Evidence map›Paper›PMID 42236490›Full record

ArticleScientific data2026

A long-term (2000-2022), high-resolution (0.005°) aboveground biomass dataset of global grasslands.

Pinzhen Wu, Qingling Sun, Daju Wang, Jiahe Si, Baolin Li, Xuetong Zhao, Siyu Zhu, Yong Li, Xiuzhi Chen, Wenping Yuan

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

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.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 authors.

Pinzhen WuGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Qingling SunGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China. sunqling@mail.sysu.edu.cn.
Daju WangGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Jiahe SiGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Baolin LiState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Xuetong ZhaoGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Siyu ZhuGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Yong LiGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Xiuzhi ChenGuangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle, Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, Guangdong, 519082, China.
Wenping YuanInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science. College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China. yuanwp@pku.edu.cn.

Funding

General Program of Guangdong Provincial Natural Science Foundation 2024A1515012731National Natural Science Foundation of China 42201059Science and Technology Program of Guangdong Province 2024B1212070012
6 · The paper itself

Abstract

Grasslands are critical to the global carbon cycle and support livestock production, yet long-term, high-resolution global datasets of grassland aboveground biomass (AGB) remain scarce, since existing products are often spatially coarse, temporally discontinuous, or regionally limited. Here, we present a global grassland AGB dataset spanning 2000-2022 at 0.005° spatial resolution. The dataset was generated by employing an optimized random forest model, which was trained on 11,265 spatially aggregated samples with global distribution. The multi-source predictors included Normalized Difference Vegetation Index (NDVI), climate variables, atmospheric CO

Indexed as

BiomassGrasslandCarbon CycleClimateRandom Forest

Identifiers

PMID42236490
PMCPMC13538673

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.