Algorithm for leaf area index inversion in the Great Xing'an Mountains using MISR data and spatial scaling for the validation

Leaf Area Index (LAI) is an important parameter of vegetation canopy structure in the research of climate and ecology. Remote sensing technology provides an effective method for rapid acquisition of large-area leaf area index. The Great Xing'an Mountains are an important ecological function area of China, where the present study was conducted. According to the different forest characteristics, we used 4-scale geometrical optics model based on physical process. Simultaneously, we used the multi-perspectives MISR remote sensing data to inverse the leaf area index of this region. Geometrical optics model characterized by parameters have physical significance which considers the hot-spot effect of the ground reflection, and modelling inversion process is independent on sample data, suitable for inversion in a large area. The MISR remote sensing data provide multiple perspectives in the same region, which effectively address the question that LAI can only be observed at a single angle and the question of low level saturation point in the LAI function relationship. Because the scale of ground validation data cannot meet the spatial resolution requirement of the MISR data, TM data were used to scale transformation for plot-measured leaf area index data. We analyzed the heterogeneity of LAI in different slopes and discussed the validation data rationality at different spatial resolutions. Our study shows that the validation data at a 600m spatial resolution can obtain optimal inversion result of MISR data, and at such a resolution, the change of LAI with spatial scale tends to stabilize and successfully avoids the error caused by the geometric registration of the two remote sensing data. The results of our study showed that: 4-scale geometry model is suitable for LAI inversion in the Great Xing'an Mountains, MISR-inversed mean absolute error of LAI is 25.6% and RMSE (the root-mean-square error) is 0.622. This research provides foundation for rapid, quantitative inversion of LAI in the Great Xing'an Mountains.