Water quality / ecosystem health / water colour

Owned by Maiwenn Herpe
Last updated: Jun 24, 2021 by Frederika Mourot
2 min read

Satellite(s)

Landsat-8 Operational Land Imager (OLI)

Monitoring element

Water spectral reflectance

Satellite(s)

Landsat-8 Operational Land Imager (OLI)

Monitoring element

Water spectral reflectance

Description technique

Water colour can be used as an intuitive water quality attribute that can be measured by satellite sensors without knowledge of inherent optical properties. Colour is linked to clarity, concentration of phytoplankton, suspended matter and colored dissolved organic matter.
Lehmann et al. (2018) developed a new algorithm for the retrieval of colour parameters (hue angle, dominant wavelength) and derived a new correction for colour purity to account for the spectral bandpass of the Landsat 8 Operational Land Imager (OLI).

Accuracy / Resolution

30-m pixel resolution allows for the observation of lakes down to about 1 ha size.

Case study

c. 45,000 observations over four years from 1486 lakes from a diverse range of optical water types in New Zealand (Lehmann et al., 2018).

Benefits

  • Enhancement of the current spatio-temporal monitoring of lakes in general in NZ.

  • Knowledge of inherent optical properties not required in this analysis.

Limitations

Clear sky is needed and limited revisit time of OLI (8 or 16 days, depending on location, which gives a maximum of 52-23 visit times per year).

Applicability for Northland

Yes, the method looks applicable.

However, spatial and temporal resolutions are relatively limited if using OLI data (e.g., <3 to <20 observations per year). Using other imagery with better resolution (e.g., Sentinel-2) would allow better conditions for environmental monitoring.

Techniques applying optical data will be limited in coverage and temporal granularity by the persistent cloud cover in the region, particularly during the winter months. Mature cloud-masking techniques are directly available for open access multispectral data (e.g. Landsat and Sentinel-2). When using commercial data, care must be taken to ensure that there is sufficiently cloud free imagery available, as cloud masking is not as mature and ordering a large volume of imagery to ensure complete cloud free coverage between multiple observations can become cost prohibitive.

Publication reference

Lehmann MK, Nguyen U, Allan M, Van der Woerd HJ. 2018. Colour Classification of 1486 Lakes across a Wide Range of Optical Water Types. Remote Sensing. 10(8):1273.

https://www.mdpi.com/2072-4292/10/8/1273

Other references

Liu X, Lee Z, Zhang Y, Lin J, Shi K, Zhou Y, Qin B, Sun Z. 2019. Remote Sensing of Secchi Depth in Highly Turbid Lake Waters and Its Application with MERIS Data. Remote Sensing. 11(19):2226.

https://www.mdpi.com/2072-4292/11/19/2226