1.

Introduction

Chlorophyll-a (Chl-a) is the dominant pigment of phytoplankton, and its concentration is often used as a proxy for phytoplankton biomass making Chl-a one of the most studied indicators of water quality.^1,2 The wide ecological importance of phytoplankton also causes the extensive need to get accurate Chl-a data at different temporal and spatial scales. Chl-a is an optically active component, i.e., it affects water reflectance (color). Consequently, it can be estimated by remote sensing.

The European Space Agency opened a new era in water remote sensing by launching twin satellites Sentinel-3A and -3B (Sentinel-3) with Ocean and Land Color Imager (OLCI) sensor in 2016 and 2018, respectively. Previous watercolor sensors, such as the medium resolution imaging spectrometer (MERIS) on Environmental Satellite (ENVISAT), were one-off scientific missions while Sentinels Program guarantees now availability of data for decades to come allowing thus monitoring of water quality. Sentinel-3 is specially designed for marine monitoring and has 21 well placed spectral bands for that purpose with medium spatial resolution (300-m pixel). It provides global coverage (at the equator) every two days.³ At the latitude of the Baltic Sea two Sentinel-3 satellites provide two images per day.

Two identical multispectral instruments (MSIs) onboard the Sentinel-2A and -2B (Sentinel-2) were launched in 2015 and 2017, respectively. Sentinel-2 was designed for land monitoring but has proved to be suitable for estimating water quality as well.⁴ It has 13 spectral bands, which offer high resolution optical imagery at 10, 20, and 60 m spatial resolution, depending on the band. This mission provides global coverage every 5 days.⁵ At the latitude of the Baltic Sea a Sentinel-2 images can be acquired every 2 to 3 days.

The Baltic Sea is the world’s largest inland brackish water sea, where the combination of a large catchment area with a high rate of human activities and a small volume with limited exchange with the Atlantic Ocean makes it especially sensitive to eutrophication.⁶ Also, the Baltic Sea is notorious for its cyanobacterial blooms,^7–10 which lead to many serious ecological problems. Concentration of Chl-a is spatially and temporally very variable in the Baltic Sea. Moreover, unlike most of phytoplankton, cyanobacteria can move in the water column and the vertical distribution of cyanobacteria has significant impact on the remote sensing signal.¹¹ There are two biologically and optically distinct phytoplankton seasons separated by a relatively clear water period.¹² Diatoms dominate the spring blooms and cyanobacteria dominate summer/late summer/early autumn blooms.

Estonian marine waters include different regions of the Baltic Sea: the Gulf of Finland, the Gulf of Riga, and the northern Baltic Proper. Those areas belong to the most eutrophicated parts of the Baltic Sea.¹³ Furthermore, high spatial and temporal variation of optical properties of water (the color and transparency) is characteristic for these areas making them an extremely complex study object for ocean color remote sensing. In addition to great variations in Chl-a concentrations, a high amount of colored dissolved organic matter (CDOM) received from the catchment area of the Baltic Sea makes the water dark, lowering the water leaving signal and requiring highly sensitive remote sensing sensors and very accurate atmospheric correction.⁴ Highly variable concentration of total suspended matter (TSM) is also characteristic for coastal waters in Eastern and Southern parts of the Baltic Sea besides Chl-a and CDOM. This is caused by shallow and sandy coastal areas where wind causes resuspension of sediments. Finally, we must not forget that the sun angle is low during most of the year in the Baltic Sea area lowering the water leaving signal detectable by remote sensing sensors and increasing sun and sky glint which is noise if we want to map water quality parameters such as Chl-a. More than 90% of signal measured by satellites above water bodies originates from atmosphere not from water. Therefore, it is obvious that even a small error in atmospheric correction of the dark water imagery may be as large as the whole body of water leaving signal.

