The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

In remote sensing (RS), shadows play an important role, commonly affecting the quality of data recorded by sensors. It is, therefore, utmost importance to detect and model shadow effect in RS as well information that is obtained from them, particularly when are be used further environmental studies. Shadows can generally categorized into four types based on their sources: cloud shadows, topographic urban a combination these. The main objective this study was review recent literature sensing. A systematic employed evaluate studies published since 1975. Various demonstrated influence significantly estimation various properties These include vegetation, impervious surfaces, water, snow, albedo, soil moisture, evapotranspiration, land surface temperature. should noted also affect outputs processes such spectral indices, heat islands, use/cover maps. extracted function sensor–target–solar geometry, overpass time, spatial resolution satellite sensor imagery. Meanwhile, modeling applying appropriate strategies reduce its impacts biophysical variables associated with many challenges. However, some have made use valuable them. An overview proposed methods for identifying removing presented.