Preliminary exploration of understory leaf area index inversion based on multi-angle remote sensing.

    Soil erosion is one of the most widespread and serious environmental problems globally. Accurately obtaining understory vegetation parameters is a challenge for regional soil erosion assessment. This article introduces a method for obtaining understory vegetation Leaf Area Index (LAI) using multi-angle remote sensing techniques. Based on the Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectivity Distribution Function (BRDF) data product (MCD43A1), the 4-scale geometric optical model was used to separate forest canopy and background reflectance. Combined with the measured understory LAI, a model for the inversion of understory LAI in the study area was developed. The results demonstrated that the background reflectance showed a similar seasonal variation trend as the reflectance of adjacent grassland, and significant difference was found between the background reflectance and the corresponding pixel total reflectance. Total reflectance was fitted separately with the measured canopy LAI and understory LAI. The coefficient of determination, R-square value between total reflectance and measured canopy LAI was 0.419, while the R-square value between total reflectance and measured understory LAI was only 0.053, it was indicated that the total reflectance mainly represents the information of the canopy. Established simple inverse models for the leaf area index of understory vegetation. the correlation between understory vegetation LAI and Ratio Vegetation Index (RVI) calculated based on background reflectance was the best, with the R-square value of 0.4733, Root Mean Square Error (RMSE) of 0.55, and Mean Relative Error(MRE) of 14.62%. This research can provide a method for evaluating understory vegetation in the quantitative estimate of regional soil erosion.

KeyWords：Leaf area index; Multi-angle remote sensing; Background reflectance; Understory vegetation.