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Quantifying drought impacts on wetlands in arid regions using a change-based drought severity index (WCDI) and Sentinel-1/2 data fusion within Google Earth Engine: evidence from two wetlands in central Iran.

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Title: Quantifying drought impacts on wetlands in arid regions using a change-based drought severity index (WCDI) and Sentinel-1/2 data fusion within Google Earth Engine: evidence from two wetlands in central Iran.
Authors: Sarvari, Ziba1 (AUTHOR) z.sarvari1993@gmail.com, Khosravi, Iman1 (AUTHOR) i.khosravi@cet.ui.ac.ir, Bagheri, Hossein1 (AUTHOR) h.bagheri@cet.ui.ac.ir
Source: Advances in Space Research. Jul2026, Vol. 78 Issue 2, p973-999. 27p.
Subjects: Wetlands, Droughts, Machine learning, Arid regions, Remote sensing, Land use, Cloud computing
Geographic Terms: Iran
Abstract: This study presents a comprehensive analysis of land use and land cover (LULC) changes in Gavkhouni and Mighan wetlands––two critical Ramsar sites in central Iran–during the period 2017 to 2023. By leveraging the cloud-computing capabilities of Google Earth Engine (GEE), we developed a robust classification workflow that integrates multi-source remote sensing data, including Sentinel-1 SAR, Sentinel-2 optical imagery, key spectral indices (NDVI, MNDWI, SAVI, BSI, WRI), and a digital elevation model (SRTM DEM). A comparative evaluation of three machine learning algorithms—support vector machine (SVM), decision tree (DT), and random forest (RF)—identified RF as the most accurate classifier, achieving exceptional overall accuracies exceeding 91% and Kappa coefficients above 0.89 for both wetlands. A key innovation of this research is the introduction of a novel index—the wetland change-based drought severity index (WCDI)—derived from the land cover change matrix. The WCDI quantifies drought severity by analyzing transitions between moisture-related (e.g., water, vegetation) and aridity-related classes (e.g., barren land, salt flats). Independent validation against the 12-month Standardized Precipitation Index (SPI-12) demonstrated moderate to strong correlations for both WCDI formulations across the two wetlands. For Gavkhouni, correlation coefficients were r = 0.81 (WCDI 1) and r = 0.79 (WCDI 2) (p < 0.05); for Mighan, they were r = 0.74 (WCDI 1) and r = 0.71 (WCDI 2) (p < 0.05). These results confirm that both indices respond predictably to climatic water deficits. Drought severity thresholds were empirically derived using a percentile-based approach applied separately to each wetland's historical WCDI time series and are therefore site-specific rather than universally transferable. The change detection results reveal an alarming ecological crisis, marked by a drastic decline in water bodies and vegetation cover, alongside a significant expansion of barren land and salt flats. The application of the WCDI revealed persistent "Very Severe Drought" conditions during 2017–2021, with a slight improvement in 2021–2023 that does not indicate full recovery. These transformations are primarily attributed to prolonged drought, unsustainable water management practices, and increasing anthropogenic pressures. This research underscores the urgent need for targeted conservation strategies and offers a scalable, semi-automated framework—enhanced by the novel WCDI —for high-precision wetland monitoring (requiring site-specific calibration) and drought assessment in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:This study presents a comprehensive analysis of land use and land cover (LULC) changes in Gavkhouni and Mighan wetlands––two critical Ramsar sites in central Iran–during the period 2017 to 2023. By leveraging the cloud-computing capabilities of Google Earth Engine (GEE), we developed a robust classification workflow that integrates multi-source remote sensing data, including Sentinel-1 SAR, Sentinel-2 optical imagery, key spectral indices (NDVI, MNDWI, SAVI, BSI, WRI), and a digital elevation model (SRTM DEM). A comparative evaluation of three machine learning algorithms—support vector machine (SVM), decision tree (DT), and random forest (RF)—identified RF as the most accurate classifier, achieving exceptional overall accuracies exceeding 91% and Kappa coefficients above 0.89 for both wetlands. A key innovation of this research is the introduction of a novel index—the wetland change-based drought severity index (WCDI)—derived from the land cover change matrix. The WCDI quantifies drought severity by analyzing transitions between moisture-related (e.g., water, vegetation) and aridity-related classes (e.g., barren land, salt flats). Independent validation against the 12-month Standardized Precipitation Index (SPI-12) demonstrated moderate to strong correlations for both WCDI formulations across the two wetlands. For Gavkhouni, correlation coefficients were r = 0.81 (WCDI 1) and r = 0.79 (WCDI 2) (p < 0.05); for Mighan, they were r = 0.74 (WCDI 1) and r = 0.71 (WCDI 2) (p < 0.05). These results confirm that both indices respond predictably to climatic water deficits. Drought severity thresholds were empirically derived using a percentile-based approach applied separately to each wetland's historical WCDI time series and are therefore site-specific rather than universally transferable. The change detection results reveal an alarming ecological crisis, marked by a drastic decline in water bodies and vegetation cover, alongside a significant expansion of barren land and salt flats. The application of the WCDI revealed persistent "Very Severe Drought" conditions during 2017–2021, with a slight improvement in 2021–2023 that does not indicate full recovery. These transformations are primarily attributed to prolonged drought, unsustainable water management practices, and increasing anthropogenic pressures. This research underscores the urgent need for targeted conservation strategies and offers a scalable, semi-automated framework—enhanced by the novel WCDI —for high-precision wetland monitoring (requiring site-specific calibration) and drought assessment in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]
ISSN:02731177
DOI:10.1016/j.asr.2026.04.096