ISSN 0001-4338, Izvestiya, Atmospheric and Oceanic Physics, 2016, Vol. 52, No. 9, pp. 988–998. © Pleiades Publishing, Ltd., 2016. Original Russian Text © P.A. Salyuk, I.E. Stepochkin, O.A. Bukin, E.B. Sokolova, A.Yu. Mayor, J.V. Shambarova, A.R. Gorbushkin, 2016, published in Issledovanie Zemli iz Kosmosa, 2016, No. 1–2, pp. 161–172. КОСМИЧЕСКИЕ ИССЛЕДОВАНИЯ МОРЕЙ И ОКЕАНОВ Determination of the Chlorophyll a Concentration by MODIS-Aqua and VIIRS Satellite Radiometers in Eastern Arctic and Bering Sea P. A. Salyuka,b*, I. E. Stepochkinb, O. A. Bukinb, E. B. Sokolovab,c, A. Yu. Mayorc, J. V. Shambarovaa, and A. R. Gorbushkind aPacific Oceanological Institute, Far East Branch, Russian Academy of Science, Vladivostok, Russia b Maritime State University, Vladivostok, Russia c Institute of Automation and Control Processes, Far East Branch, Russian Academy of Science, Vladivostok, Russia d Moscow State University, Moscow, Russia *e-mail: pavel.salyuk@gmail.com Received June 1, 2014 Abstract⎯The waters of the Bering and Chukchi seas, as well as the De Long Strait, are investigated based on the data obtained in August 2013 during the scientific expedition of the Far Eastern Floating University on the research vessel Professor Khlyustin. Chlorophyll a concentrations calculated from MODIS-Aqua and VIIRS satellite data by ocean color and obtained by means of shipboard flow-through fluorometric measurements are comparatively analyzed. Vessel data are corrected for standard spectrophotometric measurements and the vertical depth distribution of phytoplankton. It has been found that, in the waters of the Eastern Arctic, satellite radiometers showed overestimated chlorophyll a concentrations in the upper seawater layer visible from the satellite. This is associated with the additional contribution of colored dissolved organic matter in the sea surface color. In the De Long Strait, satellite measurements incorrectly estimate the depth integrated chlorophyll a concentration, since the bulk of phytoplankton cells at a chlorophyll a concentration of 10–20 g/L is at depths of 25–30 m with luminosity of 5%. Keywords: chlorophyll a, dissolved organic matter, MODIS-Aqua, VIIRS, De Long Strait, Chukchi Sea, Bering Sea, Eastern Arctic, depth distribution, sea surface color, satellite radiometer DOI: 10.1134/S0001433816090206 INTRODUCTION It is known that the operating parameters of phytoplankton communities are important indicators of the state of marine ecosystems and are used in the study of various anthropogenic and natural processes. For example, the sensitivity of phytoplankton cells to changes in the environment can be estimated by the concentration of the main pigment, chlorophyll a, the primary production, species composition, photosynthesis efficiency, etc. The analysis of the spatial and temporal distribution of these parameters is important because of the rapid response of phytoplankton communities to the slightest change in climate or human economic activities. That is especially true for the polar regions, which are most sensitive to different types of impacts. As the temperature rises, ice, snow cover, and permafrost melt and river flows increase in the Arctic, which, in turn, leads to an increase in the light and nutrient regimes of phytoplankton cells (Arrigo et al., 2012; Belanger et al., 2013). This circumstance can increase the biological productivity, and in some areas even change the species composition of phytoplankton communities (Belanger et al., 2013; Vetrov and Romankevich, 2011; Vetrov and Romankevich, 2014; Petrenko et al., 2013). According to the forecasts from (Popova et al., 2013), there will be further growth in primary production in Arctic waters. As the total biomass of phytoplankton cells and dissolved organic matter (DOM) produced by them increase, there can be additional positive feedback of warming in the Arctic, since these factors significantly affect the absorption of light by seawater (Pegau, 2002; Chang and Dickey, 2004). However, negative feedbacks caused by a greater fixation of CO2 from the atmosphere during photosynthesis are also possible (Raven and Falkowski, 1999). Currently, the method of satellite remote sensing of ocean color as a means of global monitoring of the state of phytoplankton communities is the most efficient in terms of financial costs, especially for such hard-to-reach areas as the Arctic. The data can be used to calculate sea currents, environmental monitoring of the sea surface, bioefficiency estimation, and investigation and prediction of climate changes. 