基于无人机超光谱遥感和WOFOST的结合,研究耕阶段的受精决策方法
Shilong Li1,2, Zhongyu Jin1,2, Juchi Bai1,2
1College of Information and Electrical Engineering, Shenyang Agricultural University, Shenyang, China.
Frontiers in plant science
|June 24, 2024
概括
这项研究将无人机遥感与WOFOST作物模型相结合,用于精确地在中施肥米. 数据同化减少了5.9%的肥料使用,同时保持了产量.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 作物建模作物建模
背景情况:
- 传统的肥依赖于农民的经验,缺乏准确性和适应米需求的适应性.
- 目前的农业无人机可以监测大米,但不能直接指导施肥决策.
- 在耕作阶段准确的气应用对于稳定大规模生产中的大米产量至关重要.
研究的目的:
- 开发精确的肥应用决策方法,用于在耕作阶段的米.
- 通过使用无人机高光谱数据,增强WOFOST作物生长模型的空间异质性模拟.
- 优化化肥的应用,以获得稳定的水产量.
主要方法:
- 利用无人机遥感来获取大米树冠的高光谱数据.
- 使用连续投影算法提取了超谱特征,并使用极端学习机器构建了一个生物质反转模型.
- 通过使用集成卡尔曼波器 (ENKF) 和四维变量方法进行同化反向生物质和局部WOFOST模型.
- 开发了基于WOFOST模型模拟的受精处方图,用于精确的应用.
主要成果:
- 米生物质的高光谱倒置模型实现了高精度 (训练集R=0.953,测试集R=0.914).
- 与同等产量的标准方案相比,ENKF同化方法导致肥减少5.9%.
- 数据同化纠正了WOFOST模型模拟,提高了其现场级准确性.
结论:
- 数据同化显著提高了无人机遥感在作物管理中的合理性和准确性.
- 这种方法为数据驱动的肥决策提供了一个新的理论基础.
- 综合方法使得精确的化肥应用成为可能,有助于稳定的米产量和潜在的减少环境影响.
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