The optical and biological properties of glacial meltwater in an Antarctic fjord

Temporal variability in the relationship between line height absorption and chlorophyll concentration: a case study from the Northern Gulf of Alaska

The Line Height Absorption (LHA) method uses absorption of light to estimate chlorophyll-a. While most users consider regional variability and apply corrections, the effect of temporal variability is typically not explored. The Northern Gulf of Alaska (NGA) was selected for this study because there was no published regional value and its large swings in temporal productivity would make it a good candidate to evaluate the effect of temporal variability on the relationship. The mean NGA value of 0.0114 obtained here should be treated with caution, as variation in the slope of the relationship (aLH*), and thus chlorophyll-a estimates, in the NGA region varied by ∼25% between spring (aLH* = 0.0109) and summer (aLH* = 0.0137). Results suggest that this change is driven by a shift in pigment packaging and cell size associated with changes in mixed layer depth and stratification. Consideration of how temporal variability may affect the accuracy of the LHA method in other regions is thus recommended.

Phytoplankton growth rates in the Amundsen Sea (Antarctica) during summer: The role of light

In the Amundsen Sea, significant global warming accelerates ice melt, and is consequently altering many ocean properties such as sea ice concentration, surface freshening, water column stratification, and underwater light properties. To examine the influence of light, which is one of the fundamental factors for phytoplankton growth, incubation experiments and field surveys were performed during the austral summer of 2016. In the incubation experiments, phytoplankton abundance and carbon biomass significantly increased with increasing light levels, probably indicating light limitation. Growth rates of the small pennates (mean 0.42 d^-1) increased most rapidly with an increase in light, followed by those of Phaeocystis antarctica (0.31 d^-1), and the large diatoms (0.16 d^-1). A short-term study during the field survey showed that phytoplankton distribution in the surface layer was likely controlled by different responses to light and the sinking rate of each species. These results suggest that the approach adopted by previous studies of explaining phytoplankton ecology as a characteristic of two major taxa, namely diatoms and P. antarctica, in the coastal Antarctic waters might cause errors owing to oversimplification and misunderstanding, since diatoms comprise several species that have different ecophysiological characteristics.

The Influence of Shadow Effects on the Spectral Characteristics of Glacial Meltwater

The phenomenon of shadows due to glaciers is investigated in Antarctica. The observed shadow effect disrupts analyses conducted by remote sensing and is a challenge in the assessment of sediment meltwater plumes in polar marine environments. A DJI Inspire 2 drone equipped with a Zenmuse x5s camera was used to generate a digital surface model (DSM) of 6 King George Island glaciers: Ecology, Dera, Zalewski, Ladies, Krak, and Vieville. On this basis, shaded areas of coves near glaciers were traced. For the first time, spectral characteristics of shaded meltwater were observed with the simultaneous use of a Sequoia+ spectral camera mounted on a Parrot Bluegrass drone and in Landsat 8 satellite images. In total, 44 drone flights were made, and 399 satellite images were analyzed. Among them, four drone spectral images and four satellite images were selected, meeting the condition of a visible shadow. For homogeneous waters (deep, low turbidity, without ice phenomena), the spectral properties tend to change during the approach to an obstacle casting a shadow especially during low shortwave downward radiation. In this case, in the shade, the amount of radiation reflected in the green spectral band decreases by 50% far from the obstacle and by 43% near the obstacle, while in near infrared (NIR), it decreases by 42% and 21%, respectively. With highly turbid, shallow water and ice phenomena, this tendency does not occur. It was found that the green spectral band had the highest contrast in the amount of reflected radiation between nonshaded and shaded areas, but due to its high sensitivity, the analysis could have been overestimated. The spectral properties of shaded meltwater differ depending on the distance from the glacier front, which is related to the saturation of the water with sediment particles. We discovered that the pixel aggregation of uniform areas caused the loss of detailed information, while pixel aggregation of nonuniform, shallow areas with ice phenomena caused changes and the loss of original information. During the aggregation of the original pixel resolution (15 cm) up to 30 m, the smallest error occurred in the area with a homogeneous water surface, while the greatest error (over 100%) was identified in the places where the water was strongly cloudy or there were ice phenomena.

Investigation of Sediment-Rich Glacial Meltwater Plumes Using a High-Resolution Multispectral Sensor Mounted on an Unmanned Aerial Vehicle

A Parrot Sequoia+ multispectral camera on a Parrot Bluegrass drone registered in four spectral bands (green, red, red edge (RE), and near-infrared (NIR)) to identify glacial outflow zones and determined the meltwater turbidity values in waters in front of the following Antarctic glaciers: Ecology, Dera Icefall, Zalewski, and Krak on King George Island, Southern Shetlands was used. This process was supported by a Red-Green-Blue (RGB) colour model from a Zenmuse X5 camera on an Inspire 2 quadcopter drone. Additional surface water turbidity measurements were carried out using a Yellow Springs Instruments (YSI) sonde EXO2. From this research, it was apparent that for mapping low-turbidity and medium-turbidity waters (<70 formazinenephelometricunits (FNU)), a red spectral band should be used, since it is insensitive to possible surface ice phenomena and registers the presence of both red and white sediments. High-turbidity plumes with elevated FNU values should be identified through the NIR band. Strong correlation coefficients between the reflectance at particular bands and FNU readings (RGreen = 0.85, RRed = 0.85, REdge = 0.84, and RNIR = 0.83) are shown that multispectral mapping using Unmanned Aerial Vehicles (UAVs) can be successfully usedeven in the unfavourable weather conditions and harsh climate of Antarctica. Lastly, the movement of water masses in Admiralty Bay is briefly discussed and supported by the results from EXO2 measurements.