Evidence map›Paper›PMID 41238553›Full record

ArticleScientific data2025

A parcel-level cropland dataset with field shape indicators for Xizang (Tibet) from VHR remote sensing.

Chuya Wang, Xiaoyan Wang, Yi Jiang, Shuaijun Liu, Fangyuan Jin, Zhangcheng Li, Lai Wei, Xiuchun Dong, Yuqiao Long, Si Wang and 3 more

Abstract read
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

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

13 authors.

Chuya WangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Xiaoyan WangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Yi JiangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Shuaijun LiuSchool of Emergency Management, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
Fangyuan JinState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Zhangcheng LiInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Lai WeiInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Xiuchun DongInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China. dongxiuchun_irsa@scsaas.cn.
Yuqiao LongInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China. longyuqiao_irsa@scsaas.cn.
Si WangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Ping HuangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Yimei WangInstitute of Remote Sensing and Digital Agriculture, Sichuan Academy of Agricultural Sciences, Chengdu, 610066, China.
Jin ChenState Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-resolution cropland data are essential for regional agricultural research, particularly in Xizang, China, where fragmented landscapes and complex cropping systems require accurate monitoring of crop distribution, yield estimation, and land management planning. This study presents the first parcel-level cropland dataset for Xizang, generated by a deep learning-based field extraction model and high-resolution Google Earth imagery. Field validation of the dataset with 155 UAV samples showed 90% area matching accuracy, and cross-comparison with two existing cropland products of Xizang also showed strong linear correlations (R

Identifiers

PMID41238553
PMCPMC12618508

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.