Evidence map›Paper›PMID 39865066›Full record

ArticleScientific data2025

Long-term reconstructed vegetation index dataset in China from fused MODIS and Landsat data.

Xiangqian Li, Qiongyan Peng, Ruoque Shen, Wenfang Xu, Zhangcai Qin, Shangrong Lin, Si Ha, Dongdong Kong, Wenping Yuan

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

9 authors.

Xiangqian Li *College of Science, Shihezi University, Shihezi, 832000, Xinjiang, China.
Qiongyan Peng *International Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 510245, Guangdong, China.ORCID http://orcid.org/0009-0000-6258-6674
Ruoque ShenInternational Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 510245, Guangdong, China.ORCID http://orcid.org/0000-0002-4408-829X
Wenfang XuKey Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, Guangdong, China.
Zhangcai QinInternational Research Center of Big Data for Sustainable Development Goals, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, 510245, Guangdong, China.ORCID http://orcid.org/0000-0001-9414-4854
Shangrong LinCarbon-Water Research Station in Karst Regions of Northern Guangdong, School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510006, Guangdong, China.ORCID http://orcid.org/0000-0001-7153-2184
Si HaNational Climate Center, China Meteorological Administration, Beijing, 100081, China.
Dongdong KongSchool of Environmental Studies, China University of Geosciences, Wuhan, 430074, Hubei, China.
Wenping YuanInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100091, China. yuanwp@pku.edu.cn.ORCID http://orcid.org/0000-0002-1469-4395

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The vegetation index is a key satellite-based variable used to monitor global vegetation distribution and growth. However, existing vegetation index datasets face limitations in achieving both high spatial and temporal resolution, restricting their application potential. This study revised a machine learning spatiotemporal fusion model (InENVI) to produce a high-resolution NDVI dataset with 8-day temporal and 30 m spatial resolution, covering China from 2001 to 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality and accuracy. The dataset was validated using 255,000 samples across 6 geographical regions, showing strong performance in capturing spatiotemporal NDVI variations. Additionally, the dataset effectively addresses Scan Line Corrector-off stripes in Landsat 7 imagery. This dataset enables reliable annual NDVI estimates for China at a 30-m resolution and is available for reuse through an open data repository.

Identifiers

PMID39865066
PMCPMC11770109

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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.