在非洲用于作物类型分类的光学和雷达图像的使用:一篇评论
Maryam Choukri1, Ahmed Laamrani1,2,3, Abdelghani Chehbouni1,2
1Center for Remote Sensing Applications (CRSA), UM6P, Benguerir 43150, Morocco.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
结合光学和合成光圈雷达 (SAR) 卫星数据,尽管面临挑战,但改善了非洲作物监测. 本综述详细介绍了准确作物绘图和可持续农业的方法,局限性和未来方向.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 地理空间分析的研究.
背景情况:
- 在非洲,农业监测面临诸如小规模农业,多种作物和云层覆盖等挑战.
- 多源遥感对于农业评估至关重要,但需要克服这些障碍.
- 结合光学和合成光圈雷达 (SAR) 数据,为提高精度提供了一个有希望的解决方案.
研究的目的:
- 审查非洲农业监测和绘图方面的挑战.
- 用光学和雷达卫星评估农业监测进展情况.
- 在非洲探索用于作物类型分类的数据融合技术.
主要方法:
- 对用于作物监测的光学和SAR遥感进行广泛的文献综述.
- 分析数据组合技术及其在非洲环境中的适用性.
- 评估用于作物分类的机器学习算法.
主要成果:
- 光学数据提供高分辨率;SAR数据穿透云层,对热带地区至关重要.
- 光学和SAR数据的整合技术显示出潜力,但面临局限性 (数据可用性,地面真相).
- 先进的机器学习模型可以提高作物分类的准确性和自动化.
结论:
- 结合光学和SAR数据对于在非洲准确地绘制作物地图至关重要.
- 开发强大,可扩展的模型对于解决农业多样性至关重要.
- 这种方法支持可持续农业,粮食安全和社会经济发展.
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