Spatio-Temporal Variability of Suspended Particulate Matter in a High-Arctic Estuary (Adventfjorden, Svalbard) Using Sentinel-2 Time-Series

Abstract

Arctic coasts, which feature land-ocean transport of freshwater, sediments, and other terrestrial material, are impacted by climate change, including increased temperatures, melting glaciers, changes in precipitation and runoff. These trends are assumed to affect productivity in fjordic estuaries. However, the spatial extent and temporal variation of the freshwater-driven darkening of fjords remain unresolved. The present study illustrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden estuary, Svalbard, using in-situ field campaigns and ocean colour remote sensing (OCRS) via high-resolution Sentinel-2 imagery. To compute SPM concentration (CSPMsat), a semi-analytical algorithm was regionally calibrated using local in-situ data, which improved the accuracy of satellite-derived SPM concentration by ~20% (MRD). Analysis of SPM concentration for two consecutive years (2019, 2020) revealed strong seasonality of SPM in Adventfjorden. Highest estimated SPM concentrations and river plume extent (% of fjord with CSPMsat > 30 mg L−1) occurred during June, July, and August. Concurrently, we observed a strong relationship between river plume extent and average air temperature over the 24 h prior to the observation (R2 = 0.69). Considering predicted changes to environmental conditions in the Arctic region, this study highlights the importance of the rapidly changing environmental parameters and the significance of remote sensing in analysing fluxes in light attenuating particles, especially in the coastal Arctic Ocean.

Status of Earth Observation and Remote Sensing Applications in Svalbard

“…These changes are expected to impact productivity in fjordic estuaries. The study by Walch et al [7] demonstrates the spatio-temporal variability of suspended particulate matter (SPM) in the Adventfjorden, Svalbard. The authors used in situ field measurements, taken in 2019 and 2020, along with Sentinel-2 imagery.…”

“…The SI offered broad possibilities to potential authors and encouraged contributions in all areas of Svalbard science, including the following: (1) EO and RS studies pertaining to field studies/campaigns, ESS modelling, and long-term monitoring programs in terrestrial, marine, atmospheric, and cryospheric environments of Svalbard; (2) RS-based studies on cross-sphere interaction of cryosphere with ocean, terrestrial, and atmosphere; (3) The derivation of geophysical and biophysical parameters e.g., chlorophyll concentration, eddies information, snow cover dynamics, vegetation growth, sea ice drift and type, phytoplankton blooms using satellites; (4) Svalbard-wide GI retrieval including geospatial product generation and operationalization using optical (e.g., Sentinel and Landsat series), microwave (e.g., SAR) and Lidar (e.g., ICESat-1/2) applications in Svalbard; (5) Cal/Val activities for various satellite missions, e.g., Pandora (https://www.pandonia-globalnetwork.org/, accessed on 12 January 2023) installation, validation of snow parameters derived using satellite, Cal/Val activities using ocean moorings; (6) Integration of RS, in situ, modelling and previously available GI to advance new knowledge about Svalbard; (7) Artificial intelligence (AI) including deep learning (DL), machine learning (ML), neural networks (NN) and cloud computing-based applications in Arctic areas; (8) RS applications in glaciology including snow cover and its properties, geodetic glacier mass balance, mapping glacier facies, deriving glacier surface elevation changes, etc. ; (9) RS of terrestrial and marine cryosphere including snow/firn/ice, sea ice, snow on sea ice, icebergs, ground ice, avalanche studies, and permafrost subsidence studies; (10) RS of terrestrial vegetation, estimating vegetation growing season and primary productivity, mapping vegetation abundance and extent, and time series analysis of terrestrial vegetation; (11) Applications of very high resolution (VHR) satellite and airborne platforms in Arctic areas, including use of airborne imagery and hyperspectral data acquired by Dornier research aircraft, VHR satellites (e.g., WorldView, Planet) and uncrewed aerial vehicles (UAVs); ( 12) Applications of new technologies, such as robots, autonomous underwater vehicles (AUVs), drone-based mapping using surface from motion (SfM), and terrestrial laser scanners (TLS).…”