High dynamic range (HDR) variations in satellite optical imagery arise from extreme differences in surface reflectance and illumination conditions. Conventional satellite NeRF frameworks are typically trained on tone-mapped or radiometrically enhanced images, where nonlinear preprocessing alters the physical relationship between measured pixel values and true scene radiance. This leads to biased photometric optimization and loss of geometric fidelity, especially under strong illumination contrasts. To address these limitations, we propose an HDR-consistent learning framework that integrates RawNeRF-style radiance supervision with shadow regularization. The method trains directly on raw satellite imagery using a logarithmic, tone mapping–aware loss that preserves linear radiance and stabilizes optimization under high dynamic range conditions. In parallel, a soft shadow regularization constrains network-predicted shadows using geometric cues derived from solar ray casting, promoting physically consistent irradiance decomposition. Experiments on four AOIs from the DFC2019 dataset demonstrate that HDR-aware radiance learning significantly improves DSM accuracy by maintaining linear radiometric consistency. The proposed shadow regularization also improves geometric consistency in structure-dominated urban scenes, although its effect is limited in vegetation-dominant areas where shadow cues are less informative. Although performance gains are smaller in vegetation-dominant areas, the results confirm that combining HDR radiance learning with geometric shadow regularization yields more radiometrically consistent and geometrically accurate 3D reconstruction from satellite imagery.