On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations.
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| Title: | On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations. |
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| Authors: | Barbato, Mirko Paolo1 (AUTHOR), Cilli, Roberto2 (AUTHOR), Napoletano, Paolo1,3 (AUTHOR) paolo.napoletano@unimib.it, Pompili, Alexis1,3 (AUTHOR), Ramirez-Sanchez, Gabriel2,3 (AUTHOR), Sozbilir, Umit3 (AUTHOR) |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 14, p2400. 31p. |
| Subjects: | Synthetic aperture radar, Multispectral imaging, Remote sensing, Vegetation monitoring |
| Abstract: | Highlights: What are the main findings? An investigation of how an SAR-to-multispectral reconstruction framework can produce vegetation indices statistically equivalent to dedicated SAR-to-index-specific models. An ablation study on the use of SAR and WorldCover inputs and on the use of the conventional evaluation metrics that yields statistically indistinguishable reconstruction performance across all evaluated indices. What are the implications of the main findings? A single multispectral reconstruction model can replace multiple dedicated indexspecific models, reducing training overhead while preserving accuracy. The use of solely conventional metrics (MAE, PSNR, SSIM) does not represent the real quantitative performance of the reconstruction models, thus more discriminative evaluation protocols are needed. Optical vegetation and soil indices are widely used in Earth observation, although their estimation is strongly affected by cloud coverage and illumination variability. Synthetic-aperture radar (SAR) has therefore attracted increasing interest as an alternative source for spectral index prediction. Most existing studies focus on directly estimating a single index from SAR observations. In this work, we investigate a more flexible formulation in which Sentinel-2 multispectral bands are first reconstructed from Sentinel-1 SAR data and subsequently used to derive multiple spectral indices. Experiments are conducted on the SEN12TP dataset, exploiting near-synchronous paired Sentinel-1 and Sentinel-2 acquisitions together with auxiliary elevation and land-cover information. Three SAR-to-multispectral reconstruction strategies are compared, namely, Efficient-UNet, Pix2Pix, and a conditional flow matching model. The resulting indices are then evaluated against those obtained through dedicated index-specific reconstruction models. The results show that Efficient-UNet achieves the best overall multispectral reconstruction performance among the evaluated architectures. Moreover, indices derived from reconstructed multispectral bands achieve performance comparable to dedicated index-specific models while offering substantially greater flexibility, as multiple indices can be computed within a single framework without retraining task-specific models. At the same time, the experiments highlight important intrinsic limitations of SAR-based spectral reconstruction. Although the reconstructed products preserve the large-scale spatial organization of the scenes, they do not fully recover fine spectral and vegetation-sensitive details. Consequently, SAR-derived spectral indices should be regarded as approximate proxies of optical observations rather than direct substitutes, particularly in applications requiring accurate biophysical interpretation. [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: 195801468 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Barbato%2C+Mirko+Paolo%22">Barbato, Mirko Paolo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cilli%2C+Roberto%22">Cilli, Roberto</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Napoletano%2C+Paolo%22">Napoletano, Paolo</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<i> paolo.napoletano@unimib.it</i><br /><searchLink fieldCode="AR" term="%22Pompili%2C+Alexis%22">Pompili, Alexis</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramirez-Sanchez%2C+Gabriel%22">Ramirez-Sanchez, Gabriel</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sozbilir%2C+Umit%22">Sozbilir, Umit</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jul2026, Vol. 18 Issue 14, p2400. 31p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Vegetation+monitoring%22">Vegetation monitoring</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? An investigation of how an SAR-to-multispectral reconstruction framework can produce vegetation indices statistically equivalent to dedicated SAR-to-index-specific models. An ablation study on the use of SAR and WorldCover inputs and on the use of the conventional evaluation metrics that yields statistically indistinguishable reconstruction performance across all evaluated indices. What are the implications of the main findings? A single multispectral reconstruction model can replace multiple dedicated indexspecific models, reducing training overhead while preserving accuracy. The use of solely conventional metrics (MAE, PSNR, SSIM) does not represent the real quantitative performance of the reconstruction models, thus more discriminative evaluation protocols are needed. Optical vegetation and soil indices are widely used in Earth observation, although their estimation is strongly affected by cloud coverage and illumination variability. Synthetic-aperture radar (SAR) has therefore attracted increasing interest as an alternative source for spectral index prediction. Most existing studies focus on directly estimating a single index from SAR observations. In this work, we investigate a more flexible formulation in which Sentinel-2 multispectral bands are first reconstructed from Sentinel-1 SAR data and subsequently used to derive multiple spectral indices. Experiments are conducted on the SEN12TP dataset, exploiting near-synchronous paired Sentinel-1 and Sentinel-2 acquisitions together with auxiliary elevation and land-cover information. Three SAR-to-multispectral reconstruction strategies are compared, namely, Efficient-UNet, Pix2Pix, and a conditional flow matching model. The resulting indices are then evaluated against those obtained through dedicated index-specific reconstruction models. The results show that Efficient-UNet achieves the best overall multispectral reconstruction performance among the evaluated architectures. Moreover, indices derived from reconstructed multispectral bands achieve performance comparable to dedicated index-specific models while offering substantially greater flexibility, as multiple indices can be computed within a single framework without retraining task-specific models. At the same time, the experiments highlight important intrinsic limitations of SAR-based spectral reconstruction. Although the reconstructed products preserve the large-scale spatial organization of the scenes, they do not fully recover fine spectral and vegetation-sensitive details. Consequently, SAR-derived spectral indices should be regarded as approximate proxies of optical observations rather than direct substitutes, particularly in applications requiring accurate biophysical interpretation. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195801468 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18142400 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 2400 Subjects: – SubjectFull: Synthetic aperture radar Type: general – SubjectFull: Multispectral imaging Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Vegetation monitoring Type: general Titles: – TitleFull: On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Barbato, Mirko Paolo – PersonEntity: Name: NameFull: Cilli, Roberto – PersonEntity: Name: NameFull: Napoletano, Paolo – PersonEntity: Name: NameFull: Pompili, Alexis – PersonEntity: Name: NameFull: Ramirez-Sanchez, Gabriel – PersonEntity: Name: NameFull: Sozbilir, Umit IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 14 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
