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在基于光学分类的内陆水域中远程估计菲科亚宁度

Lili Lyu¹, Kaishan Song², Zhidan Wen³

¹Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; Jilin Jianzhu University, Changchun, China.

The Science of the total environment

|August 20, 2023

概括

通过新的遥感框架,提高了水中精确的植物 (PC) 估计. 这种方法对水类进行分类,增强了蓝藻细菌繁殖的检测和早期预警系统.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
水生生态学水生生态学

背景情况:

蓝藻细菌的繁荣威胁到水生生态系统,原因是气候变化和人类活动.
物理素 (PC) 是一种关键的颜料,用于检测和警告蓝藻细菌的开花.
使用远程传感在水中估计PC度是具有挑战性的,因为光学复杂性.

研究的目的:

开发和验证一个遥感框架,用于在光学复杂的内陆水域中准确的PC估计.
改进对蓝藻细菌繁殖的早期预警系统.

主要方法:

从中国的湖泊和水库 (2020-2022) 收集了640个现场色素度和海洋和陆地色彩仪器 (OLCI) 反射率数据.
开发了一种使用遥感反射率 (Rrs) 基线高度在560,647和709 nm的水光学分类算法.
在五种分类水类中比较了三种候选算法 (基线高度,带比,三带).

主要成果:

与未分类水相比,光学分类框架显著提高了PC估计的准确性 (RMSE = 72.6 μg L-1,MAPE = 80.4%),特别是在PC度低的情况下.
带比算法显示在中等水和清水中具有广泛的适用性.
三带和线高度算法适用于中等水和高PC水分别.
分类水类与PC/Chla比率相关,使得可以评估蓝藻细菌的风险.

结论:

拟议的框架提高了在光学复杂的内陆水域中PC估计的准确性.
这项研究为内陆水生环境中的水质逆转和蓝藻细菌繁殖监测提供了一个新的策略.

关键词:

在内陆水域,内陆水域.在OLCII中,您可以使用OLCI.水的光学分类水的光学分类菲科亚尼尼的成分是什么?遥感是一种远程传感.

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