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

Abstract

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

Land Cover Change and Food Security in Central Sumba: Challenges and Opportunities in the Decentralization Era in Indonesia

“…Topographic correction is an important step in processing remote sensing data that takes into account the varying elevation of terrain. The shadow effect from hills and mountains can cause errors in the data, which can affect the accuracy of the analysis [35,36]. The Illumination Condition and Rotation model algorithm is a widely used method for topographic correction [36,37].…”

Detection and Quantification of Arnica montana L. Inflorescences in Grassland Ecosystems Using Convolutional Neural Networks and Drone-Based Remote Sensing

“…The photogrammetry software "Pix4D Mapper 4.7" was used to obtain average GSD values for each aerial survey as part of the orthomosaic building process. In computer vision technology and RS, every pixel matters, and shadows can affect colors and textures [44,[76][77][78]. To consider this factor, the take off time was used to obtain a retrospectively calculated value for shadow length.…”

To Ignore or Not: Understanding the Influence of Hillshade

“…In studies focusing on understanding surface radiation distribution [15], biophysical variables [16], vegetation growth, solar energy utilization [17], glacier estimation [18], and related areas, the influence of hillshade becomes a crucial consideration. This calls for extensive and comprehensive attention, especially since hillshade has a significant influence on the spatial distribution of surface evapotranspiration [19], surface temperature [20], and soil moisture [16] in mountainous regions. Currently, a large number of radiation transfer models tailored to mountainous areas have been developed for quantitative estimation of the radiation transfer process [15], [21], [22].…”