精确作物映射:在植物树冠内,使用多传感器超光谱图像对作物和土壤进行区分
C V S S Manohar Kumar1, Sudhanshu Shekhar Jha2, Rama Rao Nidamanuri3
1Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Department of Space, Government of India, Thiruvananthapuram, Kerala, 695547, India.
Scientific reports
|October 22, 2024
概括
这项研究使用光谱分离与基于无人机的高光谱数据来准确区分作物和植物树冠内的土壤. 这项技术通过使作物和土壤进行详细的歧视,以改善农场管理,从而增强精准农业.
科学领域:
- 遥感 遥感 遥感 遥感
- 精准农业 精准农业 精准农业
- 频谱学是一种光谱学.
背景情况:
- 精准农业依赖于准确的作物和土壤区分,以优化资源管理.
- 现有的遥感方法主要集中在现场层面的分析上,使植物内部的树冠歧视未被探索.
- 基于无人机的高分辨率成像为详细的农业监测提供了潜力.
研究的目的:
- 评估光谱分离技术,以在植物内部的树冠层面上区分作物和土壤.
- 创建用于植物或亚植物级别歧视的基准高光谱数据集.
- 评估不同的光谱混合物建模方法和终端成员来源.
主要方法:
- 在不同海拔高度获得基于无人机的蔬菜作物的高光谱图像.
- 应用线性,非线性和稀疏的光谱除方法.
- 从现场,地面和无人机图书馆提取的光谱签名 (末端成员).
主要成果:
- 在植物内部的树冠层面上,在作物土壤歧视方面达到99-100%的准确性.
- 精度取决于端子源和无人机飞行高度的组合.
- 评估了端子来源,空间分辨率和算法对丰度估计的影响.
结论:
- 光谱分离对植物内树冠作物土壤歧视非常有效.
- 生成的超光谱数据集为开发新的遥感方法提供了宝贵的资源.
- 这项研究推进了用于精密农业应用的子天花板水平分析.
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