Evidence map›Paper›PMID 42049765›Full record

ArticleScientific data2026

National 10-m soybean maps for South Africa from 2018 to 2025.

Xin Huang, Anton Vrieling, Yue Dou, Francis Muthoni, Adolph Nyamugama, Adeniyi Adeyemi, Terry Newby, Andrew Nelson

Abstract read
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

8 authors.

Xin HuangFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE, Enschede, the Netherlands. x.huang-2@utwente.nl.
Anton VrielingFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE, Enschede, the Netherlands.
Yue DouFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE, Enschede, the Netherlands.
Francis MuthoniFarming System Agronomy Program, International Institute of Tropical Agriculture, Box, 30772-00100, Nairobi, Kenya.
Adolph NyamugamaAgricultural Research Council-Natural Resource & Engineering (ARC-NRE), 600 Belvedere street-Arcadia, Pretoria, Republic of South Africa.
Adeniyi AdeyemiGeoTerraImage (Pty) Ltd., Building C (Ground Floor), Agri Hub Business Park, 477 Witherite Street, 0184, Pretoria, South Africa.
Terry NewbyGeoTerraImage (Pty) Ltd., Building C (Ground Floor), Agri Hub Business Park, 477 Witherite Street, 0184, Pretoria, South Africa.
Andrew NelsonFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE, Enschede, the Netherlands.

Funding

China Scholarship Council 202206040020
6 · The paper itself

Abstract

Accurate and spatially detailed information on soybean cultivation is essential for production estimation and land-use management, particularly as cultivated areas expand in emerging producing regions such as South Africa. This research presents SoySA10, a national 10-m soybean mapping dataset for South Africa covering 2018-2025. The dataset was generated from Sentinel-2 time series using a generalised classification model - combining random forest with dynamic time warping - with spectral-phenological features. SoySA10 includes time-series soybean distribution maps, field type maps, and a soy-cultivation change map. Accuracy assessment showed an overall accuracy of 0.89, with F1-scores of 0.66 for soybean and 0.94 for non-soybean classes. Comparisons between mapped and reported soybean areas exhibited high consistency at provincial (R

Indexed as

Glycine maxAgricultureDatasets as TopicRandom ForestSouth Africa

Identifiers

PMID42049765
PMCPMC13328755

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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.