988 DETERMINATION OF THE CHLOROPHYLL a CONCENTRATION When using the method in the polar regions, it is necessary to take into account a number of specific conditions that can affect the quality of the received satellite information (Kravchishina et al., 2013; Kuznetsova et al., 2013; Burenkov et al., 2011; Petrenko et al., 2012): the presence of ice, frequent cloudiness, low zenith angles of the Sun, low atmospheric aerosol concentration, special stratification, functional condition, and species composition of phytoplankton communities. Simultaneously, the relevance of general problems of passive satellite optical monitoring remains. These include the problem of correctly determining sea brightness coefficients and the chlorophyll a concentration in coastal waters and in waters subject to the influence of river runoffs (Remote…, 2000; Salyuk et al., 2013a; Bondur and Grebenyuk, 2001; Bondur, 2004) and the solution of inverse problems of optical radiation passing through the atmosphere (Kopelevich et al., 2009). In the case of global estimates of bioefficiency changes in the Arctic waters, the above factors or some of them are often not considered because of the lack of information about their significance and impact on the calculation of the parameters that characterize the functioning of phytoplankton communities. The purpose of this investigation is to analyze the accuracy of the determination of the chlorophyll a concentration from satellite radiometers MODISAqua and VIIRS in the waters of the Bering Sea and Eastern Arctic and to identify the main factors leading to the observed errors. N 72° 989 De Long Strait East Siberian Sea CHUKCHI SEA 69° 66° 63° 60° BERING SEA 57° 162° 168° 174 ° 180° 174 ° 168° W Fig. 1. Investigation area. Gray points indicate simultaneous measurements of the flow-through shipboard and satellite data. White circles refer to simultaneous flowthrough and submersible shipboard measurements. Crosses show submersible shipboard measurements in the De Long Strait. INVESTIGATION REGIONS toms, and other groups of algae prevail locally. The spatial distribution of these complexes is largely determined by the local peculiarities of the hydrological and nutrient regimes of the investigated regions (Sergeeva et al., 2010). The investigations were carried out in the Bering and Chukchi seas and in the De Long Strait in August 2013 (see Fig. 1). During this period, the sea surface was free of ice except for the part of the De Long Strait where rapid melting was observed that led to the freshening of the upper 10-m seawater layer up to a salinity of about 29‰. Many biooptical measurements were carried out in the Bering Sea and the eastern part of the Chukchi Sea. Descriptions of these measurements are stored in the databases on the concentration of chlorophyll a and primary production in (Ardyna et al., 2013; Petrenko et al., 2012). It should be noted that in the western part of the Chukchi Sea and in the De Long Strait far fewer such measurements were conducted. One of the reasons was difficult ice conditions in the region. In the Arctic seas in the ice-free water, one peak of phytoplankton bloom is usually observed that falls within the period from mid-July to September. However, a two-peak surge is also common. It is not that dissipative in time, and both peaks are quite close to each other (Sergeeva et al., 2010; Ardyna et al., 2013). It was established that during the summer the main dominant group in the Chukchi and Bering seas is dia- METHODS AND INSTRUMENTS Shipboard Measurements Three water intake methods were used to measure the chlorophyll a concentration (variable C): during vessel movement from the system pumping water from a depth of 5 m (flow index), during stops using submersible profiler (CTD index), and using the bathometer. For an analysis of the factors leading to the errors of the satellite determination of the chlorophyll a concentration, the temperature and salinity of seawater, photosynthetically active radiation (PAR, PAR variable), and the concentration of colored dissolved organic matter (CDOM, D variable) were additionally determined. The following methods and instruments were used: (1) During the vessel movement, the fluorescence spectra, temperature, and salinity of seawater from the pumping systems were measured and additional PAR measurements on the deck were carried out. The instrument complex included the following devices: laser flow-through fluorometer (Maior et al., 2011.), SeaBird SBE-45 thermosalinograph, Licor LI-190R IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS Vol. 52 No. 9 2016 990 SALYUK et al. PAR sensor, and a GPS navigator. A detailed description of the complex was given in (Nagornyi et al., 2014). Fluorometer measurements were carried out with a spectral resolution of 1 nm and accumulation time of 20 s, which corresponds to a spatial resolution of 113 m at a cruising speed of 11 knots. Fluorescence spectra of seawater normalized to the Raman scattering intensity of seawater were used to calculate fluorescence intensity of chlorophyll a I C and the fluorescence intensity of CDOM I D ; (2) A SeaBird 19-plus submersible profiler with standard pressure, temperature, and seawater-salinity sensors was used during vessel stops. In addition, pump-through fluorometric sensors of the concentration of chlorophyll a and CDOM, WETLabs WETStar-chlA, and WETStarCDOM, respectively, as well as a spherical PAR sensor Licor LI-193, were installed on the profiler. Calibration factors of the WETStarchlA sensor were obtained under laboratory conditions by the manufacturer by comparing the fluorescence intensity of chlorophyll a with the concentration of its extracted molecules. WETStar-CDOM sensor data were calibrated by the manufacturer to a solution of quinine sulfate dihydrate in laboratory conditions. It is known that in situ results of fluorometric measurements depend on changes in the species composition and functional state of phytoplankton cells, as well as on the pump operation in the flow-through system, which significantly affects the concentration of living cells in water samples (Didenko et al., 1985). Therefore, during the voyage, necessary regular calibrations of such measurements at different levels of PAR and concentrations of chlorophyll a and CDOM were carried out. In order to achieve this goal, seawater samples from the flow-through system of the ship and from the sea surface were taken regularly during vessel movement, and, during stops, in sync with the work of the SeaBird SBE-19plus profiler, samples of seawater were taken at different depths using a bathometer. In total, 60 samples were taken during the voyage. In the samples, the chlorophyll a concentration was determined by the standard spectrophotometric method according to GOST (State Standard) 17.1.4.02-90. Fluorometric measurements obtained using the flow-through system of the ship (C flow , Dflow ) and a submersible profiler (C CTD, DCTD ) were brought to a single units of measurement of the chlorophyll a concentration corresponding to standard measurements according to GOST and to CDOM concentration units based on the WETStar- CDOM sensor calibration. For each depth profile C CTD(z) and DCTD(z), optically balanced concentrations were determined, which theoretically should be observed from the satellite (index ow) (Salyuk et al., 2010; Smith and Baker, 1981). Integrals in terms of depth values (index z) were also calculated. ow(z ) = ( PARCTD(z )) , 2 z 99 ow C CTD = (1) z 90 ∫C CTD ( z )ow( z )dz 0 ∫ ow(z)dz , (2) 0 z bot z C CTD = 1 z bot C = ow C flow ∫ C(z)dz, (3) 0 = pc1C flow + pc2, (4) where z is the depth, z90 is the depth at which the PAR is 10% of the value above the sea surface, zbot is the bottom depth, and pс1 and pс2 are coefficients of linear regression that recalculate the values obtained in the flow-through system into optically weighted values. The coefficients were obtained by comparing C flow w and C CTD . Similar calculations were carried out for the concentrations of CDOM D. Optically weighted values of C and D most correctly reflect the contribution of phytoplankton cells and CDOMs in the formation of the upward sea-surface radiation. These values should be compared with satellite data. Figure 2 shows a scatter plot between the values w w w z and C CTD (Fig. 2a), C = C flow and C CTD C = C flow (Fig. 2b). Flow-through measurements of