{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T02:30:39Z","timestamp":1768098639176,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T00:00:00Z","timestamp":1719273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014189","name":"Korea institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET)","doi-asserted-by":"publisher","award":["320049-5"],"award-info":[{"award-number":["320049-5"]}],"id":[{"id":"10.13039\/501100014189","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014189","name":"Korea institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET)","doi-asserted-by":"publisher","award":["NIER-RP2017-204"],"award-info":[{"award-number":["NIER-RP2017-204"]}],"id":[{"id":"10.13039\/501100014189","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Institute of Environmental Research (NIER)","award":["320049-5"],"award-info":[{"award-number":["320049-5"]}]},{"name":"National Institute of Environmental Research (NIER)","award":["NIER-RP2017-204"],"award-info":[{"award-number":["NIER-RP2017-204"]}]},{"name":"Environmental Fundamental Data Examination project of the Hangang River Basin Management Committee","award":["320049-5"],"award-info":[{"award-number":["320049-5"]}]},{"name":"Environmental Fundamental Data Examination project of the Hangang River Basin Management Committee","award":["NIER-RP2017-204"],"award-info":[{"award-number":["NIER-RP2017-204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables Rrs (452\/497), Rrs (497\/580), Rrs (497\/618), and Rrs (684\/618), which had the highest correlation with the CDOM absorption coefficient aCDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m\u22121. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R2, absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m\u22121, respectively, compared to the original algorithm\u2019s test values of 0.722, 0.493, and 0.802 m\u22121. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance.<\/jats:p>","DOI":"10.3390\/rs16132313","type":"journal-article","created":{"date-parts":[[2024,6,25]],"date-time":"2024-06-25T12:46:56Z","timestamp":1719319616000},"page":"2313","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhancing Machine Learning Performance in Estimating CDOM Absorption Coefficient via Data Resampling"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7815-3400","authenticated-orcid":false,"given":"Jinuk","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2115-8969","authenticated-orcid":false,"given":"Jin Hwi","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0949-4735","authenticated-orcid":false,"given":"Wonjin","family":"Jang","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JongCheol","family":"Pyo","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea"},{"name":"Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyuk","family":"Lee","sequence":"additional","affiliation":[{"name":"Water Quality Assessment Research Division, National Institute of Environmental Research, Environmental Research Complex, Incheon 22689, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6597-7264","authenticated-orcid":false,"given":"Seohyun","family":"Byeon","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hankyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Civil, Environmental and Plant Engineering, Graduate School, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-0843","authenticated-orcid":false,"given":"Yongeun","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9729-9373","authenticated-orcid":false,"given":"Seongjoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirk, J.T.O. 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