JKSWE

1

Ali K. A., Ortiz J., Bonini N., Shuman M., Sydow C., (2016), "Application of aqua MODIS sensor data for estimating chlorophyll a in the turbid case 2 waters of Lake Erie using bio-optical models," GIScience & Remote Sensing, vol. 53, no. 4, pp. 483-505.

2

Barredo Arrieta A., Díaz-Rodríguez N., Del Ser J., Bennetot A., Tabik S., Barbado A., Garcia S., Gil-Lopez S., Molina D., Benjamins R., Chatila R., Herrera F., (2020), "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 58, pp. 82-115.

3

Baik J. J., Park J. M., Choi M. H., (2020), "Estimation and assessment of total phosphorus and trophic state index estimated from landsat 8 dataset on 2018 at Daecheong reservoir," Journal of Korean Society of Environmental Engineers, vol. 42, no. 9, pp. 415-423.[Korean Literature].

4

Busse L. B., Simpson J. C., Cooper S. D., (2006), "Relationships among nutrients, algae, and land use in urbanized southern California streams," Canadian Journal of Fisheries and Aquatic Sciences, vol. 63, no. 12, pp. 2621-2638.

5

Chen Q., Huang M., Bai K., Li X., (2020), "An optimal two bands ratio model to monitor chlorophyll-a in urban lake using Landsat 8 data," E3S Web of Conferences, vol. 143, pp. 02003.

6

Chen T., Guestrin C., (2016), "Xgboost: A scalable tree boosting system," Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794.

7

Claverie M., Ju J., Masek J. G., Dungan J. L., Vermote E. F., Roger J. C., Justice C., (2018), "The harmonized landsat and sentinel-2 surface reflectance data set," Remote Sensing of Environment, vol. 219, pp. 145-161.

8

Claverie M., Masek J. G., Ju J., Dungan J. L., (2017), "Harmonized landsat-8 sentinel-2 (HLS) product user's guide," National Aeronautics and Space Administration (NASA), Washington, DC, USA, pp. 18.

9

Friedman J. H., (2001), "Greedy function approximation: A gradient boosting machine," Annals of statistics, pp. 1189-1232.

10

Gao Y., Gao J., Yin H., Liu C., Xia T., Wang J., Huang Q., (2015), "Remote sensing estimation of the total phosphorus concentration in a large lake using band combinations and regional multivariate statistical modeling techniques," Journal of Environmental Management, vol. 151, pp. 33-43.

11

Gholizadeh M. H., Melesse A. M., Reddi L., (2016), "A comprehensive review on water quality parameters estimation using remote sensing techniques," Sensors, vol. 16, no. 8, pp. 1298.

12

Ju J., Zhou Q., Freitag B., Roy D. P., Zhang H. K., Sridhar M., Mandel J., Arab S., Schmidt G., Crawford C. J., Gascon F., Strobl P., Masek J. G., Neigh C. S. R., (2025), "The Harmonized Landsat and Sentinel-2 version 2.0 surface reflectance dataset," Remote Sensing of Environment, vol. 324, pp. 114723.

13

Kim S. H., Moon B. H., Song B. G., Park G. H., (2019), "Analysis of the feasibility of using UAV imagery to identify water quality characteristics of small streams in agricultural areas," Journal of the Korean Geographic Information Society, vol. 22, no. 3, pp. 10-20.[Korean Literature].

14

Kim S. H., Park J. H., Kim B., (2021), "Prediction of cyanobacteria harmful algal blooms in reservoir using machine learning and deep learning," Journal of Korea Water Resources Association, vol. 54, no. 12, pp. 1167-1181.

15

Kim Y. W., Kim T., Shin J., Lee D. S., Park Y. S., Kim Y., Cha Y., (2022), "Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters," Ecological Indicators, vol. 137, pp. 108737.

16

Kim Y., Jang S. W., Kim H. M., Kim T. Y., Bak S., (2023), "Application of remote sensing techniques to survey and estimate the standing-stock of floating debris in the upper Daecheong Lake," Korean Journal of Remote Sensing, vol. 39, no. 5, pp. 589-597.[Korean Literature].

17

Lechler S., Picoli M. C. A., Soares A. R., Sanchez A., Chaves M. E. D., Verstegen J., (2020), "Exploring Nasa's Harmonized Landsat and Sentinel-2 (HLS) dataset to monitor deforestation in the Amazon Rainforest," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 43, pp. 705-711.

18

Lee J. J., Lee Y. R., Lim D. H., Ahn H. C., (2021), "A study on the employee turnover prediction using XGBoost and SHAP," Journal of Information Systems Research, vol. 30, no. 4, pp. 21-42.[Korean Literature].

19

Liang Y., Yin F., Xie D., Liu L., Zhang Y., Ashraf T., (2022), "Inversion and monitoring of the TP concentration in Taihu Lake using the landsat-8 and sentinel-2 images," Remote Sensing, vol. 14, no. 24, pp. 6284.

20

Lin J. L., Karangan A., Huang Y. M., Kang S. F., (2022), "Eutrophication factor analysis using Carlson trophic state index (CTSI) towards non-algal impact reservoirs in Taiwan," Sustainable Environment Research, vol. 32, no. 1, pp. 25.

21

Lu H., Ma X., (2020), "Hybrid decision tree-based machine learning models for short-term water quality prediction," Chemosphere, vol. 249, pp. 126169.