C at a depth of 5 m are in good agreement with the results of submersible measurements averaged taking into account the optical weight and, thus, can be used for comparison with the satellite data (Fig. 2a). In this case, flowthrough and, thus, optically weighted measurements led to significant errors in the determination of the chlorophyll a concentration averaged over the entire sea thickness, although in general they reflected the overall biological productivity of marine waters (Fig. 2b). A particularly strong mismatch was observed in the area of the De Long Strait, where averaged-overdepth values were overestimated with respect to the expected values by about 4–5 times. Satellite Measurements Level 2 Ocean Color satellite data obtained by radiometers MODIS-Aqua and VIIRS installed on Aqua and Suomi-NPP satellites, respectively, and processed by NASA procedures Reprocessing 2013.1 (Ocean…, 2014) were used for the analysis. The first radiometer has been operating since 2002 and has established itself as a reliable instrument for determining the chlorophyll a concentration in the waters of the first optical type. Data from the second radiometer have been available since 2012. Thus, the analysis of the accuracy of VIIRS measurements is of particular interest. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS Vol. 52 No. 9 2016 DETERMINATION OF THE CHLOROPHYLL a CONCENTRATION The spatial resolution of satellite images at the nadir was MODIS-Aqua ≈ 1000 and VIIRS ≈ 750 m. For each of the radiometers, sea-color indices were calculated using the following formulas: for the MODIS-Aqua radiometer (OC3M index), Rrs ( 443) > Rrs ( 486) (6) , Rrs (551) where Rrs(λ) is the sea brightness factor at a given wavelength λ. Satellite chlorophyll a concentrations were calculated by the recommended global biooptical algorithms OC3M and OC3V (Werdell, 2010) ROC3V = lg C OC3M = 10 ^ (0.2424 − 2.7423ROC3M 2 3 4 + 1.8017ROC3M + 0.0015ROC3M − 1.2280ROC3M ), C OC3V = 10 ^ (0.2228 − 2.4683ROC3V 2 3 4 + 1.5867ROC 3V − 0.4275ROC 3V − 0.7768ROC3V ). 2.5 ow CCTD , mg/m3 (5) (a) 3.0 2.0 1.5 1.0 0.5 0 1 2 3 2 3 С, mg/m3 (7) (b) 3.0 (8) Comparison of Shipboard and Satellite Data Shipboard measurements obtained in the flowthrough system were used for comparison with the satellite data. Their advantage is a high spatial resolution that makes it possible to filter outliers, to take into account the presence of sharp gradients of the analyzed variables, and to accumulate statistics sufficient for the analysis during a single voyage. Since the results of the comparison of shipboard and satellite data depend on the temporal and spatial scales of averaging, interpolation, and approximation, the comparison was carried out in the ranges of distances dr and time dt between shipboard and satellite measurements. The following values were selected for the analysis: dr = ±1, ±2, ±4, ±6, ±8 km; dt = ±1, ±2, ±3, ±4, ±6 h. At the first stage of comparison, the entire series of shipboard measurements was divided into individual samples with a set of different values of dr and dt. For each sample, mean and median values, standard deviation, spatial and temporal gradients of changes in the concentration of chlorophyll a, and CDOMs were calculated. Samples with high relative measurement errors, samples on the fronts of the fields of its concentration and CDOM, and samples in the presence of significant diurnal variations were filtered. At the second stage, pixels on satellite images with centers spaced by less than dr and measured within the time dt were selected with respect to the average geographic location and measurement time of each shipboard sample. If one and the same satellite pixel could IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS 2.5 ow CCTD , mg/m3 Rrs ( 443) > Rrs ( 489) , Rrs (547) and for the VIIRS radiometer (OC3V index), ROC3M = lg 991 2.0 1.5 1.0 0.5 0 1 С, mg/m3 Fig. 2. Scatter plots of shipboard flow through (abscissa) and submersible measurements of the chlorophyll a concentration (y axis). (a) Submersible data are averaged taking into account the optical weight based on formula (2). (b) Submersible data are averaged over the sea thickness by formula (3). be attributed to two different shipboard samples, the one with a shorter distance to the center was selected. Statistical processing similar to the procedure described above for shipboard samples was carried out for every resulting sample of satellite data. As a result, arrays of simultaneously measured shipboard and satellite data were obtained with statistical estimates of the measurement accuracy and filVol. 