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The Minimum Snow/Ice (MSI) extent is an important climate and environmental indicator related to surface hydrology and freshwater resources. Variations in the MSI extent over the four first-order regions (1-4) included in the Randolph Glacier Inventory (RGI) for Canada and the Northern USA were analyzed in this study using an improved land-water mask to better discriminate landfast ice in the high Arctic coastal zone. The analysis utilized warm season snow/ice probability maps derived from MODIS clear-sky composite imagery at a 250-m spatial resolution. Results showed statistically significant declining trends in the minimum snow/ice extent with the average value of the slope for the entire area (-1,177.7 ± 362.8) km2/yr. The variations in MSI extent are well correlated with surface air temperature. The correlation coefficients are statistically significant, ranging from −0.79 to −0.82. The detailed analysis revealed a reduction of the area with snow/ice probability 100% over the RGI glaciated zones, while areas with lower probability values were increasing in size, which may be interpreted as an indication of general glacier shrinkage over the study area.

Landfast ice (LFI) is a prominent climatological feature in the Canadian Arctic. LFI is generally defined as immobile near-shore ice that remains fast along the coast and forms seaward from the land. It affects the coastline dynamics, is important for the near-shore ecosystems, wildlife, and human socio-economic activities. A method is proposed for mapping the LFI using time series of 10-day clear-sky composites derived at the Canada Center for Remote Sensing (CCRS) from the Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m imagery. The delineation of coastal zone ice utilizes simultaneous analysis of the mean and standard deviation of MODIS monthly reflectance maps. The application of this method is demonstrated for a 20-year period (2000–2019) over the coastal zone of Banks Island in the Beaufort Sea. Detailed analyses have been conducted for three LFI parameters: (1) the total area (spatial extent) occupied by LFI; (2) the distance from the coast to the outer seaward LFI edge, and (3) the water depth at the outer seaward LFI edge. Comparison with the Canadian Ice Service (CIS) data demonstrates good agreement. The average correlation coefficients between CIS and CCRS time series in April-June, when the area reaches a maximum, are equal to 0.87–0.88. The mean differences (CIS-CCRS) are 344 km2 (5,464 km2 vs 5,120 km2) or 6.3% for the spatial extent; 1.3 km (17.6 km vs 16.3 km) or 7.4% for the distance; −2.7 m (−27.4 m vs −24.7 m) or 10% for the water depth. Because the CCRS method uses monthly statistics, it tends to exclude potentially more mobile continuous landfast ice zones than the CIS analysis which is based on data collected on a specific date. The long-term trends of the LFI seasonal cycle in our region of interest since 2000 have shown a tendency for an earlier break-up, later onset, and longer ice-free period; however, these trends are not statistically significant.

The Visible Infrared Imaging Radiometer Suite (VIIRS) represents a new generation of satellite imagers for global operational observations. In many aspects, it is comparable to the Moderate Resolution Imaging Spectroradiometer (MODIS) operated since 2000, i.e. almost for two decades. The Canada Centre for Remote Sensing has developed a unique MODIS processing chain to produce a long-term time series of clear-sky composites and some terrestrial products at 250 m spatial resolution over a 5700 km × 4800 km region centered on Canada. The paper describes an extension of the MODIS time series at the top of the atmosphere level using VIIRS data. The VIIRS clear-sky composites are produced on a 250-m spatial grid for I-bands and a 500-m grid for M-bands. Nominal products are generated as 10-day composites, while snow mask and normalized difference vegetation index are generated as daily products. Preliminary assessment of VIIRS versus MODIS composites has been conducted through comparison of value-added warm season snow/ice probability maps and minimum snow/ice extent. The results demonstrate a high level of consistency (with the average different difference around 0.12%), which indicates that the developed VIIRS processing technology produces results that can potentially be used to extend MODIS time series into the future.

AIM-North is a proposed satellite mission that would provide observations of unprecedented frequency and density for monitoring northern greenhouse gases (GHGs), air quality (AQ) and vegetation. AIM-North would consist of two satellites in a highly elliptical orbit formation, observing over land from ∼40°N to 80°N multiple times per day. Each satellite would carry a near-infrared to shortwave infrared imaging spectrometer for CO2, CH4, and CO, and an ultraviolet-visible imaging spectrometer for air quality. Both instruments would measure solar-induced fluorescence from vegetation. A cloud imager would make near-real-time observations, which could inform the pointing of the other instruments to focus only on the clearest regions. Multiple geostationary (GEO) AQ and GHG satellites are planned for the 2020s, but they will lack coverage of northern regions like the Arctic. AIM-North would address this gap with quasi-geostationary observations of the North and overlap with GEO coverage to facilitate intercomparison and fusion of these datasets. The resulting data would improve our ability to forecast northern air quality and quantify fluxes of GHG and AQ species from forests, permafrost, biomass burning and anthropogenic activity, furthering our scientific understanding of these processes and supporting environmental policy.