Choukri Maryam, Laamrani Ahmed, Chehbouni Abdelghani
Center for Remote Sensing Applications (CRSA), UM6P, Benguerir 43150, Morocco.
College Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Benguerir 43150, Morocco.
Sensors (Basel). 2024 Jun 3;24(11):3618. doi: 10.3390/s24113618.
Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent.
多源遥感获取的作物信息对农业监测、评估和管理具有重要意义。在非洲，一些挑战（如与多种作物类型和农业系统复杂性相关的小规模耕作方式，以及生长季节的云层覆盖）会阻碍利用多源遥感进行农业监测。光学遥感和合成孔径雷达（SAR）数据的结合已成为提高作物类型制图和监测精度及可靠性的合适策略。这项工作旨在详细全面地回顾非洲农业监测和制图的挑战，以及基于光学和雷达卫星的农业监测的当前研究进展。在这种情况下，光学数据可提供高空间分辨率和详细的光谱信息，这使得能够根据不同作物类型的光谱特征对其进行区分。然而，合成孔径雷达（SAR）卫星能够穿透云层，这一点与光学数据不同，尤其在非洲热带地区，该技术能发挥重要作用。本综述探讨了用于整合光学和SAR数据进行作物类型分类的各种组合技术，以及它们在非洲国家背景下的适用性和局限性。此外，本综述还讨论了相关挑战以及光学和SAR数据组合存在的局限性，如数据可用性、传感器兼容性，以及模型训练和验证所需的准确地面真值数据。本研究还强调了先进建模（即机器学习算法，如支持向量机、随机森林和卷积神经网络）在提高使用组合数据进行作物类型分类的准确性和自动化方面的潜力。最后，本综述总结了未来的研究方向以及在非洲农业系统作物类型分类中利用光学和SAR数据组合技术的建议。此外，它强调了开发强大且可扩展的分类模型的重要性，这些模型能够适应非洲普遍存在的作物类型、耕作方式和环境条件的多样性。通过利用组合遥感技术，可以做出明智的决策，以支持可持续农业实践、加强营养安全并促进非洲大陆的社会经济发展。
