In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.

We analyze changes in the maximum annual transient snowline elevation (M_ATSL) of glaciers for two regions in western North America from 1984 to 2024 using five satellite remote sensing datasets. M_ATSL reached its highest elevations in 2019 (155 m above the long-term average) for Alaska (Region 1) and in 2023 (148 m) for Western Canada and USA (Region 2). The rate of M_ATSL rise accelerated fourfold, increasing from 2.1 ± 0.8 m a⁻¹ in 1984–2010 (r ² = 0.1, p < 0.01) to 8.9 ± 1.7 m a⁻¹ in 2010–2024 (r ² = 0.5, p < 0.01). In 2019, 91 glaciers exceeded the 95th percentile M_ATSL elevation, a threshold indicative of complete loss of the accumulation area, in Region 1. In Region 2, 149 glaciers exceeded this threshold in 2023. Year-to-year variability in M_ATSL was strongly influenced by mean summer air temperature with sensitivities of +46 m °C⁻¹ (r ² = 0.54) and +23 m °C⁻¹ (r ² = 0.30) for Regions 1 and 2, respectively. Mean spring snow water equivalent also played an important role with sensitivities of −351 mm w.e.⁻¹ (r ² = 0.48) and −155 mm w.e.⁻¹ (r ² = 0.37), respectively. Per-glacier analysis revealed that south-facing slopes experienced the largest M_ATSL increases. Terrain attributes, including slope, aspect, hypsometry, and elevation, enhanced M_ATSL prediction models compared to those using only climate variables. The pronounced rise in M_ATSL underscores a critical glacier melt feedback mechanism, warranting further investigation. This study highlights the utility of automated M_ATSL time-series mapping for regional-scale analyses and identifies key limitations and opportunities for future research.

Watershed-scale landscape analysis includes many disciplines, including ecological, hydrological, and geographical sciences. The recent proliferation of free optical satellite imagery (FOSI) has changed the possibilities for the monitoring of environmental change at local and global scales. Many reviews exist for discipline-specific remote sensing applications; however, this article seeks to highlight the rapidly growing archive of FOSI and applied tools that can be used by all levels of users. Herein, ten techniques and eight applications of FOSI are reviewed, along with the specifications and limitations of various sources of FOSI. Although this review focuses on Western Canada, the democratization of FOSI is globally relevant, and the objective is to explain basic concepts via figures and reference materials to help summarize this rapidly changing field.

We use the twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product to study the regional influence of the Oceanic Niño Index (ONI) and Pacific Decadal Oscillation (PDO) on snow cover in British Columbia (BC). We apply a locally weighted regression (LOWESS) interpolation to the MODIS normalized difference snow index (NDSI) time series to detect the timing and duration of snow. We confirm the general consensus from many previous in situ studies that both ONI and PDO have significant impacts on snow cover in BC. We add to this knowledge by performing seasonal and regional analysis using established hydrozones and explore variation in our results by elevation bins of 500 m. We calibrated our method with in situ snow water equivalent (SWE) data and found an optimal NDSI threshold of 30 for detecting continuous snow cover. We separate automatic snow weather station data into calibration (75 %) and validation (25 %) subsets and obtain mean absolute errors between the MODIS and in situ observations for the start, end, and duration of 8.7, 8.9, and 13.1 d for the calibration data and 12.7, 12.6, and 16.6 d for the validation data, respectively. In general, the start date of snow is poorly correlated with both ONI and PDO, whereas the end date and duration are strongly negatively correlated. Regional patterns emerge wherein northern and southern BC are most correlated with the PDO and the ONI, respectively. These relationships are generally stronger at lower elevations and vary spatially. This study demonstrates that the suitability of ocean–atmosphere teleconnections as predictors of the timing and duration of snow varies throughout BC.