模型反演的框架内引入基于伴随模型和信赖域优化的反演模式,改进了叶面积指数的遥感估算效果,提高了模型反演的运算速度.对试验区反演结果的验证说明融合两种数据源可以提高叶而积指数的估算精度.基于伴随模型和信赖域优化的地表参数反演方法,为应用于大范围遥感图像数据的模型反演提供了一种有效的途径.

Moderate-resolution imaging spectrometer (MODIS) and multi-angle imaging spectroradiometer (MISR) are two important sensors on TERRA satellite. The authors can have more spectral and multi-angular observations on the land surface objects by combining these two datasets. In the present paper, both MODIS and MISR observations were combined to estimate leaf area index (LAI) of land surface. The adjoining model and trust-region optimal algorithm were introduced into the framework of physical model inversion to speed up the running of the model inversion algorithm. And the algorithm allows the prior knowledge on the retrieved parameters to be input into the inversion procedure. The uncertainty and sensitivity matrix (USM) based analysis is helpful for selecting the observed data subset with more information and less noise to retrieve LAI. The measured LAI in situ and estimated LAI from ETM data were scaling-up to MODIS/MISR LAI product scale, and were taken as the ground truth to evaluate the new approach. The result suggests that combining two sensors datasets can improve the accuracy of LAI estimation, and the developed inversion method in this paper can be applied to the large area remote sensed image data effectively.