Evaluating Consistency and Accuracy of Public Tidal Flat Datasets in China’s Coastal Zone

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.