Multi-source remote sensing approach for monitoring the Caspian Sea shoreline dynamics

Abstract

This study presents a scalable methodology for the semi-automated extraction of the Caspian Sea shoreline and water surface based on medium-resolution satellite imagery and cloud computing within the Google Earth Engine environment. The input datasets included very high-resolution PlanetScope imagery, Harmonized Landsat–Sentinel products, and DAHITI satellite altimetry data. The proposed approach consists of three stages: generation of reference and working shoreline datasets, validation-based selection of the optimal extraction algorithm, and its subsequent scaling to the entire Caspian Sea. To assess accuracy, twelve automated water–land boundary extraction scenarios based on different spectral indices and temporal compositing methods were evaluated. Validation on test sites in the northeastern part of the Caspian Sea was performed using transect analysis in Digital Shoreline Analysis System and reference shorelines derived from PlanetScope imagery. The results demonstrated that the highest positional accuracy was achieved by combining the MNDWI with the 25th-percentile temporal compositing approach. Meanwhile, the AWEI2 method with median aggregation produced a more spatially coherent water mask and was used as an additional quality-control layer to identify local artifacts and gaps in the MNDWI_p25 mask during subsequent manual correction. Based on the selected algorithm, a multi-year geospatial dataset of shorelines and water surface polygons of the Caspian Sea was generated for the period 2016–2025. The results showed that the sea level declined by 1.33 m, the water surface area decreased by 11,216.31 km 2 , and the shoreline length was reduced from 19,907.94 km to 15,551.79 km. The most pronounced changes, reflecting the high sensitivity of shallow-water regions to fluctuations in water level, were observed in the northeastern Caspian Sea, where shoreline retreat exceeded 20 km in some areas. The obtained results confirm the effectiveness of the proposed approach as a basis for scalable monitoring of coastal-zone dynamics and demonstrate its potential for geoecological assessments, forecasting morphodynamic processes, and developing adaptation strategies under conditions of continued Caspian Sea level decline.