Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal.
Saved in:
| Title: | Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal. |
|---|---|
| Authors: | Neves, Vítor H.1,2,3 (AUTHOR), Sánchez-Pérez, Lisette2,4 (AUTHOR), Antunes, Sara C.2,3 (AUTHOR), Pace, Giorgio4,5,6 (AUTHOR), Sòria-Perpinyà, Xavier4,5 (AUTHOR), Delegido, Jesús4,6 (AUTHOR) jesus.delegido@uv.es |
| Source: | Remote Sensing. Feb2026, Vol. 18 Issue 3, p469. 20p. |
| Subjects: | Total suspended solids, Chlorophyll, Remote sensing, Atmospheric effects on remote sensing, Water depth, Artificial satellites, Water quality |
| Geographic Terms: | Portugal |
| Abstract: | Highlights: Developed localized empirical models for Chl-a, TSS, and SDD using Sentinel-2. Nine years of in situ data enhanced model calibration and validation. C2RCC-COMPLEX achieved superior performance in Alqueva Reservoir. Satellite-derived maps reveal drought and post-winter water quality trends. Monitoring water quality in large reservoirs is essential yet challenging, particularly in regions with limited in situ coverage. This study presents a robust methodology for integrating a decade-long in situ dataset (2014–2022) with Sentinel-2 multispectral imagery to develop and validate localized algorithms for water quality assessment in the Alqueva Reservoir, the largest artificial lake in Western Europe. Three atmospheric correction algorithms (C2RCC, C2X, C2X-COMPLEX) were evaluated, with C2RCC-COMPLEX identified as the most suitable for capturing the reservoir's optical complexity, yielding the lowest RMSE for Total Suspended Solids (TSS: 2.4 g/m3) and Secchi Disk Depth (SDD: 0.85 m). Empirical models using Sentinel-2 bands 7 (783 nm), 6 (740 nm), and 8A (865 nm) demonstrated strong correlations (R2 ≈ 0.69–0.71) for Chlorophyll-a (Chl-a) with a range data of 0.1–65 mg/m3, TSS with a range data of 2–13.1 g/m3, and SDD with a range data of 0.4–8 m. Spatially explicit water quality maps illustrate the models' capacity to capture distinct gradients and seasonal dynamics, e.g., elevated Chl-a (>30 mg/m3) and TSS (>7.5 g/m3) in the reservoir's nutrient-rich northern section during drought (August 2022), and more uniform conditions following winter recovery (March 2019), with SDD exceeding 2 m near the dam. These results underscore the utility of Sentinel-2 for resolving spatial and temporal variability in optically complex inland waters. The proposed workflow offers a transferable, cost-effective framework for monitoring eutrophication risks and sediment dynamics under increasing hydrological variability. [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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 191586607 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Neves%2C+Vítor+H%2E%22">Neves, Vítor H.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sánchez-Pérez%2C+Lisette%22">Sánchez-Pérez, Lisette</searchLink><relatesTo>2,4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Antunes%2C+Sara+C%2E%22">Antunes, Sara C.</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pace%2C+Giorgio%22">Pace, Giorgio</searchLink><relatesTo>4,5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sòria-Perpinyà%2C+Xavier%22">Sòria-Perpinyà, Xavier</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Delegido%2C+Jesús%22">Delegido, Jesús</searchLink><relatesTo>4,6</relatesTo> (AUTHOR)<i> jesus.delegido@uv.es</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Feb2026, Vol. 18 Issue 3, p469. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Total+suspended+solids%22">Total suspended solids</searchLink><br /><searchLink fieldCode="DE" term="%22Chlorophyll%22">Chlorophyll</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+effects+on+remote+sensing%22">Atmospheric effects on remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Water+depth%22">Water depth</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+satellites%22">Artificial satellites</searchLink><br /><searchLink fieldCode="DE" term="%22Water+quality%22">Water quality</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Portugal%22">Portugal</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: Developed localized empirical models for Chl-a, TSS, and SDD using Sentinel-2. Nine years of in situ data enhanced model calibration and validation. C2RCC-COMPLEX achieved superior performance in Alqueva Reservoir. Satellite-derived maps reveal drought and post-winter water quality trends. Monitoring water quality in large reservoirs is essential yet challenging, particularly in regions with limited in situ coverage. This study presents a robust methodology for integrating a decade-long in situ dataset (2014–2022) with Sentinel-2 multispectral imagery to develop and validate localized algorithms for water quality assessment in the Alqueva Reservoir, the largest artificial lake in Western Europe. Three atmospheric correction algorithms (C2RCC, C2X, C2X-COMPLEX) were evaluated, with C2RCC-COMPLEX identified as the most suitable for capturing the reservoir's optical complexity, yielding the lowest RMSE for Total Suspended Solids (TSS: 2.4 g/m3) and Secchi Disk Depth (SDD: 0.85 m). Empirical models using Sentinel-2 bands 7 (783 nm), 6 (740 nm), and 8A (865 nm) demonstrated strong correlations (R2 ≈ 0.69–0.71) for Chlorophyll-a (Chl-a) with a range data of 0.1–65 mg/m3, TSS with a range data of 2–13.1 g/m3, and SDD with a range data of 0.4–8 m. Spatially explicit water quality maps illustrate the models' capacity to capture distinct gradients and seasonal dynamics, e.g., elevated Chl-a (>30 mg/m3) and TSS (>7.5 g/m3) in the reservoir's nutrient-rich northern section during drought (August 2022), and more uniform conditions following winter recovery (March 2019), with SDD exceeding 2 m near the dam. These results underscore the utility of Sentinel-2 for resolving spatial and temporal variability in optically complex inland waters. The proposed workflow offers a transferable, cost-effective framework for monitoring eutrophication risks and sediment dynamics under increasing hydrological variability. [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=191586607 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18030469 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 469 Subjects: – SubjectFull: Total suspended solids Type: general – SubjectFull: Chlorophyll Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Atmospheric effects on remote sensing Type: general – SubjectFull: Water depth Type: general – SubjectFull: Artificial satellites Type: general – SubjectFull: Water quality Type: general – SubjectFull: Portugal Type: general Titles: – TitleFull: Development and Calibration of Sentinel-2 Spectral Indices for Water Quality Parameter Estimation in Alqueva Reservoir, Southern Portugal. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Neves, Vítor H. – PersonEntity: Name: NameFull: Sánchez-Pérez, Lisette – PersonEntity: Name: NameFull: Antunes, Sara C. – PersonEntity: Name: NameFull: Pace, Giorgio – PersonEntity: Name: NameFull: Sòria-Perpinyà, Xavier – PersonEntity: Name: NameFull: Delegido, Jesús IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 3 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
