Numerical Modeling and Parameter Sensitivity Analysis for Understanding Scale-Dependent Topographic Effects Governing Anisotropic Reflectance Correction of Satellite Imagery

Abstract

Anisotropic reflectance correction (ARC) of satellite imagery is required to remove multi-scale topographic effects in imagery. Commonly utilized ARC approaches have not effectively accounted for atmosphere-topographic coupling. Furthermore, it is not clear which topographic effects need to be formally accounted for. Consequently, we simulate the direct and diffuse-skylight irradiance components and formally account for multi-scale topographic effects. A sensitivity analysis was used to determine if characterization schemes can account for a collective treatment of effects, using our parameterization scheme as a basis for comparison. We found that commonly used assumptions could not account for topographic modulation in our simulations. We also found that the use of isotropic diffuse irradiance and a topographic shielding parameter also failed to characterize topographic modulation. Our results reveal that topographic effects govern irradiance variations in a synergistic way, and that issues of ARC need to be formally addressed given atmosphere-topography coupling. Collectively, our results suggest that empirical ARC methods cannot be used to effectively address topographic effects, given inadequate parameterization schemes. Characterizing and removing spectral variation from multispectral imagery will most likely require numerical modeling efforts. More research is warranted to develop/evaluate parameterization schemes that better characterize the anisotropic nature of atmosphere-topography coupling.

Ground-Based NDVI Network: Early Validation Practice with Sentinel-2 in South Korea

“…Furthermore, atmospheric effects, such as Rayleigh and aerosol scattering [ 34 ], along with the scattering ratio of the red band being approximately three times higher than that of the NIR band [ 8 ], may also contribute to errors. Additional potential causes may include subtle differences in spectra between Sentinel-2 and tower-based multispectral sensors, terrain effects [ 35 , 36 ], canopy shadowing [ 37 ], and effects due to canopy clumps [ 38 ]. Consequently, future analysis that integrate tower, drone, and satellite data must account for the shadowing effect, particularly emphasizing the need for biome-specific calibration in evergreen forests (e.g., AMD, JJ, and WD).…”