52 No. 9 2016 992 SALYUK et al. (a) (b) 1.5 1.5 Algorithm OC3M 1.0 0.5 0.5 lg(C) lg(C) 1.0 Algorithm OC3V 0 0 Flag HISOLZEN ΔROC3M ΔROC3V –0.5 –1.0 –0.5 –0.5 0.5 0 1.0 ROC3M –1.0 –0.5 0 1.0 0.5 ROC3V Fig. 3. Example of the comparison of the common logarithm of shipboard flow-through measurements of the chlorophyll a concentration (y axis) and satellite measurements of sea color index (x axis) for the scales dr = ±2 km and dt = ±2 h. Black points indicate measurements in the Eastern Arctic: (a) satellite data from the MODIS-Aqua radiometer and (b) satellite data from the VIIRS radiometer. tered outliers, where each sample was independent of the others. During the comparative analysis, color indices calculated by satellite measurements according to formulas (5) and (6) were compared with the decimal logarithm of the chlorophyll a concentration measured at the vessel, lg(C). The accuracy of global algorithms (7) and (8) was estimated. For this purpose, the determination coefficient R2, the relative RMS error of the chlorophyll a concentration determination CVRMSE, and the median value of the coefficient of proportionality between the satellite and the shipboard estimates of the chlorophyll a concentration biasmed were calculated. number, and COC3 is the satellite estimation of the chlorophyll a concentration made by formula (7) or (8) depending on the satellite radiometer used. The samples used for analysis were prefiltered to eliminate the effect of outliers on the resulting estimates. For the calculations according to formulas (9), (10), and (11), preliminary samples were eliminated for which a null value of the weighting coefficient (DuMouchel and O’Brien, 1989) was calculated according to the biquadratic function of the vector of residuals between the points on the scatterplot lg(С)– lg(COC3) and the curve defined by algorithm (7) or (8). N R2 = 1 − ∑ (C i − C OC3 i ) RESULTS OF THE COMPARISON OF SHIPBOARD AND SATELLITE DATA 2 i =1 ⎛ 1 ⎜C i − ⎜ N i =1 ⎝ N ∑ 2 (9) , ∑ (C i − C OC3 i ) 2 CVRMSE = NN ∑C ⎞ Ci ⎟ ⎟ ⎠ i =1 N N , (10) i i =1 ⎛C ⎞ (11) bias med = median ⎜ OC3 i ⎟ , ⎝ Ci ⎠ where N is the number of simultaneously measured correct samples of shipboard and satellite data for the given averaging scales dr and dt, i is the sample serial Figure 3 shows an example of a scatterplot of seacolor indices determined by satellite radiometers and a common logarithm of the shipboard measured chlorophyll a concentration with a comparison scale dr = ±2 km and dt = ±2 h. Coordinates of simultaneous measurements are shown in Fig. 1 by solid gray dots. Points related to Arctic waters of more than 66°N are marked with black on the scatterplots in Fig. 3. Biooptical algorithms (7) and (8) in Figs. 3a and 3b, respectively, are marked with a solid curve. The position of points in the scatterplots to the left of the curves indicates that satellite measurements overestimate the chlorophyll concentration. The points circled in dotted lines are statistical outliers and are not used in further calculations. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS Vol. 52 No. 9 2016 DETERMINATION OF THE CHLOROPHYLL a CONCENTRATION ANALYSIS OF THE CAUSES LEADING TO A MISMATCH BETWEEN SHIPBOARD AND SATELLITE DATA Let us analyze what can cause gross outliers, errors, and biases in the determination of the concentration of chlorophyll a from MODIS-Aqua and VIIRS radiometers. Quality Flags of Satellite Data Measurements For all satellite samples selected for comparative analysis, quality flags l2_flags from the array of satellite data of the second level were analyzed. Part of the outliers in the MODIS-Aqua data were systematically caused by the HISOLZEN flag, which indicates that the measurements were carried out at a high solarzenith angle, which was observed in the Arctic waters. An example of such outliers is shown in Fig. 3a, where the points are circled with dotted lines. Such points were not observed in VIIRS data. It should be noted that, in the third-level, data points with this flag are