22

Lundberg S. M., Lee S. I., (2017), "A unified approach to interpreting model predictions," Advances in neural information processing systems, pp. 4766-4777.

23

Ministry of Environment (ME), (2019), "Algae management policy direction presentation report," Ministry of Environment.[Korean Literature].

24

Mpakairi K. S., Muthivhi F. F., Dondofema F., Munyai L. F., Dalu T., (2024), "Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir," Environmental Monitoring and Assessment, vol. 196, no. 4, pp. 401.

25

National Institute of Environmental Research (NIER), (2026), "Water Environment Information System (WEIS)."https://water.nier.go.kr/web (accessed March 2026).

26

Ngamile S., Madonsela S., Kganyago M., (2025), "Trends in remote sensing of water quality parameters in inland water bodies: A systematic review," Frontiers in Environmental Science, vol. 13, pp. 1549301.

27

Olden J. D., Lawler J. J., Poff N. L., (2008), "Machine learning methods without tears: A primer for ecologists," The Quarterly Review of Biology, vol. 83, no. 2, pp. 171-193.

28

Park J. M., Park K. J., Kim Y. W., Kim B. K., (2025), "Estimation of high-resolution soil moisture using Sentinel-1 and LightGBM over the Miho River Watershed," Journal of the Korean Society of Hazard Mitigation, vol. 25, no. 6, pp. 65-73.[Korean Literature].

29

Park S. S., Lee G. H., Seol S. J., Kim D. M., Kim S. J., Ko J. I., (2024), "Study on machine learning-based prediction of lime dosage for mine drainage treatment," Journal of the Korean Society of Mineral and Energy Resources Engineers, vol. 61, no. 5, pp. 333-346.

30

Park S., Son S., Bae J., Lee D., Seo D., Kim J., (2023), "Estimation of Chlorophyll-a concentration in Nakdong River using machine learning-based satellite data and water quality, hydrological, and meteorological factors," Korean Journal of Remote Sensing, vol. 39, no. 5, pp. 655-667.[Korean Literature].

31

Pereyra-Laguna E., Ojeda-Castillo V., Herrera-López E. J., del Real-Olvera J., Hernández-Mena L., Vallejo-Rodríguez R., Díaz J., (2025), "Satellite-based prediction of water turbidity using surface reflectance and field spectral data in a dynamic tropical lake," Remote Sensing, vol. 17, no. 15, pp. 2595.

32

Qin H., Fang C., Liu G., Song K., Li Z., Li S., Yan Z., (2025), "Temperature is a key factor affecting total phosphorus and total nitrogen concentrations in northeastern lakes based on sentinel-2 images and machine learning methods," Remote Sensing, vol. 17, no. 2, pp. 267.

33

Salama M. S., Spaias L., Poser K., Peters S., Laanen M., (2022), "Validation of sentinel-2 (MSI) and sentinel-3 (OLCI) water quality products in turbid estuaries using fixed monitoring stations," Frontiers in Remote Sensing, vol. 2, pp. 808287.

34

Shim S., Kim Y., Lee H., Kim M., Choi J., (2022), "Comparison of chlorophyll-a prediction and analysis of influential factors in Yeongsan River using machine learning and deep learning," Journal of Korean Society on Water Environment, vol. 38, no. 6, pp. 292-305.[Korean Literature].

35

Shin G., Kim T., (2025), "Application of machine learning based on remote sensing and water quality model to predict Chlorophyll a in reservoir," Journal of the Korean Society of Environmental Technology, vol. 26, no. 2, pp. 65-77.[Korean Literature].

36

Song K., Wang Z., Blackwell J., Zhang B., Li F., Zhang Y., Jiang G., (2011), "Water quality monitoring using Landsat Thematic Mapper data with empirical algorithms in Chagan Lake, China," Journal of Applied Remote Sensing, vol. 5, no. 1, pp. 053506-053506.

37

Wang S., Qin B., (2025), "Application of optical remote sensing in harmful algal blooms in lakes: A review," Remote Sensing, vol. 17, no. 8, pp. 1381.

38

Willmott C. J., Matsuura K., (2005), "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance," Climate Research, vol. 30, no. 1, pp. 79-82.

39

Yan K., Li J., Zhao H., Wang C., Hong D., Du Y., Wang S., (2022), "Deep learning-based automatic extraction of cyanobacterial blooms from sentinel-2 MSI satellite data," Remote Sensing, vol. 14, no. 19, pp. 4763.

40

Yu G., Yang W., Matsushita B., Li R., Oyama Y., Fukushima T., (2014), "Remote estimation of Chlorophyll-a in inland waters by a NIR-Red-based algorithm: Validation in Asian lakes," Remote Sensing, vol. 6, no. 4, pp. 3492-3510.

41

Zhou C., Xu Q., Jia R., Zhang Z., Xu X., Liu H., Feng L., (2026), "Long‐term records reveal temperature‐driven nutrient limitation and predict intensified algal blooms in global lakes," Global Change Biology, vol. 32, no. 1, pp. e70719.

42

Zhou X., Liu G., Wu Y., Wang G., Xu Y., Liu T., (2025), "XGBoost-SHAP based multi-scale evaluation and nonlinear response of river health in Guangdong Province," Ecological Indicators, vol. 178, pp. 114138.

JKSWE The Journal of
the Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal ofthe Korean Society on Water Environment

Editorial Office

Journal XML

References

JKSWE The Journal of
the Korean Society on Water Environment