Water Mixing Conditions Influence Sentinel-2 Monitoring of Chlorophyll Content in Monomictic Lakes

Abstract

Prompt estimation of phytoplankton biomass is critical in determining the ecological quality of freshwaters. Remote Sensing (RS) may provide new opportunities to integrate with situ traditional monitoring techniques. Nonetheless, wide regional and temporal variability in freshwater optical constituents makes it difficult to design universally applicable RS protocols. Here, we assessed the potential of two neural networks-based models, namely the Case 2 Regional CoastColour (C2RCC) processor and the Mixture Density Network (MDN), applied to MSI Sentinel-2 data for monitoring Chlorophyll (Chl) content in three monomictic volcanic lakes while accounting for the effect of their specific water circulation pattern on the remotely-sensed and in situ data relation. Linear mixed models were used to test the relationship between the remote sensing indices calculated through C2RCC (INN) and MDN (IMDN), and in situ Chl concentration. Both indices proved to explain a large portion of the variability in the field data and exhibited a positive and significant relationship between Chl concentration and satellite data, but only during the mixing phase. The significant effect of the water circulation period can be explained by the low responsiveness of the RS approaches applied here to the low phytoplankton biomass, typical of the stratification phase. Sentinel-2 data proved their valuable potential for the remote sensing of phytoplankton in small inland water bodies, otherwise challenging with previous sensors. However, caution should be taken, since the applicability of such an approach on certain water bodies may depend on hydrological and ecological parameters (e.g., thermal stratification and seasonal nutrient availability) potentially altering RS chlorophyll detection by neural networks-based models, despite their alleged global validity.

Mapping and Assessment of the Spatial and Temporal Distribution of Turbidity in Lake Buhi From Sentinel-2 Images Using Geographically Weighted Regression and Normalized Difference Turbidity Index

“…Relatively lower can be 𝑅 2 𝑅 2 attributed to the 8-day difference between the satellite overpass and field survey dates and the occurrence of a tropical depression within that timeline. Perrone et al (2021) found no negative effect on the accuracy of their predictive models using such a time window, however, only under stable weather conditions.…”

Synergistic Use of Earth Observation Driven Techniques to Support the Implementation of Water Framework Directive in Europe: A Review

“…Table 4 provides examples of operational DCs initiatives around the globe related to the water ecosystem, proving that the big data management is not an excuse for not using new technologies when there are existing tools that are being applied with great success. Our work highlighted the recent trend to use EO big data processing platforms that are also able to execute user-defined algorithms by the various research groups which was difficult to deploy on the existing geographical information systems as a simple plugin, such as the Semi-automatic Classification Plugin [88].…”

A multivariate linear regression model for estimating chlorophyll-a concentration in Quan Son Reservoir (Hanoi, Vietnam) using Sentinel-2B Imagery

“…Currently, various optical satellite imaging systems have been utilized for estimating Chla, including the Coastal Zone Colour Scanner (CZCS) (Conkright et al, 2003), Seaviewing Wide Field Sensor (SeaWiFS) (Gholizadeh et al, 2016), Medium Resolution Imaging Spectrometer (MERIS) (Moses et al, 2012;Augusto-Silva et al, 2014), Moderate Resolution Imaging Spectroradiometer (MODIS) (Ogashawara et al, 2014;Li et al, 2019), Geostationary Ocean Colour Imager (GOCI) (Kim et al, 2016), Ocean and Land Colour Instrument (OLCI) (Werther et al, 2021;Kravitz et al, 2020), Landsat 8 OLI (Kuhn et al, 2019;Pu et al, 2019;Cao et al, 2020), and Sentinel-2 (S2) (Sent et al, 2021;Ogashawara et al, 2021;Niroumand-Jadidi et al, 2021). Among the numerous optical satellite datasets available, Sentinel-2B MSI (S2B) data have garnered recognition as a promising tool for Chla retrieval in inland waters (Niroumand-Jadidi et al, 2021;Perrone et al, 2021). The selection of this sensor is primarily motivated by its temporal coverage (10 days), spatial resolution (up to 10 meters), and easy accessibility.…”