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Phytoplankton primary production (PP) in lakes play an important role in the global carbon cycle. However, monitoring the PP in lakes with traditional complicated and costly in situ sampling methods are impossible due to the large number of lakes worldwide (estimated to be 117 million lakes). In this study, bio-optical modelling and remote sensing data (Sentinel-3 Ocean and Land Colour Instrument) was combined to investigate the spatial and temporal variation of PP in four Baltic lakes during 2018. The model used has three input parameters: concentration of chlorophyll-a, the diffuse attenuation coefficient, and incident downwelling irradiance. The largest of our studied lakes, Võrtsjärv (270 km2), had the highest total yearly estimated production (61 Gg C y−1) compared to the smaller lakes Lubans (18 Gg C y−1) and Razna (7 Gg C y−1). However, the most productive was the smallest studied, Lake Burtnieks (40.2 km2); although the total yearly production was 13 Gg C y−1, the daily average areal production was 910 mg C m−2 d−1 in 2018. Even if lake size plays a significant role in the total PP of the lake, the abundance of small and medium-sized lakes would sum up to a significant contribution of carbon fixation. Our method is applicable to larger regions to monitor the spatial and temporal variability of lake PP.

Lake productivity is fundamental to biogeochemical budgets as well as estimating ecological state and predicting future development. Combining modelling with Earth Observation data facilitates a ne...

Climate change is expected to continue in the 21st century, but the magnitude of change depends on future actions. In the Baltic Sea, specifically in the Pärnu Bay region, this is predicted to mean warmer temperatures, less ice cover, more precipitations and a slight increase in average wind speed, furthermore extreme climatic events such as heavy rains, strong winds and storms will be more intense and frequent. The coastal waters play a central role in humans and nature's everyday lives as providing food, living and recreational opportunities. Since Pärnu Bay is one of the most eutrophied area in the Baltic Sea and provides living hood more the 800 fishermen, then regular monitoring is strongly recommended, but with traditional methods often unfeasible. The availability of free Sentinel satellites data with good spectral, spatial and temporal resolution has generated wide interest in how to use remote sensing capabilities to monitor coastal waters water quality, which affects the underwater light field and can lead even to changes in fish composition. However, these waters are optically complex and influenced independently by coloured dissolved organic matter, phytoplankton and an amount of suspended sediments. Therefore, the remote sensing of optically complex waters is more challenging, and standard remote sensing products often fail. In this study, we use satellite Sentinel-3 data to investigate weather phenomena as strong wind and precipitations effect to Pärnu Bay water quality parameters. We study the spatial and temporal scope of change of water quality parameters after the event. For that, we use optical water type classification based chlorophyll-a, suspended sediments and coloured dissolved organic matter algorithms on Sentinel-3 images and estimate underwater light field changes. Furthermore, we also use in situ data to analyses the frequency and the strength of weather events. Finally, we look at the composition of fish based on literature and we investigate the possible effects of the change of the underwater light field on fish composition.

Inland waters, including lakes, are one of the key points of the carbon cycle. Using remote sensing data in lake monitoring has advantages in both temporal and spatial coverage over traditional in-situ methods that are time consuming and expensive. In this study, we compared two sensors on different Copernicus satellites: Multispectral Instrument (MSI) on Sentinel-2 and Ocean and Land Color Instrument (OLCI) on Sentinel-3 to validate several processors and methods to derive water quality products with best performing atmospheric correction processor applied. For validation we used in-situ data from 49 sampling points across four different lakes, collected during 2018. Level-2 optical water quality products, such as chlorophyll-a and the total suspended matter concentrations, water transparency, and the absorption coefficient of the colored dissolved organic matter were compared against in-situ data. Along with the water quality products, the optical water types were obtained, because in lakes one-method-to-all approach is not working well due to the optical complexity of the inland waters. The dynamics of the optical water types of the two sensors were generally in agreement. In most cases, the band ratio algorithms for both sensors with optical water type guidance gave the best results. The best algorithms to obtain the Level-2 water quality products were different for MSI and OLCI. MSI always outperformed OLCI, with R2 0.84&ndash, 0.97 for different water quality products. Deriving the water quality parameters with optical water type classification should be the first step in estimating the ecological status of the lakes with remote sensing.

Inland waters play a critical role in our drinking water supply. Additionally, they are important providers of food and recreation possibilities. Inland waters are known to be optically complex and more diverse than marine or ocean waters. The optical properties of natural waters are influenced by three different and independent sources: phytoplankton, suspended matter, and colored dissolved organic matter. Thus, the remote sensing of these waters is more challenging. Different types of waters need different approaches to obtain correct water quality products; therefore, the first step in remote sensing of lakes should be the classification of the water types. The classification of optical water types (OWTs) is based on the differences in the reflectance spectra of the lake water. This classification groups lake and coastal waters into five optical classes: Clear, Moderate, Turbid, Very Turbid, and Brown. We studied the OWTs in three different Latvian lakes: Burtnieks, Lubans, and Razna, and in a large Estonian lake, Lake Võrtsjärv. The primary goal of this study was a comparison of two different Copernicus optical instrument data for optical classification in lakes: Ocean and Land Color Instrument (OLCI) on Sentinel-3 and Multispectral Instrument (MSI) on Sentinel-2. We found that both satellite OWT classifications in lakes were comparable (R2 = 0.74). We were also able to study the spatial and temporal changes in the OWTs of the study lakes during 2017. The comparison between two satellites was carried out to understand if the classification of the OWTs with both satellites is compatible. Our results could give us not only a better overview of the changes in the lake water by studying the temporal and spatial variability of the OWTs, but also possibly better retrieval of Level 2 satellite products when using OWT guided approach.

