The satellite-based regression model provides the data model that identifies water quality for inland and coastal waters. However, the satellite regression usually depends on the selection of observation, satellite data, and model type. A resampling simulation technique, such as sequential simulation using geographically weighted regression (GWR simulation), can be applied in generating multiple realizations for water quality estimation to reduce the sampling effect and consider spatial heterogeneity. Traditional models often result in considerable underestimation in extreme observations. The GWR simulation provides the best goodness of fit and spatial varying relationship between observed water quality and remote sensing considering parameter outlier and noise removal for parameter stability. This simulation model can increase the sampling diversity from various observations and reduce the neighboring effects of observations using outlier and noise removal. The model that handles spatial uncertainty and heterogeneity is a novel tool for inferring the characteristics of water quality from a series of sample subsets.
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We would like to thank University of Tsukuba for the invaluable dataset. Apart from this, we are very grateful for the financial assistance from our Ministry of Science and Technology (106-2923-M-006-003-MY3), Taiwan.
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Chu, H., Nguyen, M.V. & Jaelani, L.M. Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal. Water Resour Manage 34, 311–325 (2020). https://doi.org/10.1007/s11269-019-02443-0
- Water quality
- Satellite images
- GWR simulation; outlier removal