{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:13Z","timestamp":1764053833896,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Major Research Focus Special Project of the Henan Academy of Sciences","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]},{"name":"Special Project for Scientific Research and Development of the Henan Academy of Sciences","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]},{"name":"Central Guidance for Local Science and Technology Development Funds Projects","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]},{"name":"Talent cultivation project of Henan Academy of Sciences","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]},{"name":"Key R&amp;D Project of Science and Technology of Kaifeng City","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]},{"name":"Science and Technology Research Project of Henan Province","award":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"],"award-info":[{"award-number":["210101007","220601067","211201004","230501008","22ZDYF006","212102310024","232102320263"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Low- and medium-resolution satellites have been a relatively mature platform for inland eutrophic water classification and chlorophyll a concentration (Chl-a) retrieval algorithms. However, for oligotrophic and mesotrophic waters in small- and medium-sized reservoirs, problems of low satellite resolution, insufficient water sampling, and higher uncertainty in retrieval accuracy exist. In this paper, a hybrid Chl-a estimation method based on spectral characteristics (i.e., remote sensing reflectance (Rrs)) classification was developed for oligotrophic and mesotrophic waters using high-resolution satellite Sentinel-2 (A and B) data. First, 99 samples and quasi-synchronous Sentinel-2 satellite data were collected from four small- and medium-sized reservoirs in central China, and the usability of the Sentinel-2 Rrs data in inland oligotrophic and mesotrophic waters was verified by accurate atmospheric correction. Second, a new optical classification method was constructed based on different water characteristics to classify waters into clear water, phytoplankton-dominated water, and water dominated by phytoplankton and suspended matter together using the thresholds of Rrs490\/Rrs560 and Rrs665\/Rrs560. The proposed method has a higher classification accuracy compared to other classification methods, and the band-ratio algorithm is simpler and more effective for satellite sensors without NIR bands. Third, given the sensitivity of the empirical method to water variability and the ease of development and implementation, a nonlinear least squares fitted one-dimensional nonlinear function was established based on the selection of the best-fitting spectral indices for different optical water types (OWTs) and compared with other Chl-a estimation algorithms. The validation results showed that the hybrid two-band method had the highest accuracy with squared correlation coefficient, root mean squared difference, mean absolute percentage error, and bias of 0.85, 2.93, 32.42%, and \u22120.75 mg\/m3, respectively, and the results of the residual values further validated the applicability and reliability of the model. Finally, the performance of the classification and estimation algorithms on the four reservoirs was evaluated to obtain images mapping the Chl-a in the reservoirs. In conclusion, this study improves the accuracy of Chl-a estimation for oligotrophic and mesotrophic waters by combining a new classification algorithm with a two-band hybrid model, which is an important contribution to solving the problem of low resolution and high uncertainty in the retrieval of Chl-a in oligotrophic and mesotrophic waters in small- and medium-sized reservoirs and has the potential to be applied to other optically similar oligotrophic and mesotrophic lakes and reservoirs using similar spectrally satellite sensors.<\/jats:p>","DOI":"10.3390\/rs15082209","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T10:11:25Z","timestamp":1682071885000},"page":"2209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Hybrid Chlorophyll a Estimation Method for Oligotrophic and Mesotrophic Reservoirs Based on Optical Water Classification"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoyan","family":"Dang","sequence":"first","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]},{"given":"Jun","family":"Du","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9628-1817","authenticated-orcid":false,"given":"Fangfang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2211-2965","authenticated-orcid":false,"given":"Lin","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}]},{"given":"Jiping","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Surveying and Mapping, Beijing 100830, China"}]},{"given":"Zheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]},{"given":"Xu","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4544-1593","authenticated-orcid":false,"given":"Jingxu","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geographical Sciences, Henan Academy of Sciences, Zhengzhou 450052, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1007\/s001280202","article-title":"Application of Remote Sensing Techniques in Monitoring and Assessing the Water Quality of Taihu Lake","volume":"67","author":"Wang","year":"2001","journal-title":"Bull. 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