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 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/11/11/2025 |
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| author | 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 Peter M. Atkinson |
| author_facet | 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 Peter M. Atkinson |
| author_sort | Seyed Kazem Alavipanah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T18:13:43Z |
| format | Article |
| id | doaj.art-5454627371224ca49209b41121c1894e |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-09T18:13:43Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-5454627371224ca49209b41121c1894e2023-11-24T08:55:32ZengMDPI AGLand2073-445X2022-11-011111202510.3390/land11112025The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A ReviewSeyed Kazem Alavipanah0Mohammad Karimi Firozjaei1Amir Sedighi2Solmaz Fathololoumi3Saeid Zare Naghadehi4Samiraalsadat Saleh5Maryam Naghdizadegan6Zinat Gomeh7Jamal Jokar Arsanjani8Mohsen Makki9Salman Qureshi10Qihao Weng11Dagmar Haase12Biswajeet Pradhan13Asim Biswas14Peter M. Atkinson15Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, IranSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaDepartment of Civil, Environmental and Geomatics Engineering, College of Engineering and Computer Science, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USADepartment of Geography and Environmental Science, North Texas University, Denton, TX 76203, USADepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran 14178-53933, IranGeoinformatics Research Group, Department of Planning and Development, Aalborg University Copenhagen, A.C. Meyers Vænge 15, DK-2450 Copenhagen, DenmarkDepartment of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, GermanyDepartment of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, GermanyDepartment of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, 11 Yuk Choi Road Hung Hom, Kowloon, Hong Kong, ChinaDepartment of Geography, Humboldt University Berlin, Unter den Linden 6, 10099 Berlin, GermanyCenter 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, AustraliaSchool of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, CanadaLancaster Environment Center, Faculty of Science and Technology, Lancaster University, Bailrigg, Lancaster LA1 4YR, UKIn 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.https://www.mdpi.com/2073-445X/11/11/2025shadowsurface biophysical variablesshadow detectionde-shadowingremote sensing |
| spellingShingle | 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 Peter M. Atkinson The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review Land shadow surface biophysical variables shadow detection de-shadowing remote sensing |
| title | The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review |
| title_full | The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review |
| title_fullStr | The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review |
| title_full_unstemmed | The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review |
| title_short | The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review |
| title_sort | shadow effect on surface biophysical variables derived from remote sensing a review |
| topic | shadow surface biophysical variables shadow detection de-shadowing remote sensing |
| url | https://www.mdpi.com/2073-445X/11/11/2025 |
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