估计叶子积 考虑到垂直异质性 使用多角无人机 遥感 在小麦
Yuanyuan Pan1,2, Jingyu Li1, Jiayi Zhang1
1National Engineering and Technology Center for Information Agriculture, Engineering and Research Center of Smart Agriculture (Ministry of Education), Key Laboratory for Crop System Analysis and Decision Making (Ministry of Agriculture and Rural Affairs), Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing Agricultural University, Nanjing 210095, China.
Plant phenomics (Washington, D.C.)
|December 6, 2024
概括
准确估计小麦的叶子积 (LNA) 需要考虑树冠垂直异质性. 使用无人机和高光谱数据的新模型通过考虑不同的叶片层来改善LNA估计,优于传统方法.
科学领域:
- 农业遥感 农业遥感
- 植物生理学 植物生理学
- 精准农业 精准农业 精准农业
背景情况:
- 叶子积 (LNA) 对作物产量和质量至关重要.
- 农作物树冠中的垂直异质性使准确的LNA估计变得复杂.
- 无人驾驶飞行器 (UAV) 和高光谱数据为非破坏性LNA评估提供了潜力.
研究的目的:
- 开发和验证用于估计小麦LNA的模型,以考虑垂直树冠异质性.
- 为了比较包含垂直信息的模型与不包含垂直信息的模型的准确性.
- 确定最佳的数据采集策略和建模方法用于LNA估计.
主要方法:
- 收集无人机多光谱和近地超光谱数据在各种视角的视角.
- 将小麦树冠分为上层,中层和下层,以量化每层的LNA.
- 开发了线性回归 (LR) 和随机森林回归 (RF) 模型,包括考虑垂直异质 (LNA_Sum) 和不考虑垂直异质 (LNA_non) 的模型.
主要成果:
- 与LNA_non模型相比,采用垂直异质性 (LNA_Sum) 的模型显著提高了LNA估计的准确性.
- 最佳估计方案涉及将上,中和下叶层结合起来,使用正常化差异红边索引.
- 该RF-LNA_Sum模型实现了最高的准确性,验证相对根平均平方误差为19.3% (无人机测量) 和17.8% (模拟数据).
结论:
- 考虑垂直树冠分布对于使用遥感精确估计LNA至关重要.
- 基于无人机的高光谱和多光谱数据,当与考虑垂直异质性的模型进行分析时,为LNA评估提供了一个强大的方法.
- 开发的RF-LNA_Sum方法为小麦生产中精确管理提供了一个有前途的工具.
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