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

Seyed Kazem Alavipanah (), Mohammad Karimi Firozjaei, Amir Sedighi, Solmaz Fathololoumi, Saeid Zare Naghadehi, Samiraalsadat Saleh, Maryam Naghdizadegan, Zinat Gomeh, Jamal Jokar Arsanjani, Mohsen Makki, Salman Qureshi, Qihao Weng, Dagmar Haase, Biswajeet Pradhan, Asim Biswas and Peter M. Atkinson
Additional contact information
Seyed Kazem Alavipanah: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Mohammad Karimi Firozjaei: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Amir Sedighi: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Solmaz Fathololoumi: School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Saeid Zare Naghadehi: Department of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA
Samiraalsadat Saleh: Department of Geography and Environmental Science, North Texas University, Denton, TX 76203, USA
Maryam Naghdizadegan: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Zinat Gomeh: Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, Iran
Jamal Jokar Arsanjani: Geoinformatics Research Group, Department of Planning and Development, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, Denmark
Mohsen Makki: Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
Salman Qureshi: Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
Qihao Weng: Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, 11 Yuk Choi Road Hung Hom, Kowloon, Hong Kong, China
Dagmar Haase: Department of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, Germany
Biswajeet Pradhan: Center for Advanced Modeling and Geospatial Information Systema (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, Building 11, 81 Broadway, Ultimo, NSW 2007, Australia
Asim Biswas: School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Peter M. Atkinson: Lancaster Environment Center, Faculty of Science and Technology, Lancaster University, Bailrigg, Lancaster LA1 4YR, UK

Land, 2022, vol. 11, issue 11, 1-30

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.

Keywords: shadow; surface biophysical variables; shadow detection; de-shadowing; remote sensing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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