Summary Remote sensing studies published up to now show that the performance of empirical (band-ratio type) algorithms in different parts of the Baltic Sea is highly variable. Best performing algorithms are different in the different regions of the Baltic Sea. Moreover, there is indication that the algorithms have to be seasonal as the optical properties of phytoplankton assemblages dominating in spring and summer are different. We modelled 15,600 reflectance spectra using HydroLight radiative transfer model to test 58 previously published empirical algorithms. 7200 of the spectra were modelled using specific inherent optical properties (SIOPs) of the open parts of the Baltic Sea in summer and 8400 with SIOPs of spring season. Concentration range of chlorophyll-a, coloured dissolved organic matter (CDOM) and suspended matter used in the model simulations were based on the actually measured values available in literature. For each optically active constituent we added one concentration below actually measured minimum and one concentration above the actually measured maximum value in order to test the performance of the algorithms in wider range. 77 in situ reflectance spectra from rocky (Sweden) and sandy (Estonia, Latvia) coastal areas were used to evaluate the performance of the algorithms also in coastal waters. Seasonal differences in the algorithm performance were confirmed but we found also algorithms that can be used in both spring and summer conditions. The algorithms that use bands available on OLCI, launched in February 2016, are highlighted as this sensor will be available for Baltic Sea monitoring for coming decades.

ESA Copernicus program will secure availability of satellite data for monitoring European seas during coming decades. The main sensor for water quality remote sensing will be OLCI on Sentinel-3 satellites. Sentinel-3A and 3B have been launched and the follow-up sensors are in preparation process. Baltic Sea is an optically complex waterbody where retrieving water quality parameters is complicated e.g. the only product provided by the Copernicus Marine Environment Monitoring Service (CMEMS) is chlorophyll-a (Chl-a) that does not have correlation with in situ data (r2=0.20). Likewise, EUMETSAT standard product is suitable for open ocean data not for the Baltic Sea. However, the EUMETSAT product contains also concentrations of Chl-a, TSS and CDOM produced with the C2RCC processor which may produce realistic results for the Baltic Sea. We used Chl-a data from the national monitoring program to validate OLCI results and used extra information from bio-optical cruises as a background information. Different processors and empirical algorithms were tested. CMEMS OLCI Level 2 (atmospherically corrected images) are currently not usable in the Baltic Sea and the reflectances produced by EUMETSAT are unrealistic in bloom conditions. We tested neural network type processors like the C2RCC. It does provide reflectance spectra that are realistic in non-bloom conditions, but the results are problematic in cyanobacterial bloom conditions. Some empirical band ratio algorithms performed reasonably well in estimating chlorophyll-a, suspended matter and CDOM concentrations. Comparisons with monitoring data showed that remote sensing products fail in spring bloom conditions, but produce reasonable results (r2=0.47) during summer phytoplankton minimum and cyanobacteria season. It became obvious that quality of regular monitoring data is not of sufficient to be used in developing and validation of remote sensing products.

Many lakes in boreal and arctic regions have high concentrations of CDOM (coloured dissolved organic matter). Remote sensing of such lakes is complicated due to very low water leaving signals. There are extreme (black) lakes where the water reflectance values are negligible in almost entire visible part of spectrum (400-700 nm) due to the absorption by CDOM. In these lakes, the only water-leaving signal detectable by remote sensing sensors occurs as two peaksnear 710 nm and 810 nm. The first peak has been widely used in remote sensing of eutrophic waters for more than two decades. We show on the example of field radiometry data collected in Estonian and Swedish lakes that the height of the 810 nm peak can also be used in retrieving water constituents from remote sensing data. This is important especially in black lakes where the height of the 710 nm peak is still affected by CDOM. We have shown that the 810 nm peak can be used also in remote sensing of a wide variety of lakes. The 810 nm peak is caused by combined effect of slight decrease in absorption by water molecules and backscattering from particulate material in the water. Phytoplankton was the dominant particulate material in most of the studied lakes. Therefore, the height of the 810 peak was in good correlation with all proxies of phytoplankton biomasschlorophyll-a (R-2 = 0.77), total suspended matter (R-2 = 0.70), and suspended particulate organic matter (R-2 = 0.68). There was no correlation between the peak height and the suspended particulate inorganic matter. Satellite sensors with sufficient spatial and radiometric resolution for mapping lake water quality (Landsat 8 OLI and Sentinel-2 MSI) were launched recently. In order to test whether these satellites can capture the 810 nm peak we simulated the spectral performance of these two satellites from field radiometry data. Actual satellite imagery from a black lake was also used to study whether these sensors can detect the peak despite their band configuration. Sentinel 2 MSI has a nearly perfectly positioned band at 705 nm to characterize the 700-720 nm peak. We found that the MSI 783 nm band can be used to detect the 810 nm peak despite the location of this band is not in perfect to capture the peak.