ArticleScientific data2024
A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China.
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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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Carbon reduction multidimensional ternary model for robotic 3D printing prefabricated construction across hotels star levels.Carbon balance and management · 2026Article
- Rainfall and Temperature Explain Colony Variation in Echolocation Calls of the Intermediate Horseshoe Bats (Ecology and evolution · 2026Article
- Comparative analysis of machine learning models with SHAP interpretation for causes of highway flood-damage blocking.Scientific reports · 2026Article
- Associations of temperature, particulate matter, and greenness with human papillomavirus positivity at screening: an analysis of Guangdong Province (2016-2022) using the treed distributed lag nonlinear model.Frontiers in public health · 2026Article
- Spatiotemporal patterns of soil moisture in Shandong Province, China: An analysis using ERA5-Land datasets.PloS one · 2026Article
- Spatio-Temporal Gap Filling of Sentinel-2 NDI45 Data Using a Variance-Weighted Kalman Filter and LSTM Ensemble.Sensors (Basel, Switzerland) · 2025Article
- Urban Form and Environmental Characteristics as Drivers of Air Pollution Exposure Variability and Inequality in Fujian Province, China.GeoHealth · 2025Article
- A daily gap-free normalized difference vegetation index dataset from 1981 to 2023 in China.Scientific data · 2024Article
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Authors and funding
8 authors.
Funding
Abstract
Long-term, daily, and gap-free Normalized Difference Vegetation Index (NDVI) is of great significance for a better Earth system observation. However, gaps and contamination are quite severe in current daily NDVI datasets. This study developed a daily 0.05° gap-free NDVI dataset from 1981-2023 in China by combining valid data identification and spatiotemporal sequence gap-filling techniques based on the National Oceanic and Atmospheric Administration daily NDVI dataset. The generated NDVI in more than 99.91% of the study area showed an absolute percent bias (|PB|) smaller than 1% compared with the original valid data, with an overall R
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Registered trials
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