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

Abstract

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.

UAVs for Science in Antarctica

“…The identification of glacial outflow zones and meltwater turbidity, as to determine their impact on the ecosystem, was obtained through UAV surveys with a multispectral camera in four glaciers in King George Island by Wójcik et al (2019) [90], which allowed identifying local features (glacial discharge, plume shapes and colours) practically unnoticed in satellite datasets. Wójcik-Długoborska and Bialik (2021) [91], went further in the same topic of research and analysed in much detail the effect of shadows caused by glaciers on the spectral characteristics of meltwater measured in UAV and satellite multispectral images. The monitoring of glacier fronts is performed by Marusazh et al (2019Marusazh et al ( , 2020 [92,93] in two of the Argentine Islands (Galindez and Winter).…”

Fixed-Wing UAV Flight Operation under Harsh Weather Conditions: A Case Study in Livingston Island Glaciers, Antarctica

“…We wish to emphasize that our study does not aim to be a comparison of fixed-wing versus multirotor systems. The latter have also been shown to provide excellent results in the Antarctic environment (e.g., [15]). Neither do we pretend that our study pioneers the use of fixed-wing RPAS in environments such as the South Shetland Islands, as there are some notable examples (e.g., [30]).…”

“…The bibliography available by that time on previous studies carried out using drones in Antarctica, mostly referred to the development of sensors [7,8] and their platforms [9,10], vegetation [11], fauna [12], and meteorological variables measurement [13,14], among others. They were seldom focused on the geometric modeling of the glacier surface, though some notable applications existed [15].…”

The Shadow Effect on Surface Biophysical Variables Derived from Remote Sensing: A Review

“…Therefore, shadow can affect the interpretation of phenomena on Earth. Moreover, shadow can have major effects on satellite-derived surface biophysical characteristics [22]. Simpson, et al [23] outlined that shadows can produce low and negative values in the normalized difference vegetation index (NDVI) especially in dense vegetation regions.…”