automatically filtered for all satellite radiometers. The remaining outliers did not have any flag that would systematically indicate satellite data-processing IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS CCTD 0 2.2 4.4 6.6 8.8 11 DCTD 0.9 0 2.2 4.4 6.6 8.8 11 662.7 828.3 3 2 10 4 z, m Because of the fact that the VIIRS radiometer has a better resolution of the measured satellite images, the example for VIIRS in Fig. 3 made it possible to obtain more comparison points. This was observed by us in almost all combinations of dr and dt used. A similar analysis was carried out for all values of dr and dt. Thus, in order to estimate the accuracy of each of the satellite radiometers, only those shipboard data samples were retained for which the weight ratio was greater than zero simultaneously for MODIS-Aqua and VIIRS. The comparison results are presented in the table. The coefficients were calculated separately for the waters of the Bering Sea and Eastern Arctic. From the analysis of the values of coefficients R2 and CVRMSE it can be seen that at small scales and of comparison of dr and dt (±(1–6) km and ±(1–4) h), VIIRS data provide better accuracy in determining the concentration of chlorophyll a. In the case of a largescale comparison (±8 km and ±6 h), MODIS-Aqua data provide comparable or better accuracy. In eastern Arctic waters, the VIIRS radiometer showed better results compared to the MODIS-Aqua scanner. For the VIIRS radiometer, the best concentration determination accuracy of chlorophyll a was achieved at dr = ±(4-8) and dt = ±(1–2) h. For the MODIS-Aqua radiometer, dr = ±8 and dt = ±(1–2) h. From the values of the coefficient biasmed, it can be seen that in general the estimates of analyzed radiometers give a correct idea about the chlorophyll a concentration in the upper layer of seawater. In eastern Arctic waters, from the satellites its concentration was overestimated by about 1.3–1.5 times. 993 20 1 30 40 PARCTD 0.1 ρCTD 1023 165.7 331.4 497 1023.7 1024.4 1025.1 1025.8 1026.5 Fig. 4. Depth distribution of the chlorophyll a concentration, in mg/m3 (curve 1), CDOM concentration, in mg/m3 (2), photosynthetically active radiation, μmol/m2/s (3), and sea water density, g/m3 (4). problems and they can be associated with both shipboard data-measurement errors and various satellite data-processing problems listed in INTRODUCTION. Vertical Distribution of Optically Active Seawater Components One of the reasons leading to significant errors in the determination of the chlorophyll a concentration in the upper layer of the sea is an incorrect account of the vertical distribution of the optically active seawater components. Figure 2a shows that flow-through measurements at a depth of 5 m adequately estimate the chlorophyll a concentration, which should be observed from the satellite. However, some points measured in the De Long Strait and indicated with crosses in Figs. 1 and 2b significantly fall out of the linear regression that connects surface measurements and measurements averaged over the entire seawater thickness. Let us analyze the corresponding vertical distributions of optically active seawater components. Figure 4 shows an example of the depth distribution C CTD(z), DCTD(z), PARCTD(z) , and seawater density ρ CTD(z), calculated from the measurements of temperature and salinity. Profiles are averaged in increments of 1 m. It can be seen that the bulk of phytoplankton in that region is at a depth of 25–30 m below the pycnocline, while at the maximum density jump there is almost no phytoplankton, and the CDOM is distributed according to seawater density gradients. Most likely, the light Vol. 52 No. 9 2016 994 SALYUK et al. Comparison of the accuracy of the determination of the chlorophyll a concentration from satellite radiometers VIIRS (V) and MODIS-Aqua (A) by OC3V and OC3M algorithms, respectively Bering Sea Δr, Regions Parameters km id N R2 Δt, h ±1 ±2 ±3 ±4 ±6 ±4 ±6 ±1 ±2 ±3 ±4 ±6 VA 93 137 167 175 224 – 24 93 145 174 185 250 ±2 VA 68 99 126 149 216 10 19 68 106 134 159 236 ±4 VA 36 59 76 91 131 – 11 37 63 80 100 143 ±6 VA 26 33 53 66 94 – – 27 42 59 72 102 ±8 VA 16 22 38 48 73 – – 17 31 42 54 79 A – – – – – – – – – – – – V 0.66 0.79 0.34 0.74 0.3 – – 0.66 0.8 0.36 0.66 0.34 A 0.01 – – – 0.37 – – 0.01 – – – 0.39 V 0.69 0.81 0.49 0.73 0.53 – – 0.69 