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The Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut (74°55′N, 109°34′W) was established in 2003 to examine Arctic ecosystem processes that would be impacted by climate warming and permafrost degradation. This paper provides a synthesis of how remote sensing has contributed to biogeophysical modelling and monitoring at the CBAWO from 2003 to 2023. Given the location and isolated nature of the CBAWO in the Canadian High Arctic, remote sensing data and derivatives have been instrumental for studies examining ecosystem structure and function at local and landscape scales. In combination with field measurements, remote sensing data facilitated mapping and modelling of vegetation types, % vegetation cover and aboveground phytomass, soil moisture, carbon exchange rates, and permafrost degradation and disturbance. It has been demonstrated that even in an environment with limited vegetation cover and phytomass, spectral vegetation indices (e.g., the normalized difference vegetation index) are able to model various biogeophysical variables. These applications are feasible for research sites such as the CBAWO using high spatial resolution remote sensing data across the visible, infrared, and microwave regions of the electromagnetic spectrum. Furthermore, as the satellite record continues to expand, we will gain a greater understanding of the impacts arising from the expected continued warming at northern latitudes. Although the logistics for research in the Arctic remain challenging, today's technologies (e.g., high spatial resolution satellite remote sensing, automated in situ sensors and data loggers, and wireless communication systems) can support a host of scientific endeavours in the Arctic (and other remote sites) through modelling and monitoring of biogeophysical variables and Earth surface processes with limited but critical field campaigns. The research synthesized here for the CBAWO highlights the essential role of remote sensing of terrestrial ecosystems in the Canadian Arctic.

Increased soil nutrient availability, and associated increases in vegetation productivity, could create a negative feedback between Arctic ecosystems and the climate system, thereby reducing the contribution of Arctic ecosystems to future climate change. To predict whether this feedback will develop, it is important to understand the environmental controls over nutrient cycling in High Arctic ecosystems and their impact on carbon cycling processes. Here, we examined the environmental controls over soil nitrogen availability in a High Arctic wet sedge meadow and how abiotic factors and soil nitrogen influenced carbon dioxide exchange processes. The importance of environmental variables was consistent over the 3 years, but the magnitudes of their effect varied depending on climate conditions. Ammonium availability was higher in warmer years and wetter conditions, while drier areas within the wetland had higher nitrate availability. Carbon uptake was driven by soil moisture, active layer depth, and variability between sampling sites and years (R2 = 0.753), while ecosystem respiration was influenced by nitrogen availability, soil temperature, active layer depth, and sampling year (R2 = 0.848). Considered together, the future carbon dioxide source or sink potential of high latitude wetlands will largely depend on climate-induced changes in moisture and subsequent impacts on nutrient availability.

Thematic maps developed from remote sensing data are extremely useful for designing intensive field studies, particularly for large areas that are logistically challenging to access. The integrated watershed studies at the Cape Bounty Arctic Watershed Observatory (CBAWO), Melville Island, Nunavut, rely heavily on land cover for establishing sampling locations regardless of the type of research being conducted (e.g., permafrost degradation, greenhouse gas exchange, surface water chemistry, etc.). Here, we present an environmental land-cover classification of the CBAWO that was developed through an iterative process employing parametric and non-parametric classification algorithms applied to WorldView-2 satellite data and topographic variables. The support vector machine classification of eight-band WorldView-2 spectral data and a topographic wetness index produced the highest classification accuracy for eight land-cover classes (overall classification accuracy: 90.7%; Kappa coefficient (κ): 0.89). This analysis also provided a more precise classification scheme, particularly in the context of the relationship between vegetation type and moisture regime. The environmental land-cover classification derived will better inform future integrated studies of the watershed and allow for upscaling of site-level characteristics to the watershed-scale using the updated vegetation classes.

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