Effects of atmospheric correction on remote sensing estimation of LAI of broadleaved forest

【Objective】This study aimed to provide a scientific basis for selecting the atmospheric correction model prior to the quantitative extraction of leaf area index of broadleaved forest at a regional scale using remote sensing.【Method】 6S model, FLAASH model, and ATCOR2 model were used respectively on Landsat 8 OLI image for the atmospheric correction to analyze the correlation of these three kinds of leaf area index (LAI) of broadleaved forest and a variety of vegetation index (VI), establishing the linear and nonlinear regression model of LAI-VI. The root mean square error and correlation of validation data set of LAI predicted value (Y) and the LAI measured values (X) were calculated.【Result and conclusion】The ATCOR2 model was not suitable for building broadleaved forest LAI-VI regression model; in addition to the RVI, for FLAASH model and 6S model, LAI of broadleaved forest had a good correlation with EVI, MSAVI. Among them the power function model of LAI-MSAVI with FLAASH model yield the best goodness of fit. LAI estimation precision of FLAASH model was superior to the 6S model for broadleaved forest. With the aid of remote sensing technology to quantitatively extract vegetation physiological parameters, suitable atmospheric correction model should be selected prudently.