Dissolved oxygen (DO)

Satellite(s)

Aqua MODIS and Suomi-NPP VIIRS.

Monitoring element

Water reflectance.

Satellite(s)

Aqua MODIS and Suomi-NPP VIIRS.

Monitoring element

Water reflectance.

Description technique

Kim et al. (2020) estimated coastal water dissolved oxygen using a multiple regression model based on the comparison of in situ-DO observation data with satellite-derived water temperature and Chlorophyl-a concentration.

Accuracy / Resolution

Absolute Relative Error, 1-ARE (89.2%), and Index of Agreement, IOA (78.6%).

Spatial resolution 1 km.

Case study

Eastern coastal region of the Yellow Sea, Korea (Kim et al. 2020).

Also fits domain

Freshwater

Benefits

  • Capability of satellite remote sensing in monitoring coastal water DO, that can extend (spatially and temporally) current monitoring.

  • Can provide long-term variability patterns.

Limitations

  • Low spatial resolution of 1 km.

  • Approach non applicable in area where strong vertical variability exists.

Applicability for Northland

Yes, 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

Kim YH, Son S, Kim H-C, Kim B, Park Y-G, Nam J, Ryu J. 2020. Application of satellite remote sensing in monitoring dissolved oxygen variabilities: A case study for coastal waters in Korea. Environment international.

https://www.sciencedirect.com/science/article/pii/S0160412019327291

Other references

Sharaf El Din E, Zhang Y, Suliman A. 2017. Mapping concentrations of surface water quality parameters using a novel remote sensing and artificial intelligence framework. International Journal of Remote Sensing. 38(4):1023-1042. doi:10.1080/01431161.2016.1275056.

https://www.tandfonline.com/doi/citedby/10.1080/01431161.2016.1275056?scroll=top&needAccess=true