Editorial: Satellite Remote Sensing for Hydrological and Water Resource Management in Coastal Zones

Jiaqi Yao¹Di Yang²Zhen Wang³Nan Xu^4*

• ¹Tianjin Normal University, Tianjin, China
• ²University of Florida, Gainesville, United States
• ³Northwestern Polytechnical University, Xi'an, China
• ⁴Shenzhen University, Shenzhen, China

Coastal zones concentrate people, economies, and biodiversity yet face mounting pressure from climate change, pollution, and overuse (Jiang et al., 2021;Zhang et al., 2022;Hu et al., 2020). Rising seas, erosion, extreme events, hydrological shifts, and contamination make safeguarding coastal water resources both urgent and complex (Xu and Gong, 2018;Wang et al., 2024;Xu et al., 2022). Satellite remote sensing now offers a step-change in capability: SWOT and Sentinel-6 resolve water-surface elevation, tides, and long-term sea-level signals; Landsat, Sentinel-1, and Sentinel-2 capture shoreline morphology, vegetation, turbidity, and other water-quality proxies. Together these missions deliver consistent, high-resolution observations to diagnose status and trends across dynamic land-ocean interfaces (Yao et al., 2025a). When fused with machine-learning models, they can quantify processes, forecast change, and inform practical decisions-from ecosystem protection and pollution control to sustainable allocation and climate adaptation (Xu et al., 2023;Wu et al., 2023).Following rigorous peer review, six manuscripts have been selected for publication. These studies encompass urban hydrological modeling, satellite-derived bathymetry, wetland ecosystem assessment, water quality parameter inversion, and hyperspectral image processing, investigating diverse environments from urban campuses and coastal waters to wetland ecosystems and specialized hyperspectral datasets. This Editorial provides an overview of the academic contributions from the six papers. Ecological and environmental monitoring underpins progress toward sustainability, and the fusion of satellite remote sensing with artificial intelligence is transforming both process understanding and evidence-based management. Against this backdrop, Manh Duy Tran and colleagues addressed the long-standing difficulty of estimating chlorophyll-a across optically diverse waterswhere classic blue/green and red/NIR algorithms often failby proposing the CONNECT framework, which first classifies optical water types (OWTs) and then applies matched bio-optical models (Tran et al., 2025). Their implementation uses two tailored multilayer perceptrons (NN-Clear and NN-Turbid) to capture spectralbiogeochemical relations under contrasting conditions, and benchmarking confirms robust performance for both turbid and clear waters. Complementing this water-quality focus, Ang Yue's team evaluated four core ecosystem functions across four wetland nature reserves in Tianjin (2000 -2020) with the InVEST model, mapping spatiotemporal dynamics of key ecological drivers and, via an ETSM analysis, quantifying trade-offs and synergies among services (Yue et al., 2025a). They found pronounced functional contrasts among management zones, with core and buffer areas consistently outperforming experimental zones, and complex, period-dependent interactions among servicesinsights that directly inform zoning and restoration priorities. In a second study, Ang Yue et al.coupled Land Expansion Analysis Strategy and a Cellular Automata module within the PLUS framework to link remotely sensed land-use change (2000 -2020) to socioeconomic and accessibility drivers (population, GDP, road distance). Using an equivalent-factor approach, they estimated ecosystem service value (ESV) and projected its evolution under natural growth, economy-oriented, and eco-protection scenarios, thereby revealing how alternative development pathways reshape wetland services (Yue et al., 2025b). Shifting to coastal bathymetry, Qingkai Lu' s group examined how water-quality optical properties modulate the accuracy of satellite laser altimetry in Oʻahu's nearshore waters (Lu et al., 2025). Working from ICESat-2 ATL03 photons, they extracted valid subsurface returns with an Adaptive Elevation Difference Threshold Algorithm, downscaled MODIS water-quality fields using Random Forest to improve spatial congruence, and derived an empirical link between optical conditions and ICESat-2 depth errorsoffering a practical route to error prediction and correction. Finally, to improve urban hydrologic modeling with SWMM, Zixin Yang and coauthors developed an intelligent calibration pipeline that integrates Latin Hypercube Sampling, Self-Organizing Maps, SA-BP sensitivity analysis, and GLUE uncertainty quantification (Yang et al., 2025). Applied to two 2023 storm events on the central campus of Jilin University, the approach tightened parameter ranges for nine key variables, reduced predictive uncertainty, identified a 10% imperviousness threshold for runoff-generation regime shifts, and clarified dominant controls across imperviousness scenarios-advancing flood modeling, drainage design, and risk-informed decision-making.