Phycocyanin Monitoring in Some Spanish Water Bodies with Sentinel-2 Imagery

Abstract

Remote sensing is an appropriate tool for water management. It allows the study of some of the main sources of pollution, such as cyanobacterial harmful algal blooms. These species are increasing due to eutrophication and the adverse effects of climate change. This leads to water quality loss, which has a major impact on the environment, including human water supplies, which consequently require more expensive purification processes. The application of satellite remote sensing images as bio-optical tools is an effective way to monitor and control phycocyanin concentrations, which indicate the presence of cyanobacteria. For this study, 90 geo-referenced phycocyanin measurements were performed in situ, using a Turner C3 Submersible Fluorometer and a laboratory spectrofluorometer, both calibrated with phycocyanin standard, in water bodies of the Iberian Peninsula. These samples were synchronized with Sentinel-2 satellite orbit. The images were processed using Sentinel Application Program software and corrected with the Case 2 Regional Coast color-extended atmospheric correction tool. To produce algorithms that would help to obtain the phycocyanin concentration from the reflectance measured by the multispectral instrument sensor of the satellite, the following band combinations were tested, among others: band 665 nm, band 705 nm, and band 740 nm. The samples were equally divided: half were used for the algorithm’s calibration, and the other half for its validation. With the best adjustment, the algorithm was made more robust and accurate through a recalculation, obtaining a determination coefficient of 0.7, a Root Mean Square Error of 8.1 µg L−1, and a Relative Root Mean Square Error of 19%. In several reservoirs, we observed alarming phycocyanin concentrations that may trigger many environmental health problems, as established by the World Health Organization. Remote sensing provides a rapid monitoring method for the temporal and spatial distribution of these cyanobacteria blooms to ensure good preventive management and control, in order to improve the environmental quality of inland waters.

A Machine Learning and Remote Sensing‐Based Model for Algae Pigment and Dissolved Oxygen Retrieval on a Small Inland Lake

“…The relatively large 300 m spatial resolution of Sentinel‐3 imagery may also contribute to the underwhelming model performance for retrieval of all water quality variables. While Sentinel‐2 does not have the 620 nm band necessary to estimate phycocyanin concentrations directly, recent studies have successfully used Sentinel‐2 imagery to indirectly estimate phycocyanin (Beck et al., 2017; Pérez‐González et al., 2021; Sòria‐Perpinyà et al., 2020, 2021). The Sentinel‐2 models for phycocyanin and chlorophyll‐a select several of the same variables, yet the selection of several different algorithms for the phycocyanin model suggests a distinct statistical relationship between band reflectance and phycocyanin compared to chlorophyll‐a.…”

“…It should be noted that Sentinel‐2 cannot sense the 620 nm phycocyanin absorption feature commonly used in phycocyanin retrieval, however, several studies have found success in using Sentinel‐2 based chlorophyll‐a proxy algorithms for indirect phycocyanin estimation (Beck et al., 2017; Pérez‐González et al., 2021; Sòria‐Perpinyà et al., 2020). Therefore, a combination of proxy algorithms, machine learning techniques, and feature selection methods is used to develop and assess models for phycocyanin and chlorophyll‐a retrieval based on Sentinel‐2 imagery.…”

Identification of Algal Blooms in Lakes in the Baltic States Using Sentinel-2 Data and Artificial Neural Networks

“…In this study we analysed the algal and cyanobacterial blooms without specifying whether algae or cyanobacteria are the cause of the bloom. We used Sentinel-2 MSI data that are not as good for cyanobacteria observation as other satellite sensors, such as, Ocean and Land Colour Instrument onboard Sentinel-3 that has a spectral band at the wavelengths of 620 nm for detection of absorption of cyanobacteria-specific pigment phycocyanin [46]. Moreover, since more than half of lakes in the region are smaller than 1 km 2 we used data from a satellite with higher spatial resolution (Sentinel-2) as opposed to 300 m resolution Sentinel-3.…”

Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

“…More recently, the launch of Sentinel-2 MSI (Multiple Spectral Instrument) and Lansat-8 OLI (Operational Land Imager) has provided the acquisition of data that could be comparable and with higher spatial resolution (10-60 m and 30 m, respectively), which is more adequate to monitor medium to small lakes and reservoirs [165][166][167][168]. However, the design of their imagery sensors was not originally thought for aquatic appli-cations and the lack of an orange band (~620 nm) has been challenging and has required the development of complex algorithms for PC estimation [169,170].…”