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http://dx.doi.org/10.7780/kjrs.2022.38.6.1.23

A Study on the Retrieval of River Turbidity Based on KOMPSAT-3/3A Images  

Kim, Dahui (Department of Geophysics, Division of Geology and Geophysics, Kangwon National University)
Won, You Jun (Department of Geophysics, Division of Geology and Geophysics, Kangwon National University)
Han, Sangmyung (Department of Geophysics, Division of Geology and Geophysics, Kangwon National University)
Han, Hyangsun (Department of Geophysics, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1285-1300 More about this Journal
Abstract
Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.
Keywords
KOMPSAT-3; KOMPSAT-3A; Turbidity; New normalized difference turbidity index; XGBoost;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Warren, M.A., S.G.H. Simis, and N. Selmes, 2021. Complementary water quality observations from high and medium resolution Sentinel sensors by aligning chlorophyll-a and turbidity algorithms, Remote Sensing of Environment, 265: 112651. http://doi.org/10.1016/j.rse.2021.112651   DOI
2 Wu, J.-L., C.-R. Ho, C.-C. Huang, A.L. Srivastav, J.-H. Tzeng, and Y.-T. Lim, 2014. Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids, Sensors, 14(12): 22670-22688. http://doi.org/10.3390/s141222670   DOI
3 Jin, C. and C. Choi, 2021. The assessment of cross calibration/validation accuracy for KOMPSAT-3 using Landsat 8 and 6S, Korean Journal of Remote Sensing, 37(1): 123-137. http://doi.org/10.7780/kjrs.2021.37.1.10   DOI
4 Kim, J., S. Heo, H. Noh, H. Yang, W. Jeong, and Y. Lim, 2002. Seasonal and long-term trend of water quality in Lake Paldang, Journal of Korean Society on Water Quality, 18(1): 67-76 (in Korean with English abstract).
5 Kuhn, C., A. de Matos Valerio, N. Ward, L. Loken, H.O. Sawakuchi, M. Kampel, J. Richey, P. Stadler, J. Crawford, R. Striegl, E. Vermote, N. Pahlevan, and D. Butman, 2019. Performance of Landsat-8 and Sentinel-2 surface reflectance products for river remote sensing retrievals of chlorophyll-a and turbidity, Remote Sensing of Environment, 224: 104-118. http://doi.org/10.1016/j.rse.2019.01.023   DOI
6 Ma, Y., K. Song, Z. Wen, G. Liu, Y. Shang, L. Lyu, J. Du, Q. Yang, S. Li, H. Tao, and J. Hou, 2021. Remote sensing of turbidity for lakes in Northeast China using Sensing-2 images with machine learning algorithms, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 9132-9146. http://doi.org/10.1109/JSTARS.2021.3109292   DOI
7 SIIS (SI Imaging Services), 2018. TOA Radiance Reflectance Conversion of KOMPSAT 1.5, https://www.si-imaging.com/resources/?pageid=1&mod=document&keyword=TOA&uid=284, Accessed on Dec. 2, 2022.
8 Claverie, M., J. Ju, J.G. Masek, J.L. Dungan, E.F. Vermote, J.-C. Roger, S.V. Skakun, and C. Justice, 2018. The harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sensing of Environment, 219: 145-161. http://doi.org/10.1016/j.rse.2018.09.002   DOI
9 Garg, V., S.P. Aggarwal, and P. Chauhan, 2020. Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19, Geomatics, Natural Hazards and Risk, 11(1): 1175-1195. http://doi.org/10.1080/19475705.2020.1782482   DOI
10 Peterson, K.T., V. Sagan, and J.J. Sloan, 2020. Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing, GIScience & Remote Sensing, 57(4): 510-525. http://doi.org/10.1080/15481603.2020.1738061   DOI
11 USGS (United States Geological Survey), 2019. Landsat-8 (L8) Data Users Handbook, Version 5.0, https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/LSDS-1574_L8_Data_Users_Handbook-v5.0.pdf, Accessed on Dec. 2, 2022.
