Data · dataset · 2026
10m bi-annual spatial distribution dataset of China's tidal flats (2016-2023)
Listed in ScienceDB
Tidal flats are highly valuable coastal ecosystems.
Description
However, large-scale, high-precision, and long-term remote sensing mapping of tidal flats faces tremendous challenges due to periodic tidal inundation and frequent cloud cover. This dataset provides a high-precision (10-m) bi-annual spatial distribution map of China's tidal flats from 2016 to 2023.
Generated on the Google Earth Engine (GEE) cloud platform, the dataset utilizes high-frequency Sentinel-2 time-series imagery covering the coastal zone of China. Methodologically, a novel tide-and-phenology-aware time-series feature reconstruction framework was applied. By employing the Harmonic Analysis of Time Series (HANTS) algorithm to precisely fit the temporal trajectories of MNDWI and NDVI, the severe interferences from instantaneous highly turbid waters and saltmarsh vegetation phenology were successfully decoupled.
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The extracted robust time-series spectral fingerprints were then fed into a Random Forest classifier for pixel-wise identification.The dataset covers four bi-annual periods (2016-2017, 2018-2019, 2020-2021, and 2022-2023). Extensive evaluations based on large-scale independent validation samples (401,829 points) demonstrate that the dataset achieves an Overall Accuracy of 99.00% and an F1 score of 0.97, exhibiting exceptional mapping robustness in extremely turbid estuaries and complex mosaic habitats.
Overcoming the omission defects of traditional static compositing methods in extracting low-tide flats, this dataset provides fundamental and highly reliable spatial data support for coastal ecological protection and restoration, natural resource inventories, nearshore hydrodynamic modeling, and the assessment of Sustainable Development Goals (SDGs).
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.36636 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
- Inferred from text
- Satellite remote sensing 65%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.36636 | 9 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:remote-sensing | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |