Multi-sensor detection of spring breakup phenology of Canada's lakes

• Published in: Remote sensing of environment 2023-09, Vol.295, p.113656, Article 113656
• Main Authors: Giroux-Bougard, Xavier, Fluet-Chouinard, Etienne, Crowley, Morgan A., Cardille, Jeffrey A., Humphries, Murray M.
• Format: Article
• Language: English
• Subjects: Arctic; Change detection; Google Earth Engine; Lake ice phenology; Optical remote sensing; Spring breakup
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2023.113656

The ice phenology of freshwater lakes throughout the Northern Hemisphere has undergone important climate-induced shifts over the past century. In Canada's North, where freshwater lakes and wetlands cover 15 to 40% of the landscape, monitoring ice phenology is vital to understand its impacts on climate, socio-economic, ecological, and hydrological systems. The rapid and dynamic nature of ice phenology events has restricted monitoring efforts to the use of satellite sensors with frequent revisit times (e.g., MODIS, AVHRR), but their low resolution (e.g., > 500 m) limits observations to larger water bodies. However, the increased abundance of high-resolution open-access satellite imagery combined with the rise of cloud-computing technologies has provided opportunities to reduce the trade-off between temporal (i.e., revisit time) and spatial (i.e., pixel size) resolution allowing for lake ice monitoring over broad scales. In this study, we present the Open Pixel-based Earth eNgine Ice (OPEN-ICE) algorithm implemented in Google Earth Engine (GEE), which classifies imagery from multiple open-access optical sensors, then combines them to construct dense annual time series of ice-water observations and estimate pixel spring breakup dates at a 30-m resolution. Using Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI scenes over lakes spanning northern latitudes, we build reference datasets to train decision trees that discriminate between ice, water, and clouds. We combine ice-water classifications from each sensor into annual time series and remove misclassifications with a temporal filter applied using a pixel-wise logistic regression. We then detect the sequence of transition from ice to water in each pixel's time series to estimate the occurrence of breakup each year. We deploy the OPEN-ICE algorithm over all freshwater pixels of Canada for the period of 2013 to 2021. Spring ice phenology events estimated by OPEN-ICE show high accuracy when compared to whole-lake breakup dates measured by the Canadian Ice Service in 105 lakes across 9 years, with mean bias errors of −1.10 and − 0.69 days for breakup start and end, respectively. We apply the OPEN-ICE algorithm to 4000 lakes across Canada and evaluate differences in breakup dates across ecozones and lake sizes. Our new OPEN-ICE tool provides accurate estimates of annual spring breakup events applicable across all boreal and arctic regions to monitor the rapid changes taking place in these vulnerable ecosystems. [D