An Unsupervised Machine Learning-Based Approach for Combining Sentinel 1 and 2 to Assess the Severity of Fires over Large Areas Using a Google Earth Engine.
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| Title: | An Unsupervised Machine Learning-Based Approach for Combining Sentinel 1 and 2 to Assess the Severity of Fires over Large Areas Using a Google Earth Engine. |
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| Authors: | Riccardi, Ciro Giuseppe1,2 (AUTHOR), Abate, Nicodemo2,3 (AUTHOR) nicodemo.abate@cnr.it, Lasaponara, Rosa2,3 (AUTHOR) |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 6, p956. 25p. |
| Subjects: | Optical remote sensing, Cloud computing, Fire risk assessment, Clustering algorithms |
| Abstract: | Highlights: What are the main findings? The integration of Sentinel-1 (SAR) and Sentinel-2 (optical) data within a Google Earth Engine framework achieved a highly accurate burned area estimation, showing a minimal discrepancy of only 1.3% compared to official EFFIS records. Statistical analysis confirms strong correlations between SAR-based indices (RVI and DPSVI) and traditional optical metrics, validating the use of radar to assess fire severity in areas with frequent cloud or smoke obstruction. What are the implications of the main findings? The proposed multi-sensor methodology provides a scalable and automated solution for large-scale wildfire monitoring, ensuring temporal continuity when optical sensors are limited by atmospheric conditions. Leveraging cloud computing and unsupervised machine learning (K-means) reduces reliance on manual interpretation, offering a robust tool for rapid post-fire damage assessment and global wildfire mitigation strategies. Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR's capability to penetrate atmospheric obstructions and optical data's spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. [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: 192591446 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Unsupervised Machine Learning-Based Approach for Combining Sentinel 1 and 2 to Assess the Severity of Fires over Large Areas Using a Google Earth Engine. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Riccardi%2C+Ciro+Giuseppe%22">Riccardi, Ciro Giuseppe</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abate%2C+Nicodemo%22">Abate, Nicodemo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> nicodemo.abate@cnr.it</i><br /><searchLink fieldCode="AR" term="%22Lasaponara%2C+Rosa%22">Lasaponara, Rosa</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 6, p956. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optical+remote+sensing%22">Optical remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Fire+risk+assessment%22">Fire risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The integration of Sentinel-1 (SAR) and Sentinel-2 (optical) data within a Google Earth Engine framework achieved a highly accurate burned area estimation, showing a minimal discrepancy of only 1.3% compared to official EFFIS records. Statistical analysis confirms strong correlations between SAR-based indices (RVI and DPSVI) and traditional optical metrics, validating the use of radar to assess fire severity in areas with frequent cloud or smoke obstruction. What are the implications of the main findings? The proposed multi-sensor methodology provides a scalable and automated solution for large-scale wildfire monitoring, ensuring temporal continuity when optical sensors are limited by atmospheric conditions. Leveraging cloud computing and unsupervised machine learning (K-means) reduces reliance on manual interpretation, offering a robust tool for rapid post-fire damage assessment and global wildfire mitigation strategies. Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR's capability to penetrate atmospheric obstructions and optical data's spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. [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/rs18060956 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 956 Subjects: – SubjectFull: Optical remote sensing Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Fire risk assessment Type: general – SubjectFull: Clustering algorithms Type: general Titles: – TitleFull: An Unsupervised Machine Learning-Based Approach for Combining Sentinel 1 and 2 to Assess the Severity of Fires over Large Areas Using a Google Earth Engine. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Riccardi, Ciro Giuseppe – PersonEntity: Name: NameFull: Abate, Nicodemo – PersonEntity: Name: NameFull: Lasaponara, Rosa IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 6 Titles: – TitleFull: Remote Sensing Type: main |
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