Despite this, Chl-a has been estimated with remote sensing techniques in the Baltic Sea region numerous times before.^{4,8,9,14–25} In general, these algorithms can provide high accuracy at certain times and areas, but usually, the algorithm performance is not adequate in other conditions. Ligi et al.²³ and Simis et al.²⁶ have shown that optical differences between the two different bloom seasons are so high that seasonal remote sensing algorithms may be needed to achieve higher accuracy in Chl-a mapping. Regardless of the issues, and even if the statistical error of the Chl-a products is as high as 100% to 200%, remote sensing has proven to be a valuable method for monitoring water quality, due to its advantage in temporal and especially spatial coverage compared with in situ methods.^27,28

There are numerous remote sensing and modeled Chl-a products for the Baltic Sea which are publicly available and can be freely used by all. Their users belong to very different interest groups from the policymakers (the European Commission, HELCOM, local authorities etc.) and researchers to the common public. Nevertheless, it is extremely challenging for the end-users to decide which product to choose. Therefore, the objective of this study was to assess the performance of the freely available remote sensing and modeled Chl-a products of the Baltic Sea using in situ observations from Estonian marine waters. Additionally, the performance of different well known band ratio algorithms (BR) was tested.

2.

Materials and Methods

2.1.

In Situ Data

The in situ Chl-a data were collected from Estonian coastal and territorial waters during ice-free periods in 2016 and 2018–2021 (Fig. 1). The total number of sampling stations was 267. Water samples were collected from the surface layer (between the surface and 0.5-m depth) and stored in dark and cold for $<10\mathrm{h}$ before filtering. Depending on particle concentration in the water, 0.5 to 1 l was filtered through two parallel Whatman glass microfiber filters (GF/F pore size $0.7\mu \mathrm{m}$). Phytoplankton pigments were extracted from the filters with 96% ethanol at 20°C for 24 h and optical density was measured with PERKIN ELMER Lambda 35 UV/VIS spectrophotometer. Later, formula by Jeffrey and Humphrey²⁹ was applied and mean of the two parallels was taken to calculate the Chl-a values.

Fig. 1

The locations of the sampling stations.

3.

Results

3.1.

In Situ Database

We carried out in situ measurements in five years covering different parts of Estonian coastal and territorial waters (Fig. 1). More measurements were carried out in the western part of the Gulf of Finland and around the largest islands, less coverage was in the eastern part of the Gulf of Finland.

All the measurements were done from March to October, mostly from April to July, when the most cloud free days occurred (Table 3).

Table 3

The temporal distribution of the in situ Chl-a, n denotes the number of measurements.

Year	n	Month	n
2016	34	March	12
2018	55	April	54
2019	46	May	53
2020	80	June	43
2021	52	July	50
		August	33
		September	12
		October	10
Total	267	Total	267

The in situ Chl-a dataset combines 267 unique measurements ranging from 0.4 to $45.9\mathrm{mg}{\mathrm{m}}^{-3}$. Although the range is $45.5\mathrm{mg}{\mathrm{m}}^{-3}$, then the mean of the dataset is $6.9\mathrm{mg}{\mathrm{m}}^{-3}$ and median is $4.9\mathrm{mg}{\mathrm{m}}^{-3}$, leaving most of the measured values under $8\mathrm{mg}{\mathrm{m}}^{-3}$ (Fig. 2; Table 4). The Chl-a concentration that exceeds the threshold of $5\mathrm{mg}{\mathrm{m}}^{-3}$ is often considered as bloom in the Baltic Sea. As a background information, the absorption coefficient of CDOM at a wavelength of 420 nm ranged from 0.42 to $12.84{\mathrm{m}}^{-1}$ (mean $1.53{\mathrm{m}}^{-1}$ and median $0.87{\mathrm{m}}^{-1}$) and TSM from 0.60 to 29.40 (mean $8.45\mathrm{mg}{\mathrm{m}}^{-3}$; median $8.06\mathrm{mg}{\mathrm{m}}^{-3}$) for the same dataset.

Fig. 2

Box plot of the in situ Chl-a dataset ($n=267$). Gray color shows Chl-a values from first Q to median, orange from median to third Q, and $X$ marks the mean value.

Table 4

Descriptive statistics of the in situ Chl-a (mg m−3) dataset: t is the time period, n is the number of the measurements, Q is the quartile, and StDev is the standard deviation of the dataset.

t	13/04/2016 – 08/10/2021
$n$	267
Min	0.36
First Q	2.07
Median	4.87
Third Q	7.92
Mean	6.89
Max	45.89
StDev	7.69
Range	45.53

3.2.

