Remote estimation of phycocyanin concentration in inland waters based on optical classification

Lili Lyu; Kaishan Song; Zhidan Wen; Ge Liu; Chong Fang; Yingxin Shang; Sijia Li; Hui Tao; Xiang Wang; Yong Li; Xiangyu Wang

doi:10.1016/j.scitotenv.2023.166363

Remote estimation of phycocyanin concentration in inland waters based on optical classification

Sci Total Environ. 2023 Nov 15:899:166363. doi: 10.1016/j.scitotenv.2023.166363. Epub 2023 Aug 19.

Authors

Lili Lyu¹, Kaishan Song², Zhidan Wen³, Ge Liu⁴, Chong Fang⁴, Yingxin Shang⁴, Sijia Li⁴, Hui Tao⁴, Xiang Wang⁴, Yong Li⁴, Xiangyu Wang⁵

Affiliations

¹ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; Jilin Jianzhu University, Changchun, China.
² Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; School of Environment and Planning, Liaocheng University, Liaocheng 252000, China. Electronic address: songkaishan@iga.ac.cn.
³ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China. Electronic address: wenzhidan@iga.ac.cn.
⁴ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
⁵ Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; College of Geographical Sciences, Changchun Normal University, Changchun 130102, China.

PMID: 37598955
DOI: 10.1016/j.scitotenv.2023.166363

Abstract

In recent years, under the dual pressure of climate change and human activities, the cyanobacteria blooms in inland waters have become a threat to global aquatic ecosystems and the environment. Phycocyanin (PC), a diagnostic pigment of cyanobacteria, plays an essential role in the detection and early warning of cyanobacterial blooms. In this context, accurate estimation of PC concentration in turbid waters by remote sensing is challenging due to optical complexity and weak optical signal. In this study, we collected a comprehensive dataset of 640 pairs of in situ measured pigment concentration and the Ocean and Land Color Instrument (OLCI) reflectance from 25 lakes and reservoirs in China during 2020-2022. We then developed a framework consisting of the water optical classification algorithm and three candidate algorithms: baseline height, band ratio, and three-band algorithm. The optical classification method used remote sensing reflectance (R_rs) baseline height in three bands: R_rs(560), R_rs(647) and R_rs(709) to classify the samples into five types, each with a specific spectral shape and water quality character. The improvement of PC estimation accuracy for optically classified waters was shown by comparison with unclassified waters with RMSE = 72.6 μg L^-1, MAPE = 80.4 %, especially for the samples with low PC concentration. The results show that the band ratio algorithm has a strong universality, which is suitable for medium turbid and clean water. In addition, the three-band algorithm is only suitable for medium turbid water, and the line height algorithm is only suitable for high PC content water. Furthermore, the five distinguished types with significant differences in the value of the PC/Chla ratio well indicated the risk rank assessment of cyanobacteria. In conclusion, the proposed framework in this paper solved the problem of PC estimation accuracy problem in optically complex waters and provided a new strategy for water quality inversion in inland waters.

Keywords: Inland waters; OLCI; Optical water classification; Phycocyanin; Remote sensing.