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Related Experiment Videos

A method for improving winter wheat mapping accuracy based on multi-temporal feature fusion and stacking ensemble

Wancheng Tao1,2, Yuting Shao3, Shuxian Ren3

  • 1School of Civil Engineering and Architecture, Suqian University, Suqian, China. 19164@squ.edu.cn.

Scientific Reports
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a new remote sensing method for accurate winter wheat mapping in China. The approach integrates multi-temporal features and stacked ensemble learning, achieving high accuracy in complex agricultural areas.

Keywords:
Ensemble learningMulti-dimensional featureTime-series imageryWinter wheat

Related Experiment Videos

Area of Science:

  • Agricultural remote sensing
  • Geospatial data analysis
  • Machine learning in agriculture

Background:

  • Accurate winter wheat mapping is crucial for China's food security and agricultural management.
  • Heterogeneous and fragmented landscapes in regions like Jiangsu Province pose challenges for conventional remote sensing classification.
  • Existing methods often suffer from insufficient feature representation and limited discriminative power, leading to suboptimal mapping accuracy.

Purpose of the Study:

  • To develop a high-accuracy winter wheat mapping framework for complex agricultural landscapes.
  • To integrate multi-temporal feature fusion and stacked ensemble learning for improved classification performance.
  • To provide a robust and scalable solution for regional crop monitoring and cultivated land management.

Main Methods:

  • Utilized Sentinel-2 time-series imagery as the primary data source.
  • Applied Savitzky-Golay filtering for temporal profile reconstruction and noise reduction.
  • Constructed a multi-dimensional feature set including spectral bands, indices, and texture metrics.
  • Implemented a stacked ensemble learning architecture with Random Forest, SVM, CART, and GTB as base classifiers, optimized by a meta-learner.

Main Results:

  • The integrated feature fusion strategy significantly enhanced classification performance over single-feature approaches.
  • The optimized stacked model achieved an Overall Accuracy (OA) of 94.74% and a Kappa coefficient of 0.9283, outperforming individual classifiers.
  • Generated winter wheat distribution maps for 2021-2023 showed high consistency with statistical data, demonstrating temporal stability and transferability (OA > 94.7%, Kappa > 0.92).

Conclusions:

  • The developed framework offers a robust and scalable remote sensing approach for winter wheat identification in complex agricultural landscapes.
  • The integration of multi-temporal features and stacked ensemble learning effectively addresses limitations of conventional methods.
  • This study provides valuable methodological support for national food security assessment, dynamic cultivated land management, and agricultural regulation.