Leaf area index remote sensing based on Deep Belief Network supported by simulation data

Leaf area index (LAI) is a key variable in the exchange of substance and energy between the surface and the atmosphere. Remote sensing inversion is the most effective method used to obtain the LAI in a large area. However, owing to the complexity of the spatial structure of vegetation, it is difficult to obtain LAI measurements with high stability and precision. One of the main reasons for this is the lack of information mining ability provided by remote sensing images. To fully utilize the information provided by these images, deep learning technology, with strong self-learning ability and information mining ability, was proposed in this study to retrieve the LAI. The accuracy and stability of deep learning technology largely depends on the quality, quantity, and representativeness of the samples. Given the present difficulties in producing enough high-quality samples from land surface measurements, this paper proposes the use of a radiative transfer model to simulate samples to realize a remote sensing inversion of the LAI. The PROSAIL model is used to simulate the training samples for LAI inversion. A Deep Belief Network (DBN) was used for LAI inversion from MODIS (Moderate-Resolution Imaging Spectroradiometer) data with seven spectral bands, and the estimated LAI was compared with the current MODIS LAI product (MOD15A2H), on the basis of validation by ground-measured LAI. The inversion results (Root Mean Square Error RMSE and Pearson’s correlation coefficient r) obtained by the DBN algorithm (RMSE = 0.8988, r = 0.7188) of this study and GLASS (Global LAnd Surface Satellite) algorithm (RMSE = 0.7111, r = 0.7995) showed a similar performance, and they are superior to the MODIS LAI product (RMSE = 1.0595, r = 0.6613).