A hierarchical framework for cropland and crop classification in the Qingtongxia irrigation district using support vector machine and random forest models.

Xiaoqian Xu, Wen Wang, Houjun He, Xiaoqiang Zhang

Abstract read

In one paragraph

Article in Scientific reports, 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

4 authors.

Xiaoqian XuState Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, 210098, China.

Wen WangState Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, 210098, China. wangwen@hhu.edu.cn.

Houjun HeInformation Center, Yellow River Conservancy Commission of the Ministry of Water Resource, Zhengzhou, 450004, China.

Xiaoqiang ZhangState Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, 210098, China.

Funding

National Key Research and Development Program of China No. 2023YFC3209201

6 · The paper itself

Abstract

Accurate classification of cropland and crop types is essential for agricultural management in arid irrigation districts but remains challenging due to spectral similarity among crops and interference from heterogeneous non-cropland backgrounds. To address these challenges, this study develops a hierarchical framework for cropland and crop-type mapping using Sentinel-2 time-series data on the Google Earth Engine platform. A two-stage strategy is adopted, in which cropland is first delineated using a support vector machine with a radial basis function kernel (SVM

Indexed as

Crop mappingHierarchical classificationIrrigation districtPhenologySentinel-2 time series

Identifiers

PMID42156889

DOI10.1038/s41598-026-53203-1

PMCPMC13385921

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.