12 Lacaux, J.P., Y.M. Tourre, C. Vignolles, J.A. Ndione, and M. Lafaye, 2007. Classification of ponds from high-spatial resolution remote sensing: Application to Rift Valley Fever epidemics in Senegal, Remote Sensing of Environment, 106: 66-74. http://doi.org/10.1016/j.rse.2006.07.012   DOI
13 Bustamante, J., F. Pacios, R. Diaz-Delgado, and D. Aragones, 2009. Predictive models of turbidity and water depth in the Donana marshes using Landsat TM and ETM+ images, Journal of Environmental Management, 90(7): 2219-2225. http://doi.org/10.1016/j.jenvman.2007.08.021   DOI
14 Kim, B. and S. Jung, 2007. Turbidity problems in streams and reservoirs in Korea and control strategies, Nature Conservation, 139: 1-7.
15 Choi, J.-Y., 2006. Turbidity control measures for the upper Han river basin through watershed management, Gyeonggi Forum, 8(3): 71-87 (in Korean with English abstract).
16 Garg, V., A.S. Kumar, S.P. Aggarwal, V. Kumar, P.R. Dhote, P.K. Thakur, B.R. Nikam, R.S. Sambare, A. Siddiqui, P.R. Muduli, and G. Rastogi, 2017. Spectral similarity approach for mapping turbidity of an inland waterbody, Journal of Hydrology, 550: 527-537. http://doi.org/10.1016/j.jhydrol.2017.05.039   DOI
17 Vermote, E., C. Justice, M. Claverie, and B. Franch, 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sensing of Environment, 185: 46-56. http://doi.org/10.1016/j.rse.2016.04.008   DOI
18 Xu, Y., L. Feng, D. Zhao, and J. Lu, 2020. Assessment of Landsat atmospheric correction methods for water color applications using global AERONETOC data, International Journal of Applied Earth Observation and Geoinformation, 93: 102192. http://doi.org/10.1016/j.jag.2020.102192   DOI
19 Hou, X., L. Feng, H. Duan, X. Chen, D. Sun, and K. Shi, 2017. Fifteen-year monitoring of the turbidity dynamics in large lakes and reservoirs in the middle and lower basin of the Yangtze River, China, Remote Sensing of Environment, 190: 107-121. http://doi.org/10.1016/j.rse.2016.12.006   DOI
20 Guttler, F.N., S. Niculescu, and F. Gohin, 2013. Turbidity retrieval and monitoring of Danube Delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western-northwestern Black Sea coastal zone, Remote Sensing of Environment, 132: 86-101. http://doi.org/10.1016/j.rse.2013.01.009   DOI
21 Quang, N.H., J. Sasaki, H. Higa, and N.H. Huan, 2017. Spatiotemporal variation of turbidity based on Landsat 8 OLI in Cam Ranh Bay and Thuy Trieu Lagoon, Vietnam, Water, 9(8): 570. http://doi.org/10.3390/w9080570   DOI
22 Ahn, H.-Y., S.-I. Na, C.-W. Park, S.-Y. Hong, K.-H. So, and K.-D. Lee, 2020. Radiometric cross calibration of KOMPSAT-3 and Landsat-8 for time-series harmonization, Korean Journal of Remote Sensing, 36(6-2): 1523-1535 (in Korean with English abstract). http://doi.org/10.7780/kjrs.2020.36.6.2.4   DOI
23 Braga, F., L. Zaggia, D. Bellafiore, M. Bresciani, C. Giardino, G. Lorenzetti, F. Maicu, C. Manzo, F. Riminucci, M. Ravaioli, and V.E. Brando, 2017. Mapping turbidity patterns in the Po river prodelta using multi-temporal Landsat 8 imagery, Estuarine, Coastal and Shelf Science, 198: 555-567. https://doi.org/10.1016/j.ecss.2016.11.003.   DOI
24 Chen, T. and C. Guestrin, 2016. Xgboost: A scalable tree boosting system, Proc. of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, Aug. 13-17, pp. 785-794. https://doi.org/10.1145/2939672.2939785   DOI
25 El-Alem, A. and K. Chokmani, 2022. A machine learning-based regional hybrid model for remote retrieving turbidity from Landsat imagery, IEEE Geoscience and Remote Sensing Letters, 19: 8021605. http://doi.org/10.1109/LGRS.2021.3115986   DOI