Evidence map›Paper›PMID 42459378›Full record

ArticleFrontiers in plant science2026

A multi-source remote sensing and machine learning framework for maize mapping and yield estimation in fragmented Loess gully regions.

Lei Sun, Hao Li, Shangkun Li, Shuanghui Zhao, Yanqun Zhang, Yan Mo, Baozhong Zhang, Youjie Wu

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

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0citing papers in PubMed
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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

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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

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Lei SunCollege of Water Resources & Civil Engineering, Hunan Agricultural University, Changsha, China.
Hao LiState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Shangkun LiState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Shuanghui ZhaoState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Yanqun ZhangState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Yan MoState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Baozhong ZhangState Key Laboratory of Water Cycle and Water Security, China Institute of Water Resources and Hydropower Research, Beijing, China.
Youjie WuCollege of Water Resources & Civil Engineering, Hunan Agricultural University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accuracy crop distribution mapping and reliable yield estimation are essential for overcoming fragmentation and decentralization in smallholder farming systems of the Loess Plateau gully region. Multi-source remote sensing data, ancillary datasets, and machine learning techniques were integrated to map maize distribution and estimate yield. First, Sentinel-2 temporal spectral features, vegetation indices, and topographic variables were integrated to identify the optimal maize mapping model by a comparing machine learning algorithms: Random Forest (RF), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), and Histogram-Based Gradient Boosting Decision Tree (HGBDT). Subsequently, Sentinel-2 optical data and ERA5-Land meteorological data were dynamically resampled and spatiotemporally fused. A maize yield estimation model was then developed by integrating these fused predictors with

Indexed as

crop mappingmulti-source feature fusionSHAPsmallholder farmingyield estimation

Identifiers

PMID42459378
PMCPMC13369130

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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.