Hydromorphological Monitoring and Navigation Assessment on Alluvial River Sections Using Sentinel-2 and Water Gauge Data

UAS-Validated Comparison of Sentinel-2 Shoreline Extraction Techniques for Large-Lake Coastal Mapping

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ2(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches.

Comparison of Shoreline Determination Methods Using Multi-Sensor Data in Low-Relief Coastal Environments

Shoreline determination is fundamental to coastal research, spatial planning, and legal boundary delineation but remains challenging in low-relief coastal areas where small sea-level variations can produce substantial horizontal shoreline displacements. This study compares shoreline determination methods based on tide gauge (TG) observations, LiDAR data, Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical satellite imagery using the low-relief coast of Pärnu Bay, Estonia, as a case study. The comparison was based on shorelines derived from Sentinel-1 and Sentinel-2 imagery acquired on selected common acquisition dates within the 2015–2025 study period, rather than on a temporally continuous annual dataset, and compared with temporally matched LiDAR-derived shorelines extracted from a Digital Terrain Model (DTM) generated from a 2021 LiDAR survey. The LiDAR-derived shorelines were extracted using the mean sea level (MSL) observed at the Pärnu and Häädemeeste TGs at the satellite overpass time, while the satellite-derived shorelines were additionally validated against RTK GNSS measurements. The results demonstrate that the evaluated methods produce substantially different shoreline positions. Sentinel-2-derived shorelines generally corresponded more closely to the temporally matched LiDAR-derived shorelines than Sentinel-1-derived shorelines and most accurately represented the instantaneous land–water boundary during field validation. These findings demonstrate that different shoreline determination methods represent different shoreline definitions. Consequently, shoreline datasets should be interpreted according to their intended purpose rather than treated as directly interchangeable.

Sentinel-2-Derived Water Surface Mapping and Bathymetric Analysis of Reservoirs for Floating PV Deployment: A Case Study of the Basilicata Region, Southern Italy

Floating photovoltaics (FPV) offer a sustainable approach to renewable energy production while enhancing water resource management by reducing land use, improving module efficiency through natural cooling, and limiting water evaporation. However, large-scale deployment requires careful site selection, operational feasibility assessment, and integration with existing water management practices. This study presents a fully cloud-native Sentinel-2-based framework to map recent water surface extents of reservoirs and reconstruct their bathymetric profiles. Developed within the Google Earth Engine (GEE) environment, the approach employs harmonized time series, automated cloud filtering, and median composites combined with adaptive NDWI/MNDWI thresholds to generate high-resolution (10 m) water masks. These dynamic surface extents are integrated with monitored water levels to estimate bathymetry through an annual multitemporal log-ratio band-switching configuration. The method has been tested across the nine main strategic reservoirs of the Basilicata region (Southern Italy). The water-surface extraction approach achieved an Overall Accuracy of 95.33% and a Kappa coefficient of 0.907 in the internal thematic assessment. The bathymetric reconstruction results led to relative volume errors for medium-to-large water bodies ranging between 13.8% and 35.1%, while larger percentage discrepancies in smaller impoundments were driven by scale-dependent normalization effects on low storage volumes. Overall, the proposed method offers a scalable, cost-effective, and Earth Observation (EO)-driven screening tool for the initial site selection, capacity assessment, and planning of FPV systems, particularly in data-scarce regions lacking updated bathymetric surveys.

Integrated Satellite-Derived Bathymetry and Morphodynamic Assessment for Regulated River Monitoring Using Machine Learning and Sentinel-2 Data

This study presents an integrated, data-driven framework for satellite-derived bathymetry and morphodynamic assessment in large, regulated rivers, providing a spatial database to support reach-scale hydromorphological monitoring and river management. Satellite-derived bathymetry (SDB) was developed using 24,768 in situ depth measurements and Sentinel-2 multispectral data to train Random Forest (RF) and Artificial Neural Network (ANN) models. Under turbid water conditions, the Random Forest model outperformed the Artificial Neural Network model in simulating the non-linear relationship between the water spectrum and water depth; the RF model achieved an R2 of 0.828 and an RMSE of 0.93 m, while the ANN model produced an R2 of 0.608 and an RMSE of 1.40 m. Depth-dependent errors were smallest at intermediate depths and larger in shallow and deep water. Morphometric parameters, including the Sinuosity Index (SI) and Braiding Index (BI), were calculated for 2017, 2019, and 2021 using the NDWI-based water mask to define channel boundaries. The reach exhibited moderate sinuosity (SI ≈ 1.16), and an increase in braiding was observed (BI ranging from 1.33 to 1.36). From 2017 to 2019, erosion (3.51 km2) exceeded deposition (1.25 km2). In contrast, the 2019–2021 period showed approximately equal areas of erosion and deposition (1.63 km2 each). The analysis is constrained by a single 2015 calibration survey, the optical penetration limit of Sentinel-2, and the reliance on three morphometric snapshots (2017, 2019, 2021), which may not capture short-term adjustments. The novelty of this study lies in integrating ML-based Sentinel-2 bathymetry with multi-temporal morphometric indicators to characterize the vertical and horizontal dynamics of regulated rivers jointly.

A Synergistic Approach for High-Precision Lake Turbidity Retrieval Using ICESat-2 and Sentinel-2 Data

Reliable monitoring of lake turbidity is often constrained by the trade-off between the spatial coverage of passive optical imagery and the vertical profiling capability of active LiDAR observations. This study proposes a multi-source retrieval framework integrating ICESat-2 ATL03 photon data with Sentinel-2 multispectral imagery for large-scale turbidity mapping in Lake Erie. An adaptive quadtree pruning strategy combined with Otsu thresholding was applied to isolate high-confidence surface water photons. Vertical distribution descriptors, including penetration depth and attenuation-related photon metrics, were quantified along 500-m segments. A Random Forest inversion model was established using these LiDAR-derived features, achieving an RMSE of 2.67 NTU. To overcome the spatial discontinuity of ICESat-2 tracks, the LiDAR-derived turbidity estimates were incorporated as virtual buoy constraints to calibrate temporally matched Sentinel-2 reflectance products. A Bayesian-optimized fusion framework was subsequently developed to generate spatially continuous turbidity fields. Validation results indicate that the synergistic model achieved an RMSE of 2.95 NTU, representing a 39% improvement over conventional optical-only retrieval methods. The proposed framework demonstrates the potential of cross-modal remote sensing synergy for large-scale inland water quality monitoring.

Reservoir Water Quality Inversion During Dredging Based on Sentinel-2 Imagery