基于遥感数据的玉米粒质量的动态监测
Weiwei Sun1, Qijin He1,2, Jiahong Liu1
1College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Frontiers in plant science
|July 10, 2023
概括
遥感通过整合超光谱和气象数据,准确预测夏季玉米的质量,包括粉和油含量. 层次线性建模显著提高了比传统方法更准确的预测准确性.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 遥感对于监测作物发育和产量至关重要.
- 精确监测作物质量特征,如谷物粉和油含量,仍然是一个挑战,特别是考虑到气象因素.
研究的目的:
- 开发一个可扩展的模型来预测夏季玉米的质量 (粉,蛋白质,油含量).
- 通过整合超光谱和气象数据,提高作物质量监测的准确性.
- 评估不同生长阶段和气象因素对预测准确性的影响.
主要方法:
- 实地实验是从2018年到2020年进行的,种植时间不同.
- 使用了分层线性建模 (HLM),结合了超光谱和气象数据.
- 模型性能与多重线性回归 (MLR) 进行了比较,使用植被指数 (VIs).
主要成果:
- HLM显著超过了MLR,实现了高的预测准确度 (例如,谷物粉含量R2=0.90).
- 整合来自,谷物填充和成熟阶段的数据进一步增强了对粉含量的预测 (R2=0.96).
- 发现气象因素,特别是降水,对谷物质量监测有很大影响.
结论:
- 开发的HLM模型为年度和年间作物质量预测提供了可靠的方法.
- 将遥感数据与气象因素和特定生长阶段相结合,可以提高预测准确度.
- 这项研究提供了一种新的方法来加强基于遥感的作物质量监测.
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