Chlorophyll-a Products

In this study, we used 16 freely available Chl-a products including 3 modeled and 13 remote sensing products that are available for the Baltic Sea. Five of the products are available in two different spatial resolutions (Table 1). The number of found match-ups was from 28 to 262 (Table 5), depending on the satellite data and quality flags used for each product (Table 1). The products that were based on Sentinel-3 data had generally more match-ups than the ones based on Sentinel-2 data due to the higher revisit time.

Table 5

Performance of the different Chl-a remote sensing products: n denotes the number of match-ups, R2 is the determination coefficient, and p-value is the statistical significance of the linear regression. MAPE in % and root mean square error (RMSE, mg m−3) show the uncertainties of the products. Bias is the systematic error. The products are in the decreasing order of the R2.

Chl-a algorithm	n	R2	p-value	MAPE	RMSE	Bias
BR8_S3	125	0.671	$<0.0001$	63.3	3.89	0.86
C2RCC_S3	125	0.548	$<0.0001$	74.2	4.50	−0.88
BR3_S2	77	0.496	$<0.0001$	58.7	3.64	0.95
MCI_S3	156	0.456	$<0.0001$	126.6	6.26	$-1.30$
ST_S3	160	0.393	$<0.0001$	90.8	5.03	0.62
BaltAlg_S3	28	0.338	0.001	118.8	4.08	1.19
POLYMER_S2_HR	77	0.310	$<0.0001$	75.5	3.98	0.03
C2X_S2_HR	65	0.268	$<0.0001$	66.7	4.70	$-0.88$
COMPLEX_S2_LR	52	0.241	0.0002	128.4	8.79	3.12
COMPLEX_S2_HR	52	0.209	0.0007	127.0	8.88	3.26
C2X_S2_LR	65	0.206	0.0001	66.3	5.27	$-0.55$
POLYMER_S2_LR	72	0.178	0.0002	75.3	4.23	$-0.04$
POLYMER_S3	141	0.101	0.0001	113.6	3.78	1.01
ONNS_S3_HR	45	0.078	0.06	94.3	9.23	0.99
SatBaltyk_model	262	0.072	$<0.0001$	92.4	8.12	$-3.24$
ERGOM_model	178	0.058	0.001	162.1	10.24	1.96
ONNS_S3_LR	94	0.053	0.03	456.3	22.12	6.98
CMEMS_model	167	0.024	0.05	166.6	10.91	$-2.38$
HR-OC_S2	42	0.023	0.33	108.1	5.86	1.11
OC4ME_S3	37	0.016	0.45	295.7	14.33	7.17
FLH_S3	163	0.013	0.16	157.3	6.57	0.35
C2RCC_S2_LR	62	0.003	0.69	118.1	6.48	$-0.08$
C2RCC_S2_HR	62	$<0.001$	0.97	118.7	6.63	$-0.24$

The $R^2$ of the Chl-a products were ranging from $<0.001$ up to 0.55 given by C2RCC_S3 product. This and maximum chlorophyll index_S3 (MCI_S3) are the two Chl-a products available with the highest $R^2$, but C2RCC_S3 has better higher MAPE and RMSE than MCI_S3 [Fig. 3(a)].

Fig. 3

Comparison of the in situ and the best (a) available remote sensing Chl-a products; and (b) BR for Sentinel-2 (POLYMER_S2_HR and BR3_S2) and -3 (C2RCC_S3 and BR8_S3).

Generally, products based on Sentinel-3 data were performing better than the products based on Sentinel-2 or MODIS. The best product based on Sentinel-2 data was POLYMER_S2_HR [Fig. 3(a)]. The weakest performance was by the C2RCC_S2_LR and the ocean Chl-a products [fluorescence line height_S3 (FLH_S3), OC4ME_S3]. These results were also not statistically significant. The uncertainties were high for all the products, MAPE ranging from 66% to 456%. RMSE ranged from 3.8 to $22.1\mathrm{mg}{\mathrm{m}}^{-3}$ (Table 5).

The BR8 (Table 2) based on Sentinel-3 TOA reflectances outperformed all the other Chl-a products (BR8_S3, Table 5), also the BR3 (Table 2) on Sentinel-2 POLYMER_S2_HR reflectance had high $R^2$ with the lowest MAPE (59%) and RMSE ($3.6\mathrm{mg}{\mathrm{m}}^{-3}$) of all (BR3_S2, Table 5) [Fig. 3(b)]. All the $R^2$ from band ratio testing are shown in Table 6. We chose atmospheric processors based on the results of the Chl-a products (C2RCC for Sentinel-3, and POLYMER and C2X for Sentinel-2).

