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

Alavipanah, SK; Karimi Firozjaei, M; Sedighi, A; Fathololoumi, S; Zare Naghadehi, S; Saleh, S; Naghdizadegan, M; Gomeh, Z; Arsanjani, JJ; Makki, M; Qureshi, S; Weng, Q; Haase, D; Pradhan, B; Biswas, A; Atkinson, PM

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

Alavipanah, SK Karimi Firozjaei, M Sedighi, A Fathololoumi, S Zare Naghadehi, S Saleh, S Naghdizadegan, M Gomeh, Z Arsanjani, JJ Makki, M Qureshi, S Weng, Q Haase, D Pradhan, B

Biswas, A Atkinson, PM

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Land, 2022, 11, (11), pp. 2025
Issue Date:: 2022-11-01

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Field	Value	Language
dc.contributor.author	Alavipanah, SK
dc.contributor.author	Karimi Firozjaei, M
dc.contributor.author	Sedighi, A
dc.contributor.author	Fathololoumi, S
dc.contributor.author	Zare Naghadehi, S
dc.contributor.author	Saleh, S
dc.contributor.author	Naghdizadegan, M
dc.contributor.author	Gomeh, Z
dc.contributor.author	Arsanjani, JJ
dc.contributor.author	Makki, M
dc.contributor.author	Qureshi, S
dc.contributor.author	Weng, Q
dc.contributor.author	Haase, D
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Biswas, A
dc.contributor.author	Atkinson, PM
dc.date.accessioned	2023-04-05T06:15:22Z
dc.date.available	2023-04-05T06:15:22Z
dc.date.issued	2022-11-01
dc.identifier.citation	Land, 2022, 11, (11), pp. 2025
dc.identifier.issn	2073-445X
dc.identifier.issn	2073-445X
dc.identifier.uri	http://hdl.handle.net/10453/169211
dc.description.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.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Land
dc.relation.isbasedon	10.3390/land11112025
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0502 Environmental Science and Management
dc.title	The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0502 Environmental Science and Management
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2023-04-05T06:15:20Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	11

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.

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http://hdl.handle.net/10453/169211