Evidence map›Paper›PMID 41971999›Full record

ArticleiScience2026

Transferability of aboveground biomass estimation using Sentinel-1/2 and GEDI data in subtropical forests of complex terrain, China.

Guoqing Wang, Wenquan Dong, Huaiqing Zhang, Yongjie Ji, Wangfei Zhang, Han Zhao, Mengjin Wang, Yuqiao Ma, Ci Song, Ying Jiang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Guoqing WangCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.
Wenquan DongDepartment of Physical Geography and Ecosystem Science, Lund University, 223 62 Lund, Sweden.
Huaiqing ZhangInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China.
Yongjie JiCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.
Wangfei ZhangCollege of Forestry, Southwest Forestry University, Kunming 650224, China.
Han ZhaoCollege of Forestry, Southwest Forestry University, Kunming 650224, China.
Mengjin WangCollege of Forestry, Southwest Forestry University, Kunming 650224, China.
Yuqiao MaCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.
Ci SongCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.
Ying JiangCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.
Chenfu BaiCollege of Forestry, Southwest Forestry University, Kunming 650224, China.
Jing WuCollege of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate forest aboveground biomass (AGB) estimation across heterogeneous subtropical regions is essential for carbon accounting and climate change mitigation. We developed XGBoost and random forest models using GEDI L4A Lidar samples and multi-source remote sensing features (Sentinel-1/2, topography) to predict AGB in Xijiang Forest Farm (Guangdong) and transferred them to Simao District (Yunnan). XGBoost demonstrated superior performance and transferability, with parameter fine-tuning effectively adapting the source-domain model to the target region (R

Indexed as

Earth sciencesForestryRemote sensing

Identifiers

PMID41971999
PMCPMC13066810

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.