The radiation quality of images is severely affected by variations in topography especially in complex mountain areas. Thus

topographic correction is a necessary pre-processing step for remote sensing image radiative correction. However

over-correction problems were easily found in the commonly used topographic correction method in steep mountain areas. We proposed a semi-empirical topographic correction method for steep mountain terrain to overcome the over-correction problems; it is the simple topographic correction method using estimation of diffuse light (SCEDIL). The SCEDIL model estimates the diffuse fraction through finding the horizontal sunlight and shadow pixels based on images and Digital Elevation Model (DEM) data. The three radiation components (direct

diffuse

and terrain irradiance) focused on the impact of the solar irradiance

reaching an inclined surface to recover the actual land surface reflectance. We consider that the solar direct radiation is the main source of incident radiation for horizontal sunlight pixels

and the incident radiation of horizontal shadow pixels come from diffuse and terrain irradiance. Therefore

the diffuse radiation can be estimated by the mean reflectance of horizontal sunlit and shadow pixels. The estimation of the surface actual reflectance is achieved using a two-step procedure. First

the atmospheric correction model (such as 6S or MODTRAN model) was used to calculate the diffuse fraction and irradiance components of horizontal surfaces. Second

SCEDIL was used to remove terrain effects. The GF-1 and Landsat ETM+ images were used to evaluate different topographic corrections (SCEDIL

C

and SCS+C models). Four validation methods were employed to assess the performance under different illumination conditions

as below: (a) visual comparison for different terrain correction models

(b) changes in the correlation coefficient between the incidence angle and the spectral bands

(c) changes in the Standard Deviation (SD) and

mean

of the reflectance of each spectral band

and (d) land cover classification accuracy. The results show that: (1) SCEDIL

C

and SCS+C corrections performed effectively for the slightly rough mountain area (average slopes

<

26°

average elevation

<

600 m). However

SCEDIL-correction produced better and smoother corrected images than SCS+C and C methods for the steep mountain area; the average slope is larger than 26°

and the average elevation is more than 700 m; (2) the SCEDIL method produced the lowest correlation coefficient between the incidence angle and the spectral bands for steep mountain

(3)SCEDIL-correction maintained more closer mean and lesser reduction in SD to original image bands than C-correction and SCS+C-correction; (4) SCEDIL-corrected images have the highest overall accuracy of classification and highest homogeneity within each land cover class using the Support Vector Machine classification method. Therefore

SCEDIL-correction was robust in terms of different terrains

and especially the steep region images in this study.