Final published version
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Research output: Contribution to Journal/Magazine › Review article › peer-review
Research output: Contribution to Journal/Magazine › Review article › peer-review
}
TY - JOUR
T1 - The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing
T2 - A Review
AU - Alavipanah, Seyed Kazem
AU - Karimi Firozjaei, Mohammad
AU - Sedighi, Amir
AU - Fathololoumi, Solmaz
AU - Zare Naghadehi, Saeid
AU - Saleh, Samiraalsadat
AU - Naghdizadegan, Maryam
AU - Gomeh, Zinat
AU - Arsanjani, Jamal Jokar
AU - Makki, Mohsen
AU - Qureshi, Salman
AU - Weng, Qihao
AU - Haase, Dagmar
AU - Pradhan, Biswajeet
AU - Biswas, Asim
AU - M. Atkinson, Peter
PY - 2022/11/12
Y1 - 2022/11/12
N2 - 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.
AB - 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.
KW - shadow
KW - surface biophysical variables
KW - shadow detection
KW - de-shadowing
KW - remote sensing
U2 - 10.3390/land11112025
DO - 10.3390/land11112025
M3 - Review article
VL - 11
JO - Land
JF - Land
SN - 2073-445X
IS - 11
M1 - 2025
ER -