Table 6

The determination coefficients (R2) for each tested band ratio (BR; the formulae of the band ratios are in Table 2). The band ratios were tested on: Sentinel-3 OLCI TOA and C2RCC reflectances (S3 TOA and S3 C2RCC, accordingly); Sentinel-2 MSI TOA, Case-2Extreme, and POLYMER reflectances (S2 TOA, S2 C2X, S2 POLYMER, accordingly). The highest values for S3 and S2 are shown in bold. n notes the number of match-ups.

	S3 TOA	S3 C2RCC	S2 TOA	S2 C2X	S2 POLYMER
$n$	125	125	66	66	77
BR1	0.139	0.245	0.059	0.214	0.121
BR2	0.554	0.482	0.239	0.269	0.019
BR3	0.598	0.552	0.236	0.267	0.496
BR4	0.013	0.171	0.029	0.145	0.047
BR5	0.028	0.529	0.061	0.200	0.093
BR6	0.228	0.169	0.078	0.013	$<0.001$
BR7	0.337	0.217	0.324	0.233	0.293
BR8	0.671	0.576	0.380	0.273	0.032

4.

Discussion

The in situ measurements covered the seasonal variations of phytoplankton (spring bloom, summer minimum and cyanobacteria bloom) giving a good sense of the state in the Baltic Sea (Fig. 1 and Table 3). About 267 unique in situ measurements showed variability with range of $45.5\mathrm{mg}{\mathrm{m}}^{-3}$, but concentrations of Chl-a remained mostly between 2 and $8\mathrm{mg}{\mathrm{m}}^{-3}$ (first to third Q) with average $7\mathrm{mg}{\mathrm{m}}^{-3}$ and median $5\mathrm{mg}{\mathrm{m}}^{-3}$ (Fig. 2; Table 4).

Only five Chl-a products out of 21 had $R^2>0.3$ and only six products had RMSE $<5\mathrm{mg}{\mathrm{m}}^{-3}$ (median of the in situ database). The best performing available Chl-a remote sensing product was C2RCC_S3 ($R^2=0.55$, $\mathrm{RMSE}=4.5\mathrm{mg}{\mathrm{m}}^{-3}$, MAPE = 74%, and $n=125$). This product has shown good performance also in other parts of the Baltic Sea. Kyryliuk and Kratzer²⁴ tested it in the west coast of the Baltic Sea (Sweden) and found strong relationship with in situ data ($R^2=0.79$ and $n=27$). Kratzer and Plowey²⁵ found the similar correlations with our results ($R^2=0.56$ and $n=59$) in the North-Western part of the Baltic Sea.

The six products, that had lowest RMSE, were all remote sensing products of Chl-a. Three of them were the POLYMER Chl-a products (Table 5). While POLYMER products showed one of the lowest RMSE, then C2X products had lowest MAPE (66% to 67%). Although, MCI_S3, ST_S3, and BaltAlg_S3 remote sensing products had stronger relationship with in situ, they also had much higher uncertainties, so those products might not be reliable. In addition, we can without doubt claim that all model-based products (CEMS_model, ERGOM_model, and SatBaltyk_model) and almost half of the remote sensing products (C2RCC_S2_HR, C2RCC_S2_LR, FLH_S3, HR_OC_S3, OC4ME_S3, ONNS_S3_HR, and ONNS_S3_LR) failed to retrieve Chl-a in Estonian marine waters: $R^2<0.1$, $\mathrm{MAPE}>90\%$, $\mathrm{RMSE}>5\mathrm{mg}{\mathrm{m}}^{-3}$, and $p$-value mostly showed no statistical significance and in most of the cases, remote sensing products outperformed modeled Chl-a products (Table 5). Overall, the C2RCC_S3 and POLYMER_S2_HR products have reasonably high $R^2$ and lower errors than other products (Fig. 3 and Table 5).

