Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification

FU Qunzhong; JIA Yuhao; CHENG Feifei; XU Chunyan; XUE Jiahui; ZHANG Yalin

doi:10.11959/j.issn.2096-3750.WLW26022

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Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification

更新时间：2026-07-03

- Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification
- Chinese Journal on Internet of Things (2026)
- 作者机构：
  
  1.黄河科技学院工学部，河南郑州 450063
  2.黄河科技学院教务处，河南郑州，450063
  3.黄河科技学院医学部，河南郑州，450063
- 作者简介：
- 基金信息：
  
  National College Students' Innovative Training Program(202511834018);Industry-University-Research Innovation Fund for Chinese Universities: Subproject of New Generation Information Technology Innovation Program(2023IT043);Postgraduate Education Reform and Quality Improvement Program of Henan Province(YJS2025GZZ64);Approved Research and Practice Project for Higher Education Teaching Reform in Henan Province(2026SJGLX226);Soft Science Project of Henan Provincial Department of Science and Technology(252400410022)
- DOI：10.11959/j.issn.2096-3750.WLW26022
  CLC： TP391
- Received：15 February 2026，
  
  Revised：2026-06-18，
  
  Accepted：30 June 2026，
- 稿件说明：
移动端阅览
FU Qunzhong, JIA Yuhao, CHENG Feifei, et al. Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification[J/OL]. Chinese Journal on Internet of Things, 2026.
DOI：

FU Qunzhong, JIA Yuhao, CHENG Feifei, et al. Review on Multi-source Remote Sensing Satellite Data Fusion and Machine Learning in Crop Identification[J/OL]. Chinese Journal on Internet of Things, 2026. DOI： 10.11959/j.issn.2096-3750.WLW26022.

摘要

在全球人口增长与环境变化背景下，为提高农作物识别效率与精度，研究系统梳理了光学、雷达和高光谱等多源遥感卫星数据的特点及适用场景，探讨了辐射校正、几何处理和分辨率统一等预处理方法。重点分析了时空融合、层级融合和抗干扰融合策略，以及支持向量机、随机森林等传统机器学习方法和卷积神经网络、循环神经网络、Transformer 等深度学习模型在作物识别中的应用。结果表明，多源数据融合与机器学习结合可显著提升识别精度，时空融合增强了作物生长曲线重建能力，决策级融合使分类精度达 92% 以上，深度学习模型在复杂场景中精度超过 93%。研究指出数据质量、模型泛化能力和计算资源等仍是主要挑战，未来需加强新型融合算法、深度融合技术和模型优化研究，以推动精准农业的数字化与智能化发展。

Abstract

Under the background of global population growth and environmental change

to improve the efficiency and accuracy of crop identification

this paper systematically reviews the characteristics and application scenarios of multi-source remote sensing satellite data

including optical

radar

and hyperspectral data

and discusses preprocessing methods such as radiometric correction

geometric processing

and resolution normalization. It focuses on spatiotemporal fusion

hierarchical fusion

and anti-interference fusion strategies

as well as the application of traditional machine learning methods (e.g.

support vector machines and random forests) and deep learning models (e.g.

convolutional neural networks

recurrent neural networks

and Transformers) in crop recognition. The results show that the combination of multi-source data fusion and machine learning can significantly improve identification accuracy. Spatiotemporal fusion enhances the ability to reconstruct crop growth curves

decision-level fusion achieves classification accuracy above 92%

and deep learning models exceed 93% accuracy in complex scenarios. This paper points out that data quality

model generalization

and computational resources remain major challenges

and future research should focus on novel fusion algorithms

deep integration technologies

and model optimization to promote the digitalization and intelligent development of precision agriculture.

关键词

Keywords

references

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