New Airborne Multi-angle High Resolution Sensor AMTIS LAI Inversion Based on Neural Network

Abstract

Leaf area index (LAI) is an important biophysical parameter, and Remote Sensing provides the possibility for the LAI retrieval over large area. Model based inversion is one of the main LAI retrieval methods, and the multi-angle data are the important data sets. However, the general model-fitting algorithm is time consuming in LAI inversion. In this paper, we proposed a kernel-driven model and Neural Network based LAI inversion algorithm to speed the process. The data obtained by the new Airborne Multi-angle Thermal/Visible Imaging System (AMTIS) is synchronous and has higher resolution. Compared with the low-resolution multi-angle data such as MISR and MODIS, it has a resolution as high as 1.36 meter. Using the kernel-driven model, the BRF was reconstructed from the AMTIS data. On the other hand, a 3-dimension radiative transfer model and the measured parameters were used to model the BRF. Then LAI was inversed based on the neural network. Synchronous ground-based measurements of LAI for the wheat were taken in Shunyi to validate our method. Some conclusions were got from the study: (1) LAI can be retrieved successfully using the high-resolution multi-angle data based on neural network; (2) Based on the Neural Network and the kernel-driven model, the inversion rate can be improved; (3) By adjusting the soil moisture classification, the inversion precision can be improved.