Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data

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Abstract

A 15-year (1997-2012) time series of chlorophyll a (Chl a) in the Baltic Sea, based on merged multi-sensor satellite data was analysed. Several available Chl a algorithms were sea-truthed against the largest in situ publicly available Chl a data set ever used for calibration and validation over the Baltic region. To account for the known biogeochemical heterogeneity of the Baltic, matchups were calculated for three separate areas: (1) the Skagerrak and Kattegat, (2) the central Baltic, including the Baltic Proper and the gulfs of Riga and Finland, and (3) the Gulf of Bothnia. Similarly, within the operational context of the Copernicus Marine Environment Monitoring Service (CMEMS) the three areas were also considered as a whole in the analysis. In general, statistics showed low linearity. However, a bootstrapping-like assessment did provide the means for removing the bias from the satellite observations, which were then used to compute basin average time series. Resulting climatologies confirmed that the three regions display completely different Chl a seasonal dynamics. The Gulf of Bothnia displays a single Chl a peak during spring, whereas in the Skagerrak and Kattegat the dynamics are less regular and composed of highs and lows during winter, progressing towards a small bloom in spring and a minimum in summer. In the central Baltic, Chl a follows a dynamics of a mild spring bloom followed by a much stronger bloom in summer. Surface temperature data are able to explain a variable fraction of the intensity of the summer bloom in the central Baltic.

Figures

  • Table 1. Summary of the algorithms used in the validation analysis with the acronym used in this work along with the required input for each of them. GLC stands for GlobColour, OC4v6 for Ocean Colour four-band algorithm (version 6), OC5 for Ocean Colour five-band algorithm, and MLP for multi-layer perceptron.
  • Figure 1. (a) Spatial distribution of the 4492 in situ stations used in the matchup analysis (see Sect. 3.1) along with the partition of the area of study. The Skagerrak and Kattegat is highlighted in blue with 1456 matchup points, Central Baltic is highlighted in red with 2922 matchup points, and the Gulf of Bothnia is green with 114 stations. Temporal station distribution is also shown using the same colour code (b). The frequency distribution of the entire in situ Chl a is shown in panel (c).
  • Figure 2. Density scatter plots of in situ vs. satellite-retrieved Chl a for all algorithms providing meaningful values. The line of best fit (blue) and that of equal value (black) are superimposed, with relevant statistics.
  • Figure 3. Density scatter plots of in situ vs. satellite-retrieved Chl a for the OC4v6 algorithm. The best linear regression (blue) and the line of equal value (black) are superimposed, with relevant statistics.
  • Figure 4. Upper left panels, in black: best linear fits (slope m and intercept n) of 1000 randomly chosen calibration data sets (Ncal = 2246, x axis) of log10 (CHL ain situ) vs. log10(CHL aOC4v6). Lower left panel: application of all 1000 (m, n) pairs to the OC4v6 vs. in situ scatter cloud. In red, slope and intercept for the whole data set, as shown in Fig. 3a. In green, average of the 1000 calibration results. Right panels, in black: statistics when applying each m and n pair from the left side to the complementary validation data sets (Nval = 2246, x axis). These are the coefficient of determination, bias (Eq. 1), and rms (Eq. 2). Same statistics found for the whole data set, as shown in Fig. 3a, are in red. The average of the 1000 validation results is in green.
  • Figure 5. Histogram of the absolute error between OC4v6corr and in situ Chl a, both in logarithmic form. Associated mean and standard deviation are also shown and used to compute a relevant fitted Gaussian distribution (black line).
  • Figure 6. Chl a daily climatology. For any given day of the year, the average was computed only if data for a minimum of 6 years were available. Plots of individual time series with their associated standard deviation bars can be found in the Supplement. To improve the plot readability, all time series were smoothed with a 1-week moving average.
  • Figure 8. Time series of the Chl a and SST anomalies with respect to their climatologies, over the Central Baltic. The reference value 0 is also displayed. Shaded areas indicate the part of the time series not used for the computation of the cross-correlation coefficient, which is indicated on each year. Full size plots of individual years can be found in the Supplement.

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CITATION STYLE

APA

Pitarch, J., Volpe, G., Colella, S., Krasemann, H., & Santoleri, R. (2016, March 8). Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data. Ocean Science. Copernicus GmbH. https://doi.org/10.5194/os-12-379-2016

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