Evidence map›Paper›PMID 39987271›Full record

ArticleScientific data2025

A 10-meter resolution dataset of abandoned and reclaimed cropland from 2016 to 2023 in Inner Mongolia, China.

Deji Wuyun, Liang Sun, Zhongxin Chen, Yangwei Li, Mengwei Han, Zhenxin Shi, Tingting Ren, Hongwei Zhao

Abstract read
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

8 authors.

Deji WuyunState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China.ORCID http://orcid.org/0000-0001-5890-1758
Liang SunState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China. sunliang@caas.cn.
Zhongxin ChenDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, Italy.
Yangwei LiState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China.
Mengwei HanState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China.
Zhenxin ShiState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China.
Tingting RenAsia Hub, Nanjing Agricultural University, Nanjing, 210095, China.
Hongwei ZhaoState Key Laboratory of Efficient Utilization of Arable Land in China, Chinese Academy of Agricultural Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amid growing global food security concerns and frequent armed conflicts, real-time monitoring of abandoned cropland is essential for strategic planning and crisis management. This study develops a method to map abandoned cropland accurately, crucial for maintaining the food supply chain and ecological balance. Utilizing Sentinel-1/2 satellite data, we employed multi-feature stacking and machine learning to create the ARCC10-IM (Abandoned and Reclaimed Cropland Classification at 10-meter resolution in Inner Mongolia) dataset, which tracks annual cropland activity. A novel temporal segmentation algorithm was developed to extract cropland abandonment and reclamation patterns annually, using sliding time windows over several years. This research differentiates cropland states-active cultivation, unstable fallowing, continuous abandonment, and reclamation-providing continuous, regional-scale maps with 10-meter resolution. ARCC10-IM is crucial for land planning, environmental monitoring, and agricultural management in arid areas like Inner Mongolia, enhancing decision-making and technology in land use tracking.

Identifiers

PMID39987271
PMCPMC11846897

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LicenceCC BY-NC-ND
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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.