ArticleScientific data2024
HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Unraveling Spatiotemporal Heterogeneity and Driving Mechanism of Vegetation Recovery in Co-Seismic Landslide Areas via Nonlinear Trajectories and Bayesian Networks.Ecology and evolution · 2026Article
- A high-resolution (daily and 1 km) atmospheric moisture collection over the North China Plain during 2003-2020.Scientific data · 2026Article
- Article
- Interactions between brucellosis and environmental factors: spatiotemporal epidemiology from Xinjiang, China.BMC public health · 2025Article
- Ambient air pollution exposure in relation to cerebral small vessel disease in Chinese population: A cranial magnetic resonance imaging-based study.Eco-Environment & Health · 2025Article
- Nonlinear health benefits of public green space: evidence from a nationwide machine learning study in China.Frontiers in public health · 2025Article
- Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022.Scientific data · 2024Article
- HiMIC-Monthly: A 1 km high-resolution atmospheric moisture index collection over China, 2003-2020.Scientific data · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Near-surface atmospheric moisture is a key environmental and hydro-climatic variable that has significant implications for the natural and human systems. However, high-resolution moisture data are severely lacking for fine-scale studies. Here, we develop the first 1 km high spatial resolution dataset of monthly moisture index collection in China (HiMIC-Monthly) over a long period of 2003~2020. HiMIC-Monthly is generated by the light gradient boosting machine algorithm (LightGBM) based on observations at 2,419 weather stations and multiple covariates, including land surface temperature, vapor pressure, land cover, impervious surface proportion, population density, and topography. This collection includes six commonly used moisture indices, enabling fine-scale assessment of moisture conditions from different perspectives. Results show that the HiMIC-Monthly dataset has a good performance, with R
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