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研究无人机遥感数据和水产作物模型的同化

Wei Li^1,2, Manpeng Li¹, Muhammad Awais^1,2

¹National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

这项研究通过将无人机 (UAV) 多光谱数据与使用粒子群优化的 AquaCrop 模型集成,提高了作物模型的准确性. 这提高了茶园的大规模模拟准确度.

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:

由于复杂性和不确定性,像AquaCrop这样的作物生长模型仅限于单点模拟.
扩大作物模型应用的规模需要整合现实世界的数据以提高准确性.

研究的目的:

为了提高茶园的AquaCrop模型的空间适用性.
开发一种同化方法,将无人机衍生的树冠覆盖面与AquaCrop模型相结合.
为了评估新的AquaCrop-PSO模型的提高准确性.

主要方法:

使用无人驾驶飞行器 (UAV) 多光谱技术覆盖倒置茶园树冠.
采用粒子群优化 (PSO) 来将树冠覆盖数据同化为AquaCrop模型,创建AquaCrop-PSO模型.
与本地化模型模拟和测量数据对比验证的模拟结果.

主要成果:

在植被指数和树冠覆盖面之间发现了显著的回归关系,OSAVI显示了高的反转精度 (R2=0.855).
在模拟茶叶产量 (R2=0.927) 和树冠覆盖面 (R2>0.9) 方面,AquaCrop-PSO模型实现了高精度.
与局部化模型相比,模拟精度提高了大约19.8%.

结论:

无人机遥感对于检索作物参数,如树冠覆盖率,是有效的.
从无人机获得的数据的同化显著提高了作物模型的准确性和空间适用性.
AquaCrop-PSO模型为茶园产量和树冠覆盖率模拟提供了更准确的方法.

关键词:

在AquaCrop的作物模型中,AquaCrop的作物模型.无人机远程传感无人机远程传感树冠覆盖范围的覆盖范围参数同化方式参数同化

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