From eight different band ratios tested, the Sentinel-3 TOA BR2, BR3, BR8, and BOA BR3 and BR8 outperformed all the available Chl-a products for the Baltic Sea (Table 6). The best was BR8 on TOA reflectances ($R^2=0.67$, $n=125$, and MAPE = 63%). With Sentinel-2 data the BRs were not as successful, no BR outperformed the best Chl-a available product (C2RCC_S3). For Sentinel-2 data, the most successful were BR3 on POLYMER ($R^2=0.50$, $n=77$, and $\mathrm{MAPE}=59\%$) and BR8 on TOA reflectances (Table 6). POLYMER showed the best suitability as atmospheric correction in the Baltic Sea area for Sentinel-2 MSI data. The same conclusion was made by Warren et al.⁴⁶ Overall, BR3 and BR8 were the most successful and seemed to work better with Sentinel-2 and -3 TOA data. The fact that the best BR algorithm was based on TOA reflectances, is nothing new. The TOA band ratios have been tested before.^4,45 Even more, Soomets et al.⁴⁵ found also BR8 and BR3 most successful in inland waters with low Chl-a ($\sim 5\mathrm{mg}{\mathrm{m}}^{-3}$ which agrees well with Chl-a median of the present study). Both the BR3 and BR8 are essentially algorithms that are using the depth Chl-a absorption feature and a peak at 700 to 710 nm against the Chl-a absorption maximum (reflectance minima). BR8 only normalizes it to a SWIR band to minimize atmospheric and glint effects. The fact that empirical algorithms using distinct spectral features in the TOA data perform better than any other method on atmospherically corrected data suggests that atmospheric corrections still need improvements.

The high uncertainties of the results are due to the optically complex nature of the Baltic Sea which has high temporal and spatial variability of the Chl-a and turbidity caused by the high loads of organic matter and nutrients from the rivers, shipping, or other human activities.¹⁰ In the cyanobacterial bloom, that occurs in the Baltic Sea every summer, the Chl-a often vary by 2–3 orders of magnitude over the distance of a few meters.¹⁴ It makes the development of remote sensing algorithms very difficult as water sample collected in one point may not have anything in common with concentration obtained for a satellite pixel which is 300 × 300 m in size.¹⁴ The same problem occurs in validation of satellite products—in situ sampling has to be carried out in very precise location in space and time and even then, may not be useful if water around the sampling station is very heterogenous.

The best available Chl-a product and the tested BR algorithms showed high errors: RMSE from 3.6 to $4.5\mathrm{mg}{\mathrm{m}}^{-3}$ is too high for the in situ dataset with the median of $4.9\mathrm{mg}{\mathrm{m}}^{-3}$ and this might influence the reliability of the derived concentration of Chl-a. Regardless of the high errors, the tested BR algorithms show still more promise in accuracy of deriving Chl-a in the Baltic Sea than any of the evaluated products. Especially, the model (Level 4) results were very poor and are not useful for smaller nor larger scale analysis. We can say the same to all the Copernicus Marine Service Level 3 products, except for BaltAlg_S3. This product is competitive by its results, although it is still not preferable (usable in larger analysis) because of the lack of valid data. There were only 28 match-ups, which is noticeable less than in any other product. Because there are still room for improvement in deriving Chl-a in Baltic Sea, it is necessary to continue validation of the remote sensing products.

5.

Conclusions

Both modeled and remote sensing-based, altogether 21 different Chl-a products of the Baltic Sea, were tested on in situ data collected in Estonian marine waters during 2016–2021. In addition, eight different empirical band ratio type algorithms were tested on TOA and BOA reflectances of Sentinel-3 and Sentinel-2. The best performing Chl-a product was C2RCC of Sentinel-3 OLCI ($R^2=0.55$, $\mathrm{RMSE}=4.5\mathrm{mg}{\mathrm{m}}^{-3}$, MAPE = 74%, and $n=125$). All modeled Chl-a products and about half of the remote sensing Chl-a products failed to produce reasonable results. The empirical BR with Sentinel-3 data were in fact more successful than any of the available remote sensing or modelling products: $R^2=0.67$, $\mathrm{RMSE}=3.9\mathrm{mg}{\mathrm{m}}^{-3}$, MAPE = 63%, and $n=125$. Our results showed better performance of the TOA reflectance, which suggests that there is still room for improvement of atmospheric correction methods. Also, high uncertainties of the retrieved Chl-a concentrations (products or band ratio algorithms) due to the complex waters of the Baltic Sea, show that future validation and improvement of Chl-a deriving methods are still needed.