A Topographic Shadow Effect Correction (TSEC) Method for Correcting Surface Reflectance of Optical Remote Sensing Images in Rugged Terrain

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Authors: Xu Yang, Wenbin Xie, Xiaoqing Zuo, S J Guo, Daming Zhu, Yanfang Li, Jiangqi Li, Yan Jiang Luo
Cited by: 1
References: 42

Abstract

The topographic shadow effect can cause surface reflectance distortions in the shadow areas of remote sensing images, particularly in complex mountainous areas. In this study, based on the difference in solar radiation received at the surface of sunlit and shadow areas, we introduced the shadow intensity, vegetation index, and band adjustment factors, and proposed a topographic shadow effect correction (TSEC) method. The method was then tested using eight Landsat 8 OLI scenes under different illumination conditions from two different regions. The results indicate that TSEC effectively corrected the topographic shadow effect. The corrected images exhibited good visual quality without obvious shadow pixels. Importantly, TSEC retained spectral information in sunlit areas while correcting spectral distortion in shadow areas, resulting in strong agreement between spectral curves of shady and sunny slopes. The method demonstrated high stability in normalized difference vegetation index (NDVI) correction, as the difference in NDVI before and after correction was less than 0.07 for the four scenes within the Changjiang study area. Moreover, the TSEC corrected the enhanced vegetation index (EVI) effectively, reducing an initial EVI difference of over 0.35 between the shady and sunny slopes to a maximum of 0.074 for the four scenes within the Wuyi Mountain study area. Relative to four established topographic correction models, the proposed method suppresses the over-correction phenomena typical of self-shadows and minimizes under-correction in cast shadows, resulting in stable overall correction results with few outliers. The TSEC provides a simple and effective method to correct the distorted reflectance in shadow areas using only image and DEM data, which can be adapted to complex mountainous areas and for images with different illumination conditions.

Fields of Study & Subjects

Remote Sensing in Agriculture Remote-Sensing Image Classification Impact of Light on Environment and Health

Publication details

Journal / source
Remote Sensing
Publisher
MDPI AG
Volume
18
Issue
4
Pages
642–642
Publication date
19 February 2026
Language
EN