Coastal / ocean water quality

Satellite(s) ENVISAT/MERIS, Sentinel-2/MSI, Sentinel-3/OLCI.	Monitoring element Water reflectance.

Satellite(s)

ENVISAT/MERIS, Sentinel-2/MSI, Sentinel-3/OLCI.

Monitoring element

Water reflectance.

Description technique

Arabi et al. (2020) retrieved a 15-year diurnal variation of Water Constituent Concentrations (WCCs) from multi-sensor satellite images and in-situ hyperspectral measurements, using Radiative Transfer (RT) modeling.

Accuracy / Resolution

Strong agreement between in-situ and satellite-derived WCC values:

Chlorophyll-a:
R² ≥ 0.70, RMSE ≤7.5 mg.m^-3
Suspended Particulate Matter:
R² ≥ 0.72, RMSE ≤5.5 g.m^-3
Colored dissolved organic matter absorption at 440 nm:
R² ≥ 0.67, RMSE ≤1.7 m^-1.

Case study

Dutch part of the Wadden Sea (Arabi et al. 2020).

Also fits domain

Freshwater

Benefits

The method delivered reliable WCC maps from earth observations data over complex coastal waters.
Satellite water quality products have moderate pixel sizes and therefore represent an average over a few hundred meters, which might be more representative than point measurements.
The integration of spatio-temporal WCC data obtained from in-situ measurements and satellite images can support coastal management, help detect anomaly events and serve as a warning for actions in coastal areas.

Limitations

Optical imagery and in-situ hyperspectral measurements can be contaminated by cloud cover, sunlight, etc. The method applies to a selection of high-quality images/measurements.
Satellite based reflectance values need to be atmospherically corrected to be compared to in-situ measurements.

Applicability for Northland

Yes, very likely.

Remote sensed data would need to be ground-truthed to confirm applicability/accuracy in the Northland context.

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 references

Arabi B, Salama MS, Pitarch J, Verhoef W. 2020. Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas. Remote Sensing of Environment. 239:111632. doi:10.1016/j.rse.2020.111632.

https://www.sciencedirect.com/science/article/pii/S0034425720300018

Other comments or information

Although the higher-spatial resolution satellite sensor MSI is suitable for land monitoring application, this sensor has a lower signal-to noise ratio in comparison to other ocean color satellites like MERIS and OLCI and is not the most suitable one for aquatic remote sensing.

Other references

McCarthy MJ, Colna KE, El-Mezayen MM, Laureano-Rosario AE, Méndez-Lázaro P, Otis DB, Toro-Farmer G, Vega-Rodriguez M, Muller-Karger FE. 2017. Satellite Remote Sensing for Coastal Management: A Review of Successful Applications. Environmental Management. 60(2):323-339. doi:10.1007/s00267-017-0880-x.

https://link.springer.com/article/10.1007/s00267-017-0880-x

Pitarch J, van der Woerd HJ, Brewin RJW, Zielinski O. 2019. Optical properties of Forel-Ule water types deduced from 15 years of global satellite ocean color observations. Remote Sensing of Environment. 231:111249. doi:10.1016/j.rse.2019.111249.

https://www.sciencedirect.com/science/article/pii/S0034425719302688