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Remote Sensing of Water Quality

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Abstract

There is a need for increased monitoring of freshwater resources to effectively manage water quality. Remote sensing has the potential to substantially improve the spatiotemporal resolution of monitoring. Satellites sensors vary in temporal, spatial, radiometric, and spectral resolution. For remote sensing of optically active water constituents, there is no one definitive remote sensing solution for any lake or group of lakes, and the method needs to be tailored to the lake size and optical complexity of the system and the spectral and spatial resolution of the sensor. There are three general categories of sensors for spaceborne remote sensing, including hyperspectral sensors, broadband medium spatial resolution sensors, and narrow band low spatial resolution satellite sensors. The satellite sensor’s spatial resolution will determine the minimum lake size that can be monitored via remote sensing, while the sensor’s spectral resolution will determine the ability of the sensor to differentiate optically active constituents. Algorithms for remote sensing of water constituents can be divided into empirical, semi-analytical, or analytical methods. Empirical methods are applicable where there is a simple relationship between the constituent of interest (e.g., chlorophyll a) and reflectance and are therefore usually limited to lakes where only one water quality constituent dominates reflectance. Semi-analytical algorithms can often be applied in place of empirical algorithms and have a number of advantages. Semi-analytical algorithms can be developed independently of in situ samples, are applicable to multiple satellite sensors, have greater spatiotemporal applicability, and are designed to determine more than one water quality parameter simultaneously. Analytical methods are based on radiative transfer modelling or simplifications thereof.

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Authors and Affiliations

  1. Environmental Research Institute, The University of Waikato, Hamilton, New Zealand

    Mathew G. Allan & Chris G. McBride

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  1. Mathew G. Allan
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Editors and Affiliations

  1. Australian Rivers Institute, Griffith University, Brisbane, Australia

    David P. Hamilton

  2. Environmental Research Institute, The University of Waikato, Hamilton, New Zealand

    Kevin J. Collier

  3. NIWA (National Institute of Water and Atmospheric Research), Hamilton, New Zealand

    John M. Quinn

  4. NIWA (National Institute of Water and Atmospheric Research), Christchurch, New Zealand

    Clive Howard-Williams

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Allan, M.G., McBride, C.G. (2018). Remote Sensing of Water Quality. In: Hamilton, D., Collier, K., Quinn, J., Howard-Williams, C. (eds) Lake Restoration Handbook. Springer, Cham. https://doi.org/10.1007/978-3-319-93043-5_14

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