Reducing Precipitation-Driven Climatic Bias in SDG 15.3.1 Land Degradation Assessments Using a Hybrid Productivity Approach: A Remote Sensing Analysis for Northern and Central Morocco (2000–2022).
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| Title: | Reducing Precipitation-Driven Climatic Bias in SDG 15.3.1 Land Degradation Assessments Using a Hybrid Productivity Approach: A Remote Sensing Analysis for Northern and Central Morocco (2000–2022). |
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| Authors: | Raghuvanshi, Nikhil1 (AUTHOR) nikhil.raghuvanshi@uni-hamburg.de, Ahmadian, Nima1 (AUTHOR), Dubovyk, Olena1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1531. 43p. |
| Subjects: | Precipitation variability, Normalized difference vegetation index, Remote sensing, Arid regions, Land degradation, Environmental monitoring |
| Geographic Terms: | Morocco |
| Abstract: | Highlights: What are the main findings? The hybrid framework successfully decouples precipitation variability from vegetation signals, preventing the systematic misclassification of drought-induced "browning" as permanent land degradation. Cross-tabulation results reveal that anthropogenic pressures, specifically overgrazing and water mismanagement, are the dominant drivers of long-term degradation in Moroccan drylands. What are the implications of the main findings? Integrating climate-aware indicators into SDG 15.3.1 reporting is essential for distinguishing transient climatic stress from human-induced decline, a critical requirement for achieving Land Degradation Neutrality (LDN) by 2030. The methodology's reliance on open-source Landsat data and cloud computing provides a scalable, cost-effective template for context-sensitive environmental monitoring across global dryland regions. Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that 18 % of land showed declining productivity, 75 % remained stable, and 6 % showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. [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: 194141056 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Reducing Precipitation-Driven Climatic Bias in SDG 15.3.1 Land Degradation Assessments Using a Hybrid Productivity Approach: A Remote Sensing Analysis for Northern and Central Morocco (2000–2022). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Raghuvanshi%2C+Nikhil%22">Raghuvanshi, Nikhil</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nikhil.raghuvanshi@uni-hamburg.de</i><br /><searchLink fieldCode="AR" term="%22Ahmadian%2C+Nima%22">Ahmadian, Nima</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dubovyk%2C+Olena%22">Dubovyk, Olena</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1531. 43p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Precipitation+variability%22">Precipitation variability</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Arid+regions%22">Arid regions</searchLink><br /><searchLink fieldCode="DE" term="%22Land+degradation%22">Land degradation</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+monitoring%22">Environmental monitoring</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Morocco%22">Morocco</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The hybrid framework successfully decouples precipitation variability from vegetation signals, preventing the systematic misclassification of drought-induced "browning" as permanent land degradation. Cross-tabulation results reveal that anthropogenic pressures, specifically overgrazing and water mismanagement, are the dominant drivers of long-term degradation in Moroccan drylands. What are the implications of the main findings? Integrating climate-aware indicators into SDG 15.3.1 reporting is essential for distinguishing transient climatic stress from human-induced decline, a critical requirement for achieving Land Degradation Neutrality (LDN) by 2030. The methodology's reliance on open-source Landsat data and cloud computing provides a scalable, cost-effective template for context-sensitive environmental monitoring across global dryland regions. Land productivity assessments used in SDG 15.3.1 commonly rely on NDVI trends, which may be strongly influenced by precipitation variability and can therefore misrepresent actual land condition change, particularly in dryland environments where vegetation productivity responds rapidly to rainfall fluctuations. To address this issue, this study presents a land degradation assessment (2000–2022) using a fully reproducible Google Earth Engine workflow integrating high-resolution 30 m Landsat time-series NDVI, precipitation, land cover, and soil organic carbon datasets. The core methodological contribution is a precipitation-conditioned hybrid productivity framework that dynamically selects among NDVI trends, Rain-Use Efficiency (RUE), and Residual Trends (RESTREND) according to local rainfall dynamics. By adapting productivity metrics to precipitation conditions, the framework reduces precipitation-driven misinterpretation of vegetation trends, operationalizes a more climate-aware implementation of the land productivity (LP) sub-indicator within SDG 15.3.1, and enables systematic comparison of productivity metrics under contrasting rainfall regimes. Results for the 2015–2022 monitoring period, which included multiple drought years, indicate that 18 % of land showed declining productivity, 75 % remained stable, and 6 % showed improvement. Decline was spatially concentrated in arid and semi-arid regions, whereas irrigated and managed landscapes exhibited localized improvements. The hybrid indicator provides an additional option for LP assessment that explicitly accounts for precipitation variability, supporting more climate-sensitive interpretation of productivity trends. This transferable, reproducible methodology strengthens national capacity for SDG 15.3.1 reporting and offers a scalable framework for land degradation assessments in other drought-prone regions. [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=194141056 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/rs18101531 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 43 StartPage: 1531 Subjects: – SubjectFull: Precipitation variability Type: general – SubjectFull: Normalized difference vegetation index Type: general – SubjectFull: Remote sensing Type: general – SubjectFull: Arid regions Type: general – SubjectFull: Land degradation Type: general – SubjectFull: Environmental monitoring Type: general – SubjectFull: Morocco Type: general Titles: – TitleFull: Reducing Precipitation-Driven Climatic Bias in SDG 15.3.1 Land Degradation Assessments Using a Hybrid Productivity Approach: A Remote Sensing Analysis for Northern and Central Morocco (2000–2022). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Raghuvanshi, Nikhil – PersonEntity: Name: NameFull: Ahmadian, Nima – PersonEntity: Name: NameFull: Dubovyk, Olena IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 10 Titles: – TitleFull: Remote Sensing Type: main |
| ResultId | 1 |
