A Topographic Shadow Effect Correction (TSEC) Method for Correcting Surface Reflectance of Optical Remote Sensing Images in Rugged Terrain.
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| Title: | A Topographic Shadow Effect Correction (TSEC) Method for Correcting Surface Reflectance of Optical Remote Sensing Images in Rugged Terrain. |
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| Authors: | Yang, Xu1 (AUTHOR), Xie, Wenbin2,3 (AUTHOR) xwb@ygbdcenter.com, Zuo, Xiaoqing3,4 (AUTHOR), Guo, Shipeng4,5 (AUTHOR), Zhu, Daming4,5 (AUTHOR), Li, Yongfa1,4 (AUTHOR), Li, Jiangqi1,2 (AUTHOR), Luo, Yan1,3 (AUTHOR) |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 4, p642. 26p. |
| Subjects: | Optical remote sensing, Digital elevation models, Vegetation monitoring, Landscapes, Normalized difference vegetation index |
| Abstract: | Highlights: What are the main findings? Proposed the TSEC model integrating shadow intensity, band adjustment, and vegetation index factors to effectively restore spectral information in rugged terrain. TSEC outperforms traditional methods (MIN, SCS + C) in shadow restoration by effectively avoiding over-correction in self-shadows and under-correction in cast shadows. What are the implications of the main findings? The method ensures high spectral fidelity and stability for key vegetation indices (NDVI and EVI) across varying illumination conditions. TSEC offers a robust and effective solution for quantitative remote sensing in complex mountainous areas, requiring only original images and DEM data. The topographic shadow effect can cause surface reflectance distortions in the shadow areas of remote sensing images, particularly in complex mountainous areas. In this study, based on the difference in solar radiation received at the surface of sunlit and shadow areas, we introduced the shadow intensity, vegetation index, and band adjustment factors, and proposed a topographic shadow effect correction (TSEC) method. The method was then tested using eight Landsat 8 OLI scenes under different illumination conditions from two different regions. The results indicate that TSEC effectively corrected the topographic shadow effect. The corrected images exhibited good visual quality without obvious shadow pixels. Importantly, TSEC retained spectral information in sunlit areas while correcting spectral distortion in shadow areas, resulting in strong agreement between spectral curves of shady and sunny slopes. The method demonstrated high stability in normalized difference vegetation index (NDVI) correction, as the difference in NDVI before and after correction was less than 0.07 for the four scenes within the Changjiang study area. Moreover, the TSEC corrected the enhanced vegetation index (EVI) effectively, reducing an initial EVI difference of over 0.35 between the shady and sunny slopes to a maximum of 0.074 for the four scenes within the Wuyi Mountain study area. Relative to four established topographic correction models, the proposed method suppresses the over-correction phenomena typical of self-shadows and minimizes under-correction in cast shadows, resulting in stable overall correction results with few outliers. The TSEC provides a simple and effective method to correct the distorted reflectance in shadow areas using only image and DEM data, which can be adapted to complex mountainous areas and for images with different illumination conditions. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 191972727 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Topographic Shadow Effect Correction (TSEC) Method for Correcting Surface Reflectance of Optical Remote Sensing Images in Rugged Terrain. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yang%2C+Xu%22">Yang, Xu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xie%2C+Wenbin%22">Xie, Wenbin</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> xwb@ygbdcenter.com</i><br /><searchLink fieldCode="AR" term="%22Zuo%2C+Xiaoqing%22">Zuo, Xiaoqing</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Shipeng%22">Guo, Shipeng</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Daming%22">Zhu, Daming</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Yongfa%22">Li, Yongfa</searchLink><relatesTo>1,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiangqi%22">Li, Jiangqi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Luo%2C+Yan%22">Luo, Yan</searchLink><relatesTo>1,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 4, p642. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optical+remote+sensing%22">Optical remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+elevation+models%22">Digital elevation models</searchLink><br /><searchLink fieldCode="DE" term="%22Vegetation+monitoring%22">Vegetation monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Landscapes%22">Landscapes</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? Proposed the TSEC model integrating shadow intensity, band adjustment, and vegetation index factors to effectively restore spectral information in rugged terrain. TSEC outperforms traditional methods (MIN, SCS + C) in shadow restoration by effectively avoiding over-correction in self-shadows and under-correction in cast shadows. What are the implications of the main findings? The method ensures high spectral fidelity and stability for key vegetation indices (NDVI and EVI) across varying illumination conditions. TSEC offers a robust and effective solution for quantitative remote sensing in complex mountainous areas, requiring only original images and DEM data. The topographic shadow effect can cause surface reflectance distortions in the shadow areas of remote sensing images, particularly in complex mountainous areas. In this study, based on the difference in solar radiation received at the surface of sunlit and shadow areas, we introduced the shadow intensity, vegetation index, and band adjustment factors, and proposed a topographic shadow effect correction (TSEC) method. The method was then tested using eight Landsat 8 OLI scenes under different illumination conditions from two different regions. The results indicate that TSEC effectively corrected the topographic shadow effect. The corrected images exhibited good visual quality without obvious shadow pixels. Importantly, TSEC retained spectral information in sunlit areas while correcting spectral distortion in shadow areas, resulting in strong agreement between spectral curves of shady and sunny slopes. The method demonstrated high stability in normalized difference vegetation index (NDVI) correction, as the difference in NDVI before and after correction was less than 0.07 for the four scenes within the Changjiang study area. Moreover, the TSEC corrected the enhanced vegetation index (EVI) effectively, reducing an initial EVI difference of over 0.35 between the shady and sunny slopes to a maximum of 0.074 for the four scenes within the Wuyi Mountain study area. Relative to four established topographic correction models, the proposed method suppresses the over-correction phenomena typical of self-shadows and minimizes under-correction in cast shadows, resulting in stable overall correction results with few outliers. The TSEC provides a simple and effective method to correct the distorted reflectance in shadow areas using only image and DEM data, which can be adapted to complex mountainous areas and for images with different illumination conditions. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18040642 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 642 Subjects: – SubjectFull: Optical remote sensing Type: general – SubjectFull: Digital elevation models Type: general – SubjectFull: Vegetation monitoring Type: general – SubjectFull: Landscapes Type: general – SubjectFull: Normalized difference vegetation index Type: general Titles: – TitleFull: A Topographic Shadow Effect Correction (TSEC) Method for Correcting Surface Reflectance of Optical Remote Sensing Images in Rugged Terrain. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yang, Xu – PersonEntity: Name: NameFull: Xie, Wenbin – PersonEntity: Name: NameFull: Zuo, Xiaoqing – PersonEntity: Name: NameFull: Guo, Shipeng – PersonEntity: Name: NameFull: Zhu, Daming – PersonEntity: Name: NameFull: Li, Yongfa – PersonEntity: Name: NameFull: Li, Jiangqi – PersonEntity: Name: NameFull: Luo, Yan IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |