3. White Sea

3.1. Satellite data

The whole basin was divided into 7 sub-regions according to position of main sources of suspended matter and processes forming TSM distribution (Fig. 7) [24]. Fig.8 shows coverage of all of the sub-regions by MODIS-Aqua data (see the comment in 2.1).

3.2. Algorithms

Development of the regional algorithms is usually based on concurrent field measurements of normalized water-leaving radiances LWN and of concentration of seawater constituents (total suspended matter TSM and chlorophyll-a Chl concentrations). Field measurements of LWN in the White Sea are not numerous and carried out mainly in relatively clear waters, whereas the TSM and Chl determinations are numerous because they are made on the ship way. On the strength of that, the following procedure was applied to derive regional bio-optical algorithms for the White Sea.

Chlorophyll concentration

For each water sample point, data of ocean color scanner MODIS-Aqua were collected, where available. Direct determination of concentration of phytoplankton pigments (chlorophyll “a” and pheophytin “a”) was performed by fluorometric method with seawater samples [25]. After a preliminary rejection, 68 pairs of data of simultaneous measurements of chlorophyll concentration and of the seawater radiance reflectance from MODIS-Aqua (after reprocessing of 2009 and 2010) were selected to develop a regional algorithm. The following regression equation was obtained:

Chl = 2.13 (Rrs(531)/Rrs(547))-2.42 n=68, r2=0.61, (5)

where Rrs(531) and Rrs(547) are values of the above-surface remote-sensing reflectance.

Particle backscattering and TSM

To derive an algorithm for calculation of TSM from satellite data, all data of ship measurements of TSM in surface layer over the period of 2002-2010 were collected. Each point of TSM measurements was considered as a potentially sub-satellite one and data of ocean color scanners were collected for each of them. Data corresponding to good weather conditions were used in further analysis (also quality check of satellite data was carried out). First, the values of the particle backscattering coefficient bbp(550) were computed from MODIS-Aqua data (after reprocessing 2009 and 2010) with the algorithm described in Section 2.2.

As a result, 195 pairs of quasi-concurrent measurements of bbp and TSM were selected. Statistical analysis of these data led to the following regression equation:

TSM=22.8 bbp0.53 , n=195, r2=0.70. (6)

where TSM is measured in mg/l, bbp – in m-1.

Yellow substance absorption

To derive values of the yellow substance absorption coefficient ag , the semi-analytic bio-optical algorithm was used [12]. Due to small values of the water-leaving radiance in the shortwave spectral region in the White Sea and great errors of the atmospheric correction algorithm there, the MODIS spectral bands 412, 443, 469, 488 nm were not used, and the input parameters for solving the inverse problem were values of the above-surface remote-sensing reflectance Rrs(λi) at six MODIS spectral bands 531, 547, 555, 645, 667, and 678 nm. The values of Rrs(λi) were converted into values of the subsurface radiance reflectance ρ(λi) by means of a formula given in [15].

The inverse problem was solved by a least square method, and, as a result of its solving, three unknowns were retrieved: chlorophyll concentration Chl, the particle backscattering coefficient bbp. and the yellow substance absorption coefficient ag.