0.82 0.51 0.74 0.54 A 0.12 0.06 0.3 0.27 0.65 – – 0.12 0.1 0.32 0.3 0.66 V 0.88 0.86 0.61 0.68 0.59 – 0.21 0.88 0.86 0.62 0.69 0.46 A 0.5 0.47 0.47 0.48 0.66 – – 0.49 0.51 0.45 0.49 0.67 V 0.87 0.83 0.75 0.52 0.59 – – 0.87 0.81 0.5 0.53 0.61 A 0.87 0.85 0.83 0.85 0.83 – – 0.85 0.84 0.83 0.82 0.83 V 0.87 0.85 0.7 0.72 0.53 – – 0.87 0.81 0.71 0.71 0.55 A 0.99 1.16 1.32 1.45 1.39 – 0.42 0.99 1.18 1.34 1.47 1.44 V 0.55 0.45 0.87 0.55 0.96 – 0.51 0.55 0.46 0.88 0.64 0.99 A 1.01 1.48 1.49 1.49 0.99 0.41 0.89 1.01 1.52 1.52 1.52 1.03 V 0.57 0.46 0.75 0.56 0.86 0.54 0.74 0.57 0.47 0.76 0.58 0.89 A 0.91 0.95 0.87 0.9 0.68 – 0.73 0.91 0.97 0.89 0.92 0.69 V 0.34 0.37 0.65 0.6 0.73 – 0.58 0.34 0.38 0.66 0.61 0.86 A 0.69 0.67 0.73 0.74 0.65 – – 0.69 0.74 0.77 0.76 0.67 V 0.35 0.37 0.5 0.72 0.72 – – 0.35 0.46 0.74 0.73 0.73 A 0.37 0.35 0.44 0.43 0.46 – – 0.4 0.43 0.45 0.47 0.47 V 0.37 0.35 0.58 0.58 0.77 – – 0.37 0.47 0.59 0.6 0.78 A 1.13 1.1 1.13 1.02 0.95 – 1.3 1.13 1.12 1.15 1.06 1.02 V 1.03 0.95 0.96 0.96 1.07 – 1.39 1.03 0.98 0.98 1.01 1.11 A 0.99 1.08 1.07 1.06 1.01 1.31 1.51 0.99 1.12 1.11 1.07 1.04 V 1.01 0.98 0.97 1 1.04 1.46 1.42 1.01 1.01 0.99 1.04 1.09 A 0.96 1 1.03 1.03 1.02 – 1.45 0.97 1.03 1.06 1.04 1.02 V 1.01 0.92 0.95 0.95 0.99 – 1.36 1.05 0.97 0.97 0.99 1.02 A 0.83 0.83 1.01 1.09 1.01 – – 0.83 0.94 1.08 1.11 1.03 V 0.9 0.86 0.92 0.97 1 – – 0.92 0.93 0.98 1.03 1.03 A 0.85 0.84 1 1.06 1.02 – – 0.86 0.96 1.06 1.06 1.05 V 0.83 0.84 0.94 0.95 0.97 – – 0.85 0.94 0.94 0.96 0.98 ±1 ±4 ±6 ±8 ±1 ±2 ±4 ±6 ±8 biasmed Both regions ±1 ±2 CVRMSE Eastern Arctic ±1 ±2 ±4 ±6 ±8 The “best” values of the considered parameters are given in bold face. IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS Vol. 52 No. 9 2016 DETERMINATION OF THE CHLOROPHYLL a CONCENTRATION 995 (b) (a) 6 1.5 5 1.0 1 0.5 lgC D, mg/m3 4 3 0 2 1 –0.5 1 2 0 2 4 6 8 C, mg/m3 10 12 –1 –0.5 0 ROC3V N 72° (c) 0.5 1.0 (d) 0.5 0.4 2 69° 0.3 2 66° ΔROC3M 0.2 0.1 63° 0 1 60° 1 –0.1 57° –0.2 –0.3 2 4 v = D/C 6 8 162° 168° 174 ° 180° 172 ° 168° W Fig. 5. Analysis of the influence of changes in the ratio between chlorophyll a and CDOM concentrations (ν) on the errors of the determination of the chlorophyll a concentration from the satellite at comparison scales between shipboard and satellite data of dr = ±2 km dt = ±2 h. Values of the coefficient ν are highlighted for all points: blue (1), 0.16; red (2), 0.84. (a) Scatter plot of shipboard chlorophyll a concentrations of (C) and CDOM (D). (b) Scatter plot of the sea index color ROC3V derived from data of the VIIRS satellite radiometer and the common logarithm marine of the shipboard chlorophyll a concentration. (c) Scatter plot of the coefficient ν and color index of deviation from the global biooptical algorithm OC3V ΔROC3V. (d) The map of distribution of points with the calculated ν coefficient. regime is the main influence on the distribution of phytoplankton in this region. In the maximum chlorophyll a concentration layer it is 3–5% of the surface irradiance or 50–60 μmol/m2/s, which corresponds to IZVESTIYA, ATMOSPHERIC AND OCEANIC PHYSICS the optimal illumination of algae in the Arctic waters (Sakshaug and Slagstad, 1991). This distribution can also be associated with the peculiarities of the species composition, algae development period, or mineral Vol. 52 No. 9 2016 996 SALYUK et al. nutrition regime. A similar pattern is usually observed for Arctic phytoplankton communities at the end of the flowering period (Ardyna et al., 2013). Thus, in the De Long Strait, satellite measurements were significantly not taking into account the average chlorophyll a concentration in the entire sea thickness. According to Fig. 2b, the overestimation is about 5–6 times, which is a significant error that must be taken into account in the study of seasonal and climatic changes in the functioning of phytoplankton communities in the region. Simultaneously, in order to estimate the primary production from the satellite, the error of measurement of the chlorophyll a concentration in dimly lit layers can be not as significant, since the corresponding models additionally take into account the illumination level (Behrenfeld and Falkowski, 1997), which partially offsets the influence of phytoplankton from the deep layers of cells on the total primary production calculation, but