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Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal

Author

Listed:
  • Hone-Jay Chu

    (National Cheng Kung University)

  • Mạnh Van Nguyen

    (National Cheng Kung University
    Vietnam Academy of Science and Technology)

  • Lalu Muhamad Jaelani

    (Institut Teknologi Sepuluh Nopember (ITS))

Abstract

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.

Suggested Citation

  • Hone-Jay Chu & Mạnh Van Nguyen & Lalu Muhamad Jaelani, 2020. "Satellite-Based Water Quality Mapping from Sequential Simulation with Parameter Outlier Removal," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 311-325, January.
    • Handle: RePEc:spr:waterr:v:34:y:2020:i:1:d:10.1007_s11269-019-02443-0
    DOI: 10.1007/s11269-019-02443-0
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    References listed on IDEAS

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    1. Steven Farber & Antonio Páez, 2007. "A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations," Journal of Geographical Systems, Springer, vol. 9(4), pages 371-396, December.
    2. Lawrence Kiage & Nan Walker, 2009. "Using NDVI from MODIS to Monitor Duckweed Bloom in Lake Maracaibo, Venezuela," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(6), pages 1125-1135, April.
    3. Neamat Karimi & Mohammad Hossein Bagheri & Farhad Hooshyaripor & Ashkan Farokhnia & Sara Sheshangosht, 2016. "Deriving and Evaluating Bathymetry Maps and Stage Curves for Shallow Lakes Using Remote Sensing Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(14), pages 5003-5020, November.
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