Secchi depth maps for the Baltic Sea, 2003-2012, with links to GeoTIFs

Secchi depth is a measure of water transparency. In the Baltic Sea region, Secchi depth maps are used to assess eutrophication and as input for habitat models. Due to their spatial and temporal coverage, satellite data would be the most suitable data source for such maps. But the Baltic Sea's optical properties are so different from the open ocean that globally calibrated standard models suffer from large errors. Regional predictive models that take the Baltic Sea's special optical properties into account are thus needed. This paper tests how accurately generalized linear models (GLMs) and generalized additive models (GAMs) with MODIS/Aqua and auxiliary data as inputs can predict Secchi depth at a regional scale. It uses cross-validation to test the prediction accuracy of hundreds of GAMs and GLMs with up to 5 input variables. A GAM with 3 input variables (chlorophyll a, remote sensing reflectance at 678 nm, and long-term mean salinity) made the most accurate predictions. Tested against field observations not used for model selection and calibration, the best model's mean absolute error (MAE) for daily predictions was 1.07 m (22%), more than 50% lower than for other publicly available Baltic Sea Secchi depth maps. The MAE for predicting monthly averages was 0.86 m (15%). Thus, the proposed model selection process was able to find a regional model with good prediction accuracy. It could be useful to find predictive models for environmental variables other than Secchi depth, using data from other satellite sensors, and for other regions where non-standard remote sensing models are needed for prediction and mapping. Annual and monthly mean Secchi depth maps for 2003-2012 come with this paper as Supplementary materials.

Sediment organic matter, grain size, and results of prediction models from northern North Atlantic and Arctic Seas

Sediment samples and hydrographic conditions were studied at 28 stations around Iceland. At these sites, Conductivity-Temperature-Depth (CTD) casts were conducted to collect hydrographic data and multicorer casts were conductd to collect data on sediment characteristics including grain size distribution, carbon and nitrogen concentration, and chloroplastic pigment concentration. A total of 14 environmental predictors were used to model sediment characteristics around Iceland on regional geographic space. For these, two approaches were used: Multivariate Adaptation Regression Splines (MARS) and randomForest regression models. RandomForest outperformed MARS in predicting grain size distribution. MARS models had a greater tendency to over- and underpredict sediment values in areas outside the environmental envelope defined by the training dataset. We provide first GIS layers on sediment characteristics around Iceland, that can be used as predictors in future models. Although models performed well, more samples, especially from the shelf areas, will be needed to improve the models in future.

Whale sightings, group sizes and krill biomass in West Greenland in 2005

This study combined data on fin whale Balaenoptera physalus, humpback whale Megaptera novaeangliae, minke whale B. acutorostrata, and sei whale B. borealis sightings from large-scale visual aerial and ship-based surveys (248 and 157 sightings, respectively) with synoptic acoustic sampling of krill Meganyctiphanes norvegica and Thysanoessa sp. abundance in September 2005 in West Greenland to examine the relationships between whales and their prey. Krill densities were obtained by converting relationships of volume backscattering strengths at multiple frequencies to a numerical density using an estimate of krill target strength. Krill data were vertically integrated in 25 m depth bins between 0 and 300 m to obtain water column biomass (g/m**2) and translated to density surfaces using ordinary kriging. Standard regression models (Generalized Additive Modeling, GAM, and Generalized Linear Modeling, GLM) were developed to identify important explanatory variables relating the presence, absence, and density of large whales to the physical and biological environment and different survey platforms. Large baleen whales were concentrated in 3 focal areas: (1) the northern edge of Lille Hellefiske bank between 65 and 67°N, (2) north of Paamiut at 63°N, and (3) in South Greenland between 60 and 61° N. There was a bimodal pattern of mean krill density between depths, with one peak between 50 and 75 m (mean 0.75 g/m**2, SD 2.74) and another between 225 and 275 m (mean 1.2 to 1.3 g/m**2, SD 23 to 19). Water column krill biomass was 3 times higher in South Greenland than at any other site along the coast. Total depth-integrated krill biomass was 1.3 x 10**9 (CV 0.11). Models indicated the most important parameter in predicting large baleen whale presence was integrated krill abundance, although this relationship was only significant for sightings obtained on the ship survey. This suggests that a high degree of spatio-temporal synchrony in observations is necessary for quantifying predator-prey relationships. Krill biomass was most predictive of whale presence at depths >150 m, suggesting a threshold depth below which it is energetically optimal for baleen whales to forage on krill in West Greenland.

Downwelling spectral irradiance as measured in different depths, remote sensing reflectance and phytoplankton pigment concentration during POLARSTERN cruises ANT-XXIV/1, ANT-XXIV/4, ANT-XXVI/4 and Maria S. Merian cruise MSM18/3

The composition and abundance of algal pigments provide information on phytoplankton community characteristics such as photoacclimation, overall biomass and taxonomic composition. In particular, pigments play a major role in photoprotection and in the light-driven part of photosynthesis. Most phytoplankton pigments can be measured by high-performance liquid chromatography (HPLC) techniques applied to filtered water samples. This method, as well as other laboratory analyses, is time consuming and therefore limits the number of samples that can be processed in a given time. In order to receive information on phytoplankton pigment composition with a higher temporal and spatial resolution, we have developed a method to assess pigment concentrations from continuous optical measurements. The method applies an empirical orthogonal function (EOF) analysis to remote-sensing reflectance data derived from ship-based hyperspectral underwater radiometry and from multispectral satellite data (using the Medium Resolution Imaging Spectrometer - MERIS - Polymer product developed by Steinmetz et al., 2011, doi:10.1364/OE.19.009783) measured in the Atlantic Ocean. Subsequently we developed multiple linear regression models with measured (collocated) pigment concentrations as the response variable and EOF loadings as predictor variables. The model results show that surface concentrations of a suite of pigments and pigment groups can be well predicted from the ship-based reflectance measurements, even when only a multispectral resolution is chosen (i.e., eight bands, similar to those used by MERIS). Based on the MERIS reflectance data, concentrations of total and monovinyl chlorophyll a and the groups of photoprotective and photosynthetic carotenoids can be predicted with high quality. As a demonstration of the utility of the approach, the fitted model based on satellite reflectance data as input was applied to 1 month of MERIS Polymer data to predict the concentration of those pigment groups for the whole eastern tropical Atlantic area. Bootstrapping explorations of cross-validation error indicate that the method can produce reliable predictions with relatively small data sets (e.g., < 50 collocated values of reflectance and pigment concentration). The method allows for the derivation of time series from continuous reflectance data of various pigment groups at various regions, which can be used to study variability and change of phytoplankton composition and photophysiology.