this requires separate studies. In Fig. 4, the increased CDOM concentration in the 5–20 m layer when compared with the chlorophyll a concentration is also of interest. It leads to the fact that in this sea-surface area the color will be determined more by the presence of CDOM than phytoplankton. Change of Concentration Ratios of Optically Active Components Сhlorophyll a concentrations in eastern Arctic waters obtained from the satellites are more overestimated both in the MODIS-Aqua scanner and VIIRS scanner assessments (see table and black dots in Fig. 3). As was noted above, the variation of ratios of chlorophyll a and CDOM concentrations in seawater can be a factor which can lead to errors of the determination of its concentrations from the satellites. Let us consider a scatter plot of shipboard measurements of chlorophyll a and CDOM concentrations (Fig. 5a). It can be seen that the points are divided into two main dependences. In the Arctic waters, the ratio of CDOM to chlorophyll a concentrations ν = D C is higher than in the Bering Sea. The latter can lead to the fact that the signal determined by the additional CDOM absorption is erroneously associated with the chlorophyll a absorption, which leads to an overestimation of its concentration from the satellite. In order to confirm this hypothesis, the dependence between the coefficient v and the value by which the color index changes in relation to the standard algorithm was built (Fig. 5b). Figure 5c shows that, approximately starting from ν = 5, a statistically significant correlation of the error Δ ROC3M on the D/C ratio can be seen. These dots refer entirely to the Arctic waters. Such a dependence was observed in (Bukin et al., 2010) in the Peter the Great Gulf. Figure 5 shows the results for the VIIRS radiometer. In the case of MODIS-Aqua, a similar pattern was observed. Therefore, it is possible to conclude that, in the Eastern Arctic in August 2013, errors that led to an overestimation of the apparent chlorophyll a concentration from the satellite were associated with the presence of CDOM in the upper sea layer. In this case, there is a combination of two errors. On the one hand, the satellite lowers depth integrated chlorophyll a concentrations because of the fact that the bulk of phytoplankton is in unlit lower layers and has little effect on the ocean color. On the other hand, there is a chlorophyll a concentration overestimation in the upper layers of the sea caused by the incorrect interpretation of the presence of additional CDOM. This problem can be resolved by the use of regional oceanographic and biooptical models that should be developed using autonomous underwater measuring systems capable of a year-round operation at different depth horizons or by the development of quasi-analytic biooptical algorithms, for which it is necessary to increase the spectral resolution of satellite radiometers in the visible range. CONCLUSIONS (1) The VIIRS scanner provided more correctly measured synchronous shipboard and satellite data. Since the shipboard data with the same spatial resolution were used in both cases, it is possible to conclude that the VIIRS scanner provides better spatial coverage of ocean color measurements when compared with the MODIS-Aqua scanner. The reason is the better spatial resolution of the VIIRS scanner, which is important at the boundary of high-quality data close to the shore, the ice edge, or clouds. (2) In the analyzed Bering Sea and eastern Arctic waters, the VIIRS radiometer provided more accurate measurements of the chlorophyll a concentration in comparison with the MODIS-Aqua radiometer on averaging scales up to ±6 km and ±4 h inclusively. (3) Both radiometers overestimated chlorophyll a concentrations visible to the satellite in eastern Arctic waters; this is associated with the high relative content of colored organic matter in the upper sea layers, which is erroneously interpreted by the global biooptical algorithms OC3M and OC3V as an additional contribution of phytoplankton. (4) The satellite data in the De Long Strait do not provide a complete idea of the total biomass of depth integrated phytoplankton, since the largest number of phytoplankton cells is at depths with a light level of 3– 5% of the surface level. 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