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Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments
Water Research ( IF 11.4 ) Pub Date : 2025-01-21 , DOI: 10.1016/j.watres.2025.123176
Chong Fang, Kaishan Song, Zhaojiang Yan, Ge Liu

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

中文翻译:


遥感监测全球内陆水域藻蓝蛋白:进展与未来发展



蓝藻水华正日益成为全球内陆水生生态系统的主要威胁。藻蓝蛋白 (PC) 是蓝藻特有的一种色素,可为蓝藻水华预警的研究提供重要参考。新的卫星技术和云计算平台极大地改善了对 PC 的研究,检查它的平均研究数量从 2018 年之前的每年 5 项增加到此后的每年 17 项。许多经验、半经验、半分析、准分析算法 (QAA) 和机器学习 (ML) 算法都是基于 PC 在大约 620 nm 处的独特吸收特性开发的。然而,大多数模型都是针对特定区域的单个湖泊或湖泊群开发的,它们在更大空间尺度上的适用性需要评估。需要回顾光学机制、不同模型类型的原理和优缺点、PC 遥感反演中主流传感器的性能优缺点,并评估全球湖泊 PC 数据集。我们检查了 1900 年至 2024 年间 Web of Science 引文数据库中的 230 篇文章,总结了其中 57 篇涉及 PC 反演模型构建的文章,并编制了一份全球 6526 个 PC 采样站点的列表。本文提出,实现全球湖泊PC遥感反演和时空演化分析的关键是充分利用现有的多源遥感大数据平台,结合ML和光学机制的深度结合,根据湖泊光学特征、富营养化水平、水深、气候类型、海拔、 流域内的人口密度。 此外,整合来自多源卫星传感器、地面观测和无人机的数据,将使全球湖泊 PC 远程估计的未来发展成为可能,并有助于实现联合国可持续发展目标内陆水域目标。
更新日期:2025-01-21
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