Selecting an optimal atmospheric correction model for above-ground forest biomass estimation

Atmospheric correction is an important image-preprocessing step in which negative effects need to be minimized and convert to surface reflectance from optical remove sensing data. Our object is to select an optimal atmospheric correction model for above-ground forest biomass (AGB) estimation based on remote sensing approach. Three selected atmospheric correction models were investigated namely 1) Dark Object Subtraction (DOS), 2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and 3) the Second Simulation of Satellite Signal in the Solar Spectrum (6S); then they were compared with Top Of Atmospheric (TOA) reflectance. Gongju and Sejong region, South Korea was chosen to estimate AGB by using the k-Nearest Neighbor (kNN) algorithm and five Landsat ETM+ images. As a result, the 6S model provided the best RMSE's, followed by FLAASH, DOS and TOA. More importantly, a significant improvement of RMSE by 6S was found with images when the study site had higher total water vapor and temperature levels.