As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China.

Tidal flats, as critical transitional ecosystems between land and sea, face significant threats from climate change and human activities, necessitating accurate monitoring for conservation and management. However, publicly available tidal flat datasets exhibit substantial discrepancies due to variations in data sources, spectral indices, and classification methods. This study systematically evaluates six widely used 2020 tidal flat datasets (GTF30, GWL_FCS30, MTWM-TP, DCTF, CTF, and TFMC) across China’s coastal zone, assessing their spatial consistency, area estimation differences, and edge classification accuracy. Using a novel edge validation point set (3150 samples) derived from tide gauge stations and low-tide imagery, we demonstrate that MTWM-TP (OA = 0.85) and TFMC (OA = 0.84) achieve the highest accuracy, while DCTF and GTF30 show systematic underestimation and overestimation, respectively. Spatial agreement is strongest in Jiangsu (49.8% unanimous pixels) but weak in turbid estuaries (e.g., Zhejiang). Key methodological divergences include sensor resolution (Sentinel-2 outperforms Landsat in low-tide coverage), spectral index selection (mNDWI reduces false positives in turbid waters), and boundary constraints (high-tide masks suppress inland misclassification). We propose establishing an automated multi-source framework integrating optical (Sentinel-2, Landsat) and radar (Sentinel-1) observation data to enhance low-tide coverage, constructing region-adaptive spectral indices and improving boundary accuracy through the combination of machine learning and thresholding algorithms. This study provides a critical benchmark for dataset selection and methodological advancements in coastal remote sensing.

Tidal flats, as critical transitional ecosystems between land and sea, face significant threats from climate change and human activities, necessitating accurate monitoring for conservation and management. However, publicly available tidal flat datasets exhibit substantial discrepancies due to variations in data sources, spectral indices, and classification methods. This study systematically evaluates six widely used 2020 tidal flat datasets (GTF30, GWL_FCS30, MTWM-TP, DCTF, CTF, and TFMC) across China’s coastal zone, assessing their spatial consistency, area estimation differences, and edge classification accuracy. Using a novel edge validation point set (2,550 samples) derived from tide gauge stations and low-tide imagery, we demonstrate that MTWM-TP (OA=0.83) and TFMC (OA=0.81) achieve the highest accuracy, while DCTF and GTF30 show systematic underestimation and overestimation, respectively. Spatial agreement is strongest in Jiangsu (49.8% unanimous pixels) but weak in turbid estuaries (e.g., Zhejiang). Key methodological divergences include sensor resolution (Sentinel-2 outperforms Landsat in low-tide coverage), spectral index selection (mNDWI reduces false positives in turbid waters), and boundary constraints (high-tide masks suppress inland misclassification). We recommend integrating multi-sensor data, dynamic index selection, and optimized thresholding algorithms to improve future tidal flat mapping. This study provides a critical benchmark for dataset selection and methodological advancements in coastal remote sensing.

Accurate mapping of coastal wetland vegetation is essential for ecological conservation and blue carbon assessment, yet it remains challenging due to fragmented boundaries, spectral similarity among species, pronounced phenological variability, and limited generalization under small-sample conditions. We propose a spatio-temporal attention hybrid network (STAH-Net) for multi-source time-series classification. A 29-band feature cube is constructed from Sentinel-1 and Sentinel-2 data, integrating monthly Normalized Difference Vegetation Index (NDVI) and vertical–vertical (VV) polarization time series with static spectral and polarimetric indices. An improved U-Net backbone with dual-branch 3D convolutions extracts optical and Synthetic Aperture Radar (SAR) temporal features in parallel, incorporating a Temporal Enhancement Attention Module (TEAM) for phenological weighting, a Local Channel Attention (LCA) module for channel recalibration, and Atrous Spatial Pyramid Pooling (ASPP) for multi-scale context. An edge-weighted hybrid loss combining edge-weighted cross-entropy and Dice loss mitigates class imbalance and boundary blurring. Experiments in Hangzhou Bay, Liaohe Estuary, and Minjiang Estuary achieves overall accuracy (OA) values of 98.16%, 97.23%, and 95.76%, respectively, with a maximum Kappa of 97.49%, outperforming ConvLSTM, U-Net, SegFormer, SITSMamba, and GLMambaNet under identical settings. Ablation and visual analyses confirm the effectiveness of the proposed modules, with NDVI phenology providing primary discrimination and VV offering complementary structural information. Cross-year and cross-region tests show promising transferability, with OAs of 97.21% and 94.75% in Liaohe Estuary for 2024 and 2020, respectively, and 93.83% in Shanghai. These results demonstrate that STAH-Net provides an effective and transferable workflow for coastal wetland vegetation mapping.