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通过无人机图像与光合作用积累模型对大米地面生物质的估计

Kaili Yang¹, Jiacai Mo¹, Shanjun Luo¹

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.

Plant phenomics (Washington, D.C.)

|June 5, 2023

概括

准确的米地面生物量 (AGB) 估计对于作物管理至关重要. 新的模型整合了来自无人机的植被指数和树冠高度,在整个生长季节显著提高了AGB估计的准确性.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
植物生理学植物生理学

背景情况:

准确估计地表生物量 (AGB) 对于评估作物生长和实施特定地点的管理策略至关重要.
米AGB积累主要是由绿叶中的光合作用活动驱动的.

研究的目的:

开发和比较使用无人机 (UAV) 数据估计大米AGB的新型模型.
评估整合植被指数 (VI) 和树冠高度在整个大米生长季节的AGB估计的有效性.

主要方法:

提出了光合作用积累模型 (PAM) 和简化光合作用积累模型 (SPAM).
利用无人机获取的图像来得出植被指数 (例如,NDVI) 和树冠高度数据.
综合VI和树冠高度数据以估计不同生长阶段的米AGB.

主要成果:

传统的NDVI和树冠高度模型显示整个赛季AGB估计的准确性有限.
将NDVI和顶高度结合在一起的PAM显著提高了AGB估计准确度 (R2 = 0.95,RMSE = 136.81 g/m2).
在较少的观测中,SPAM模型实现了高精度 (R2 > 0.8),证明了多年来稳定性和概括性.

结论:

在整个生长季节中,PAM和SPAM提供了高效和准确的方法,用于基于无人机的水AGB估计.
这些模型显示出大规模现场管理,精准农业和作物育种应用的巨大潜力.

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