Adaptation and Validation of a Sentinel-Based Chlorophyll-a Retrieval Software for the Central European Freshwater Lake, Balaton

Abstract

The importance of lakes and reservoirs leads to the high need for monitoring lake water quality both at local and global scales. Remote sensing is a rapidly evolving, versatile technology that can be successfully applied in several economic and scientific fields. Numerous studies demonstrate the applicability of satellites in algae detection and monitoring. Algae play an essential role in aquatic ecosystems, although their overgrowth poses a serious risk. Overgrowth of algae, also known as algal bloom, has serious ecological, social, economic and health effects. The research area of our study was Lake Balaton, the largest lake in Central Europe. The aim was to find the most appropriate algorithm(s) for the inland lake to define the chlorophyll-a amount. In addition, two new algorithms were developed based on the reflectance values of the satellite image. The results show that the two highest correlations were performed by the newly validated, blue/green ratio-based algorithm, Chl-aB/G (r = 0.93) and the chl_re_oc2 algorithm (r = 0.86) of the Acolite software. Although the Acolite software was not developed for lakes but for marine waters, it is also applicable to inland waters.

Assessing the Chlorophyll-a Retrieval Capabilities of Sentinel 3A OLCI Images for the Monitoring of Coastal Waters in Algoa and Francis Bays, South Africa

“…Table 5 presents the results of the analysis of variance (ANOVA) between image-based Chl-a concentrations and in-situ measurements. The statistical significance of correlations was determined by reference to critical values of the Pearson product-moment correlation coefficient, and the statistical significance of ANOVA was determined by reference to procedures provided at [98], with these tests being performed following recommendations provided by Tóth et al [99].…”

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir

“…Bramich et al (2021) found that using the Normalized Difference Chlorophyll Index (NDCI), which is what the Mishra algorithm is built upon, underestimated chl-a measurements especially when samples were greater than 30 μg/L. Recently, Tóth et al (2021) used ACOLITE and tested the Gons, Mishra, and Moses algorithms on S2 satellite data and found that SRS data overestimated chl-a if it was low (<10 μg/L) underestimated chl-a if it was too high (values ranged up to 653 μg/L). Algorithms such as Mishra specifically use formulations that combine reflectance data measured in both the red and near-infrared portions to be more robust to confounding water quality factors such as CDOM and other non-algal particulate matter such as suspended sediments that contribute to the overall turbidity of the water.…”