Spatiotemporal drought analysis and future risk assessment using multi-index remote sensing approach and hybrid trend-based prediction modeling

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dc.authorid0009-0006-1815-4886
dc.contributor.authorPolat, Ahmet Batuhan
dc.contributor.authorAlumert, Egehan
dc.contributor.authorAkcay, Ozgun
dc.date.accessioned2026-02-03T12:03:02Z
dc.date.available2026-02-03T12:03:02Z
dc.date.issued2026
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThis study presents a comprehensive spatiotemporal drought assessment for & Ccedil;anakkale province, T & uuml;rkiye, utilizing multi-index remote sensing approaches over a 20-year period (2005-2024) coupled with predictive risk modeling for 2025-2027. Four key environmental parameters were derived through the Google Earth Engine platform: Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Standardized Precipitation Index (SPI), and Soil Moisture Condition Index (SMCI). Multiple satellite data sources were integrated, including Landsat 7 ETM + , MODIS/MOD11A1, CHIRPS precipitation dataset, and TerraClimate hydrological data. The retrospective analysis revealed significant climatic variability characterized by inter-annual LST fluctuations, progressive NDVI enhancement toward 2024, and pronounced negative trends in both SPI and SMCI indices during recent years. Particularly, SMCI reached - 1.14 in 2023, indicating severe soil moisture deficit. Spatial heterogeneity was evident across the province, with differential vegetation dynamics and precipitation patterns between coastal and interior regions. A Principal Component Analysis-based integrated drought index was developed, explaining 68.7% of total variance, providing comprehensive drought characterization beyond univariate approaches. A hybrid trend-based forecasting framework incorporating seasonal decomposition, climatological constraints, and stochastic variability was implemented. Model validation demonstrated robust performance for LST (R2 = 0.85) and NDVI (R2 = 0.88), while SPI and SMCI exhibited challenges inherent to normalized indices with small-magnitude variations. Prospective projections indicate systematic elevation in composite drought risk from 2.58 (2025) to 2.67 (2026-2027), representing a 3.5% increase and persistent moderate-to-high drought vulnerability. These findings provide critical insights for regional water resource management, agricultural planning, and climate adaptation strategies in Mediterranean ecosystems facing intensifying drought pressures.
dc.identifier.doi10.1007/s10661-025-14895-6
dc.identifier.issn0167-6369
dc.identifier.issn1573-2959
dc.identifier.issue2
dc.identifier.pmid41524964
dc.identifier.scopus2-s2.0-105027217513
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10661-025-14895-6
dc.identifier.urihttps://hdl.handle.net/20.500.12428/34948
dc.identifier.volume198
dc.identifier.wosWOS:001660177100002
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofEnvironmental Monitoring and Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20260130
dc.subjectDrought monitoring
dc.subjectRemote sensing
dc.subjectClimate change
dc.subjectDrought prediction
dc.subjectTemporal modeling
dc.titleSpatiotemporal drought analysis and future risk assessment using multi-index remote sensing approach and hybrid trend-based prediction modeling
dc.typeArticle

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