Remote Sensing Inversion of Full-Profile Topography Data for Coastal Wetlands Using Synergistic Multi-Platform Sensors from Space, Air, and Ground.

Jiabao Zhang, Jin Wang, Yu Dai, Yiyang Miao, Huan Li

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jiabao ZhangCollege of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China.ORCID 0009-0006-6719-1544

Jin WangSuzhou Water Conservancy Project Administration, Suzhou 215004, China.

Yu DaiNanjing Hezhen Lianxing Geographic Information Technology Co., Ltd., Nanjing 210017, China.

Yiyang MiaoCollege of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China.

Huan LiCollege of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China.

Funding

Fundamental Research Funds for the Central Universities BB250203014Key Program of the NSFC-UNEP International Science Cooperation Program for Sustainable De-velopment 42361144873National Key R&D Program of China 2024YFE0103100National Natural Science Foundation of China 42476157

6 · The paper itself

Abstract

This study proposes a "zonal inversion-fusion mosaicking" technical framework to address the challenge of acquiring continuous full-profile topography data in coastal wetland intertidal zones. The framework integrates and synergistically analyzes data from multi-platform sensors, including satellite, unmanned aerial vehicle (UAV), and ground-based instruments. Applied to the Min River Estuary wetland, this framework employs zone-specific optimization strategies: in the inundated zone, the topography was inverted using Landsat-9 OLI imagery and a Random Forest algorithm (R

Indexed as

Digital Elevation Model (DEM)remote sensingsynergistic multi-sensor datatidal Flattopographic inversionzonal fusion

Identifiers

PMID41471401

DOI10.3390/s25247405

PMCPMC12736605

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.