Advances in remote sensing of phycocyanin for inland waters

被引：0
|
作者：
Lyu L. [1 ,2 ]
Song K. [1 ,3 ]
Liu G. [1 ]
Wen Z. [1 ]
Shang Y. [1 ]
Li S. [1 ]
机构：
[1] Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun
[2] University of Chinese Academy of Sciences, Beijing
[3] School of Environment and Planning, Liaocheng University, Liaocheng
来源：
National Remote Sensing Bulletin | 2022年 / 26卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Inland waters; Inversion algorithm; Lake remote sensing; Optical properties; Phycocyanin; Satellite sensor;
D O I：
10.11834/jrs.20221276
中图分类号：
学科分类号：
摘要：
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. © 2022, Science Press. All right reserved.
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页码：32 / 48
页数：16
相关论文
共 89 条
  • [21] Jin Q, Lyu H, Shi L, Miao S, Wu Z M, Li Y M, Wang Q., Developing a two-step method for retrieving cyanobacteria abundance from inland eutrophic lakes using MERIS data, Ecological Indicators81, pp. 543-554, (2017)
  • [22] Jupp D L B, Kirk J T O, Harris G P., Detection, identification and mapping of cyanobacteria-using remote sensing to measure the optical quality of turbid inland waters, Australian Journal of Marine and Freshwater Research, 45, 5, pp. 801-828, (1994)
  • [23] Kudela R M, Palacios S L, Austerberry D C, Accorsi E K, Guild L S, Torres-Perez J., Application of hyperspectral remote sensing to cyanobacterial blooms in inland waters, Remote Sensing of Environment, 167, pp. 196-205, (2015)
  • [24] Kwon Y S, Pyo J, Kwon Y H, Duan H T, Cho K H, Park Y., Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir, Remote Sensing of Environment, 236, (2020)
  • [25] Le C F, Li Y M, Zha Y, Wang Q, Zhang H, Yin B., Remote sensing of phycocyanin pigment in highly turbid inland waters in Lake Taihu, China, International Journal of Remote Sensing, 32, 23, pp. 8253-8269, (2011)
  • [26] Lee Z P, Carder K L, Arnone R A., Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters, Applied Optics, 41, 27, pp. 5755-5772, (2002)
  • [27] Li L, Sengpiel R E, Pascual D L, Tedesco L P, Wilson J S, Soyeux E., Using hyperspectral remote sensing to estimate chlorophyll-a and phycocyanin in a mesotrophic reservoir, International Journal of Remote Sensing, 31, 15, pp. 4147-4162, (2010)
  • [28] Li L H, Li L, Shi K, Li Z C, Song K S., A semi-analytical algorithm for remote estimation of phycocyanin in inland waters, Science of the Total Environment, 435-436, pp. 141-150, (2012)
  • [29] Li L H, Li L, Song K S., Remote sensing of freshwater cyanobacteria: an extended IOP Inversion Model of Inland Waters (IIMIW) for partitioning absorption coefficient and estimating phycocyanin, Remote Sensing of Environment, 157, pp. 9-23, (2015)
  • [30] Liu G, Simis S G H, Li L, Wang Q, Li Y M, Song K S, Lyu H, Zheng Z B, Shi K., A four-band semi-analytical model for estimating phycocyanin in inland waters from simulated MERIS and OLCI data, IEEE Transactions on Geoscience and Remote Sensing, 56, 3, pp. 1374-1385, (2018)
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