The Glacial Meltwater Turbidity Algorithm (GaMTA): Adaptation of single-band algorithm retrieving turbidity to satellite and UAV dataset from highly glaciated Antarctic region

The growing need for marine polar observations, especially using remote sensing data, necessitates the adaptation of existing algorithms to local conditions. Five satellite imageries acquired by MSI and OLI sensors from 2020 and 2021 depicting Admiralty Bay in King George Island and sixteen multispectral maps created using data from Unoccupied Aerial Vehicles (UAV) flights with a spectral camera over the cove near Zalewski Glacier were analysed. Data were supplemented by field measurements of turbidity. The Glacial Meltwater Turbidity Algorithm (GaMTA) was proposed and compared with well-known semianalytical, single-band algorithms for turbidity retrieval based on spectral imageries of heavily glaciated areas. Comparative analysis showed the ineffectiveness of algorithms applied to medium -resolution satellite images in the area of small glacial coves with high and spatially varying surface water turbidity (R = 0.59-0.61, RMSE = 4.45-7.46), in contrast to open water with low turbidity (R = 0.79-0.88, RMSE = 2.61-3.08). The results from the use of high-resolution UAV data showed the applicability of this algorithm to aerial images (R = 0.96-0.97, RMSE = 5.75-7.57). The use of GaMTA, which is adapted to local conditions, produces better results than the other algorithms, but validation on a larger dataset is needed.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

The study examines the adaptation of existing algorithms to local conditions for marine polar observations using remote sensing data. Five satellite images and sixteen multispectral maps were analyzed to compare the Glacial Meltwater Turbidity Algorithm (GaMTA) with other algorithms for turbidity retrieval in heavily glaciated and open water areas. The results show that GaMTA performs better in small glacial coves with high and spatially varying surface water turbidity, while also showing applicability to aerial images.