ArticleScientific data2026
TSA-MC v1.0: A 30-m dataset of true surface area for China's mountains to support SDG 15.4 indicators monitoring.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- TSA-MC v1.0: A 30-m dataset of true surface area for China's mountains to support SDG 15.4 indicators monitoring.Scientific data · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Traditional two-dimensional Projected Area (PA) calculations systematically underestimate area of mountain ecosystems, introducing significant uncertainty into resource management and sustainable development assessments. To address this critical data gap, we present the True Surface Area of China's Mountains (TSA-MC v1.0), the first nationwide, 30-m resolution dataset providing a physically realistic three-dimensional land surface measure. Developed from ASTER GDEM V2 using a robust pixel decomposition algorithm, its reliability was confirmed through rigorous validation of its physical accuracy (e.g., TSA/PA ratio-slope correlation, ρ = 0.3917) and geomorphic consistency. The dataset, by accounting for three-dimensional topography, quantifies an additional 582,000 km² of surface area compared to traditional projected-area measurements. Consequently, this revision demonstrates that mountainous surface terrain is underestimated in previous assessments, with the updated estimate (67.25%, TSA-based) exceeding the Digital Map of China's Mountains projection (64.9%, PA-based) by 2.35%. This foundational data layer enables more accurate area-dependent applications, including carbon stock accounting, hydrological modeling, habitat analysis, and the monitoring of SDGs. The complete dataset is openly available via the Zenodo repository to support further research.
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Registered trials
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.