Water quality / algal blooms

Satellite(s) Landsat, Sentinel-2, Sentinel-3, MODIS, PRISMA, Hyperion	Monitoring element Water spectral reflectance

Satellite(s)

Landsat, Sentinel-2, Sentinel-3, MODIS, PRISMA, Hyperion

Monitoring element

Water spectral reflectance

Description technique

Flores et al. (2019) worked on deriving a relationship between water quality metrics (e.g. Chlorophyll-a content) and spectral reflectance.
For the purpose, in-situ water quality samples of Chlorophyll-a concentration were collected and correlated with satellite imagery water surface reflectance to develop a semi-empirical algorithm.

Accuracy / Resolution

Flores et al. (2019) reported a relative error of 33%.

Case studies

Lake Atitlan, a tropical mountain lake in Guatemala that exemplifies a freshwater body subjected to pressures that have increased over the years.
The Waikato Regional Council is also testing a range of RS platforms for applicability to monitoring lake water quality.

Benefits

Cheaper than in situ testing, provides regular, complete overview opposed to limited sampling, takes advantage of new remote sensing data and platforms (e.g. GEE).
Hyperion offers very fine spectral resolution (220 bands). The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chlorophyll-a concentrations in Lake Atitlán during the dry season.

Limitations

Need to cope with cloud contamination, specular reflection, interference from suspended solids and chromophoric dissolved organic matter, relatively low spectral resolution of sensors such as Landsat and Sentinel-2.
Hyperion has limited spatial (30m) and temporal (16-30d) resolutions. This will therefore limit the ability to represent cyanobacterial bloom spatial variability and revisit frequency be major constraint for water quality monitoring.

Applicability for Northland

Yes, the method could be used to supplement the existing Northland RC monitoring program.
Hyperion imagery coverage is very limited for Northland ( USGS EROS archive earth observing one eo-1 Hyperion), so other sources hyperspectral imagery would have to be used.

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

Flores-Anderson AI, Griffin R, Dix M, Romero-Oliva CS, Ochaeta G, Skinner-Alvarado J, Ramirez Moran MV, Hernandez B, Cherrington E, Page B, et al. 2020. Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala. Frontiers in Environmental Science. 8(7). doi:10.3389/fenvs.2020.00007.

Reference link Envirosat tools documentation

Other comments or information

The improving spatial resolution of spaceborne hyperspectral sensors should facilitate monitoring of smaller water features with greater granularity in the near future.
The algorithm developed could be tested/updated with operational multispectral sensors (e.g., Landsat and Sentinel-2). E.g., S2 provides a five-day revisit time and due to its medium spectral resolution, the advantage could be to combine both types of sensors.

Other references

Dörnhöfer K, Oppelt N. 2016. Remote sensing for lake research and monitoring – Recent advances. Ecological Indicators. 64:105-122. doi10.1016/j.ecolind.2015.12.009

https://www.sciencedirect.com/science/article/pii/S1470160X15007141

Papenfus M, Schaeffer B, Pollard AI, Loftin K. 2020. Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs. Environmental Monitoring and Assessment. 192(12):808. doi:10.1007/s10661-020-08631-5.

https://link.springer.com/article/10.1007%2Fs10661-020-08631-5

Arias-Rodriguez, L.F., Duan, Z., Sepúlveda, R., Martinez-Martinez, S.I. and Disse, M., 2020. Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches. Remote Sensing, 12(10), p.1586.

https://www.mdpi.com/2072-4292/12/10/1586

Toming K, Kutser T, Laas A, Sepp M, Paavel B, Nõges T. 2016. First Experiences in Mapping Lake Water Quality Parameters with Sentinel-2 MSI Imagery. Remote Sensing. 8(8):640.

https://www.mdpi.com/2072-4292/8/8/640

Tu M-C, Smith P, Filippi AM. 2018. Hybrid forward-selection method-based water-quality estimation via combining Landsat TM, ETM+, and OLI/TIRS images and ancillary environmental data. PLOS ONE. 13(7):e0201255. doi:10.1371/journal.pone.0201255.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201255

Niroumand-Jadidi, M., Bovolo, F. and Bruzzone, L., 2020. Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2. Remote Sensing, 12(23), p.3984.

https://www.mdpi.com/2072-4292/12/23/3984