LAI Inversion from MODIS Data Using Deep Belief Network (DBN)

Leaf area index (LAI) is a key variable to describe the energy, mass and momentum exchange between the surface and the planetary boundary layer. Remote sensing technology can quickly acquire the change of LAI on a large scale and provide support for the study of ecological environment and climate change. Due to the complexity of the surface and atmospheric structure, the inversion of LAI is still facing great difficulties. Limited by the information provided by remote sensing sensors and the ability of information mining by traditional methods, the accuracy of LAI inversion is not high. In order to improve the accuracy of LAI inversion, a method of LAI inversion based on deep belief network (DBN) is proposed in this paper and is applied to the MODIS data. Results are verified by the ground measurements performed within the framework of the ImagineS (Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels) project, and shows that the LAI obtained by the DBN algorithm (hereinafter referred to as DBN LAI) can reach a high precision, and performs better than current MODIS LAI product (MCD15A2).