Evaluation of Topographic Correction Models Based on 3-D Radiative Transfer Simulation

The timely and accurate assessment of changes in mountain vegetation biomass and other parameters is of great importance to mountain ecosystem conservation. With the rapid development of remote sensing technology, hyperspectral remote sensing images have facilitated the large-scale and long-time series monitoring of environmental changes in mountainous areas. However, topographic effects cause remote sensing images of mountainous areas to be prone to spectral variations within the same land cover and spectral confusion among different land covers. This phenomenon seriously affects the accuracy of remote sensing inversions and hinders the development and application of remote sensing in mountainous areas. Numerous scholars have established various topographic correction models (TCMs) to eliminate the influence of topographic effects. Comparative evaluation of the performance of different TCMs allows us to better understand their characteristics. Most previous evaluation studies have directly applied in remote sensing images, which were limited by the changing conditions of the study area. Therefore, this letter used computer simulations to controllably evaluate six popular TCMs on hyperspectral images. The results showed that their performance varied with the spectral band, and overall, the best performance was achieved by the C correction model, followed by the sun-canopy-sensor (SCS) + C model. This letter provides a basis for the optimal selection of TCMs in complex terrains.

Automated summary

This letter discusses the importance of timely and accurate assessment of changes in mountain vegetation biomass and other parameters for mountain ecosystem conservation. The study evaluates different topographic correction models (TCMs) on hyperspectral images to provide insights for selecting the optimal TCMs.