首页 >  2022, Vol. 26, Issue (1) : 32-48

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DOI:

10.11834/jrs.20221276

收稿日期:

2021-04-30

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内陆水体藻蓝蛋白遥感反演研究进展
吕丽丽1,2,宋开山1,3,刘阁1,温志丹1,尚盈辛1,李思佳1
1.中国科学院东北地理与农业生态研究所遥感中心,长春 130102;2.中国科学院大学, 北京 100049;3.聊城大学 环境与规划学院, 聊城 252000
摘要:

藻蓝蛋白(PC),作为蓝藻的标志性色素,通常被用作进行蓝藻水华遥感监测的标志物。近年来,内陆水体水质恶化,富营养化加剧,藻华频发,PC遥感反演研究越来越受到关注。本文从PC光学特性、反演算法开发、卫星传感器应用等方面难点及干扰因素,综合梳理了过去30 a PC遥感反演研究的发展历程和趋势,以期理解国内外相关研究的新思路和新方法,掌握未来PC遥感反演发展研究的新动态,为水质和水资源的监管提供数据和理论基础。过去的30 a里,PC遥感反演研究发文量持续走高,并在算法上取得突破性进展,先后出现了很多经典算法,如波段比法、基线法、嵌套波段比法、生物光学模型、衍生算法和机器学习算法等,成功地分离出PC特征吸收波段(620 nm)的光谱信息,弱化了其他光学活性物质的影响,获得了较可靠的精度。另外,除高光谱航空/卫星影像数据(Hyperion、HICO)外,多光谱卫星遥感平台的PC遥感反演研究也日趋成熟,可选择的数据源种类较多,如Landsat系列、MODIS、MERIS、Sentinel-2 MSI、Sentinel-3 OLCI,其中MERIS和Sentinel-3 OLCI由于其辐射分辨率、空间分辨率和波段设置更适合用于PC遥感反演,在PC遥感反演中被广泛使用。由于PC光谱信号较弱,且易受叶绿素a、悬浮颗粒物等的干扰,想获得高精度的PC遥感估算,仍然是个挑战,未来PC遥感反演研究将向着PC测试方法的标椎化、算法精细化、时空格局大尺度化及PC遥感反演应用深入化等方面发展。

Advances in remote sensing of phycocyanin for inland waters
Abstract:

Phycocyanin (PC), as the signature pigment of cyanobacteria, is usually used for remote sensing monitoring of cyanobacteria bloom. In recent years, the water quality of inland water has deteriorated, the eutrophication has intensified, and the algal blooms frequently occur. The research of remote sensing inversion of PC has attracted more and more attention. Therefore, it is urgent to sort out and write a comprehensive overview paper. In this paper, 178 relevant literatures were reviewed, and the development history and trend of PC remote sensing inversion research of PC in the past 30 years (1990—2020) were comprehensively summarized from the perspectives of PC optical characteristics, development of inversion algorithms, application of satellite sensors, difficulties and interference factors of quantitative remote sensing inversion of PC. This paper helps us to understand the new ideas and new methods emerging at home and abroad, master the new development trends of PC remote sensing inversion in the future, and provide data basis for the monitoring and management of water environment, water quality and water resources. Over the past 30 years, PC remote sensing inversion study number rising, and a breakthrough in the algorithm, has a lot of classic algorithms, such as band ratio method, the baseline method, nested band ratio method, biological optical model, derivative algorithm and machine learning algorithms, algorithm successfully isolated PC spectral characteristic of absorption coefficients at wavelength of 620 nm. Decreased the influence of other optically active substances (Chla、TSM) and obtained the high precision of inversion and validation . In addition, the development of PC inversion algorithms mostly based on in situ hyperspectral data or aerial images (CASI-2, AISA Eagle). In order to meet the needs of PC concentration distribution in a certain space and frequency, satellite image data sources are mostly used. PC. There are many types of multi-spectral satellite data sources to choose, such as Landsat series, MODIS, MERIS, Sentinel-2 MSI, Sentinel-3 OLCI, etc. However, due to the more appropriate band setting, MERIS and Sentinel-3 OLCI are still the most used data sources for PC remote sensing inversion research. Because PC spectrum signal is weak, and vulnerable to the interference of chlorophyll a, TSM, there is still a major difficult to get an accurate estimation result. Based on the above analysis, the future development direction of PC remote sensing inversion can be summarized as the following aspects: first, an international standard measurement method is urgently needed in PC extraction testing; secondly, the development of algorithm, adhere to the mechanism research related to the inherent optical properties, and the integrating machine learning algorithm to bring higher inversion accuracy; third, the scale of study area water body in space and time will toward a larger geographical space scale, longer time series history tracing and future prediction; fourth, at the aspect of the expansion of application, PC remote sensing inversion is not limited to the quantitative estimation of cyanobacteria biomass, but will predict distribution of algal toxins and related diseases based on the relationships between these parameters and PC concentration, furtherly establish a water risk factor rating system based on remote sensing in the future.

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