SCSCTS: An improved SCS+C topographic correction model with shadow compensation for mountainous regions.

Area of Science:

• Remote Sensing
• Geospatial Analysis
• Environmental Monitoring

Background:

• Topographic effects cause significant distortions in surface reflectance and vegetation indices in mountainous areas.
• These distortions lead to systematic biases in ecological and biophysical parameter retrievals from satellite data.
• Accurate topographic correction is crucial for reliable remote sensing applications in complex terrain.

Purpose of the Study:

• To propose and evaluate an illumination-stratified topographic correction method (SCSCTS) for mountainous regions.
• To improve the accuracy of surface reflectance and vegetation index retrievals by addressing topographic distortions.
• To enhance the robustness of remote sensing parameter retrievals in complex mountainous environments.

Main Methods:

• Developed the illumination-stratified topographic correction method (SCSCTS), extending the SCS+C model.
• Incorporated an illumination-based zonal radiative compensation scheme.
• Accounted for terrain occlusion and adjacent terrain reflection.
• Evaluated SCSCTS using Landsat 8 data in a karst mountainous region, comparing it with SCS+C and PLC models.

Main Results:

• SCSCTS reduced near-infrared reflectance dependence on topographic illumination by up to 99.4% under low solar elevation.
• Anomalous reflectance pixels decreased by 72.0% in spring and 93.9% in winter.
• Terrain-related correlations for EVI, NIRv, and RDVI were reduced by 78.6% in spring and 86.1% in winter.
• SCSCTS showed more stable performance than PLC under strong shadow conditions.

Conclusions:

• The SCSCTS method improves radiometric consistency for reflectance and vegetation indices.
• It effectively suppresses overcorrection effects and enhances retrieval robustness in mountainous areas.
• SCSCTS offers a physically based and operationally feasible solution for topographic correction in remote sensing.