• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

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.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.667-669
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• 2003

A bio-optical model was developed specific for turbid and shallow waters. Special studies were carried out to estimate absorption and scattering properties as well as backscattering probability of suspended matter. The inversion of bio-optical model allows for direct retrieval of turbidity and chlorophyll- a from the visible-near infrared (VNIR) range sensor. Time-series satellite imagery from ASTER AM-1 sensor, were used to monitor the Laguna de Bay water quality condition. Spatial distribution of temperature for the lake was extracted from the thermal infrared (TIR) sensor. Corresponding field surveys were conducted to parameterize the bio -optical model. In-situ measurements include suspended particle and chlorophyll-a concentrations profiles from nephelometric devices and processing of water samples. Hyperspectral measurements were used to validate results of the bio -optical model and satellite- based estimation. This study provides a theoretical basis and a practical illustration of applying space- based measurements on an operational basis.

• Korean Journal of Remote Sensing
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• v.23 no.3
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• pp.161-169
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• 2007

A study was conducted in the Hong Kong with the aim of deriving an algorithm for the retrieval of suspended sediment (SS) and sea surface salinity (SSS) concentrations from Aqua/MODIS level 1B reflectance data with 250m and 500m spatial resolutions. 'In-situ' measurements of SS and SSS were also compared with coincident MODIS spectral reflectance measurements over the ocean surface. This is the first study of SSS modeling in Southeast Asia using earth observation satellite images. Three analysis techniques such as multiple regression, linear regression, and principal component analysis (PCA) were performed on the MODIS data and the 'in-situ' measurement datasets of the SS and SSS. Correlation coefficients by each analysis method shows that the best correlation results are multiple regression from the 500m spatial resolution MODIS images, $R^2$= 0.82 for SS and $R^2$ = 0.81 for SSS. The Root Mean Square Error (RMSE) between satellite and 'in-situ' data are 0.92mg/L for SS and 1.63psu for SSS, respectively. These suggest that 500m spatial resolution MODIS data are suitable for water quality modeling in the study area. Furthermore, the application of these models to MODIS images of the Hong Kong and Pearl River Delta (PRO) Region are able to accurately reproduce the spatial distribution map of the high turbidity with realistic SS concentrations.

• Journal of the Korean earth science society
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• v.42 no.3
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• pp.247-263
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• 2021

Climate change has been accelerating in coastal waters recently; therefore, the importance of coastal environmental monitoring is also increasing. Chlorophyll-a concentration, an important marine variable, in the surface layer of the global ocean has been retrieved for decades through various ocean color satellites and utilized in various research fields. However, the commonly used chlorophyll-a concentration algorithm is only suitable for application in clear water and cannot be applied to turbid waters because significant errors are caused by differences in their distinct components and optical properties. In addition, designing a standard algorithm for coastal waters is difficult because of differences in various optical characteristics depending on the coastal area. To overcome this problem, various algorithms have been developed and used considering the components and the variations in the optical properties of coastal waters with high turbidity. Chlorophyll-a concentration retrieval algorithms can be categorized into empirical algorithms, semi-analytic algorithms, and machine learning algorithms. These algorithms mainly use the blue-green band ratio based on the reflective spectrum of sea water as the basic form. In constrast, algorithms developed for turbid water utilizes the green-red band ratio, the red-near-infrared band ratio, and the inherent optical properties to compensate for the effect of dissolved organisms and suspended sediments in coastal area. Reliable retrieval of satellite chlorophyll-a concentration from turbid waters is essential for monitoring the coastal environment and understanding changes in the marine ecosystem. Therefore, this study summarizes the pre-existing algorithms that have been utilized for monitoring turbid Case 2 water and presents the problems associated with the mornitoring and study of seas around the Korean Peninsula. We also summarize the prospective for future ocean color satellites, which can yield more accurate and diverse results regarding the ecological environment with the development of multi-spectral and hyperspectral sensors.