基于半监督光学分类的白洋淀叶绿素a浓度反演方法

doi:10.3964/j.issn.1000-0593(2025)07-2068-10

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Abstract The estimation of Chlorophyll-a concentration (Chl-a) using remote sensing technology is considered an effective way to monitor eutrophication in water bodies. Due to the complexity of the optical properties of inland water bodies and the existence of large spatial and temporal variability, it is difficult for a single estimation model to accurately estimate Chl-a concentration, and targeted modelling estimation based on the results of the optical classification of water bodies is one of the most important technological approaches for inland water body Chl-a inversion. In this study, Baiyangdian is taken as the study area, and a semi-supervised optical classification-based estimation method is proposed using the measured reflectance spectra and Chl-a concentration as the data source. First, to ensure that the number of samples in each category after classification is sufficient to support the construction of the estimation model, this study divides the samples into modelling set and validation set according to the ratio of 7∶1. Representative labelled samples of Baiyangdian were selected by multiple spectral indices and Moore's voting algorithm. Secondly, the fuzzy C-mean clustering algorithm and random forest algorithm are selected to construct a semi-supervised classifier, based on the representative labelled samples obtained, to further explore the potential information in the unlabelled samples and improve the classification accuracy of the unlabelled samples. Finally, the estimation models were established for each water type, the centre-of-mass spectra of each water type were calculated, and the final estimation results were obtained by hybrid-weighting using distance weights. The results show that Baiyangdian water bodies can be classified into phytoplankton-dominated, intermediate and suspended matter-dominated according to the spectral characteristics, and different types of water bodies have obvious differences in optical properties, which can be used to select the optimal estimation model and improve the estimation accuracy according to the optical classification results. Compared with the traditional optical classification strategy, the method proposed in this study performed the best, with a decrease in the mean relative error, root mean square error and mean absolute error, and was able to estimate Chl-a concentration more accurately (MRE=0.10, RMSE=0.126 μg·L^-1, MAE=0.106 μg·L^-1). In addition, applying ZY01-02E image data for Chl-a estimation in this study can effectively reveal the spatial distribution of Chl-a concentration. This method demonstrates the potential for application in eutrophication monitoring of water bodies, and also provides a new idea for remote sensing estimation of Chl-a concentration in inland water bodies.

Key words：Semi-supervised classification;Optical type of water;Chlorophyll-a;Baiyangdian

Received: 2024-10-12 Accepted: 2025-02-10

ZTFLH:

TP79

Corresponding Authors: ZHAO Qi-chao E-mail: rs_zhao@nciae.edu.cn

Cite this article:

CHEN Wen-yue,ZHAO Qi-chao,YANG Xiu-feng, et al. Estimation Approach of Chlorophyll-a Concentration in Baiyangdian Based on Semi-Supervised Optical Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(07): 2068-2077.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2025)07-2068-10 OR https://www.gpxygpfx.com/EN/Y2025/V45/I07/2068

[1] LI Na，LI Jia-xi，LI Guo-wen，et al(李娜，黎佳茜，李国文，等). Acta Hydrobiologica Sinica(水生生物学报)，2018，42(4)：854.
[2] BAO Ying，TIAN Qing-jiu，CHEN Min，et al(包颖，田庆久，陈旻，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析)，2016，36(8)：2562.
[3] Cui T W，Zhang J，Wang K，et al. ISPRS Journal of Photogrammetry and Remote Sensing，2020，163: 187.
[4] LUO Jie-chun-yi，QIN Long-jun，MAO Peng，et al(罗婕纯一，秦龙君，毛鹏，等). Remote Sensing Technology and Application(遥感技术与应用)，2021，36(3)：473.
[5] KUANG Run-yuan，ZHAO Yan-fu，LUO Wei，et al(况润元，赵艳福，罗卫，等). Journal of China Hydrology(水文)，2017，37(6)：23.
[6] LI Yun-mei，ZHAO Huan，BI Shun，et al(李云梅，赵焕，毕顺，等). National Remote Sensing Bulletin(遥感学报)，2022，26(1)：19.
[7] FENG Chi，JIN Qi，WANG Yan-nan，et al(冯驰，金琦，王艳楠，等). Environmental Science(环境科学)，2015，36(5)：1557.
[8] SONG Zi-hao，KUANG Run-yuan(宋子豪，况润元). Jiangxi Hydraulic Science & Technology(江西水利科技)，2021，47(1)：70.
[9] Zhang F，Li J，Shen Q，et al. International Journal of Applied Earth Observation and Geoinformation，2019，74: 138.
[10] Shi K，Li Y，Li L，et al. Science of the Total Environment，2013，444: 1.
[11] ZHANG Fang-fang，LI Jun-sheng，WANG Chao，et al(张方方，李俊生，王超，等). National Remote Sensing Bulletin(遥感学报)，2023，27(3)：769.
[12] Dang X，Du J，Wang C，et al. Remote Sensing，2023，15(8): 2209.
[13] Jiang G，Loiselle S A，Yang D，et al. Remote Sensing of Environment，2020，241：111735.
[14] Le C，Li Y，Zha Y，et al. Remote Sensing of Environment: An Interdisciplinary Journal，2011，115(2)：725.
[15] Sun D，Hu C，Qiu Z，et al. Remote Sensing of Environment，2014，155: 289.
[16] HAN Bao-hui，ZHAO Qi-chao，CHANG Rong，et al(韩宝辉，赵起超，常荣，等). Journal of Geo-Information Science(地球信息科学学报)，2023，25(9)：1882.
[17] Lichtenthaler H K, Buschmann C. Current Protocols in Food Analytical Chemistry，John Wiley & Sons, Inc., 2001.
[18] Gan H，Sang N，Huang R，et al. Neurocomputing，2013，101: 290.
[19] KUANG Run-yuan，LUO Wei，ZHANG Meng(况润元，罗卫，张萌). Resources and Environment in the Yangtze Basin(长江流域资源与环境)，2015，24(5)：773.