Arctic‐Boreal Lake Dynamics Revealed Using CubeSat Imagery

Abstract

Abstract Fine‐scale, subseasonal fluctuations in Arctic‐Boreal surface water reflect regional water balance and modulate trace gas emissions to the atmosphere but have eluded detection using traditional satellite remote sensing. We use high‐resolution (~3–5 m), high‐frequency CubeSat sensors to measure near‐daily changes in lake surface area through an object‐based tracking method that incorporates machine learning to overcome notable limitations of CubeSat imagery. From ~76,000 images we obtain >2.2 million individual observations of changing surface areas for 85,358 lakes in Northern Canada and Alaska between 1 May and 1 October 2017. We find broad‐scale lake area declines across diverse climatic, hydrologic, and physiographic terrains. Localized exceptions reveal lowland flooding and aquatic vegetation phenology cycles. Cumulative small shoreline changes of abundant lakes on the Canadian Shield exceed total inundation variations of better‐studied lowland environments, revealing a surprisingly dynamic landscape with respect to subseasonal variations in surface water extent and trace gas emissions.

Keywords

• CubeSats
• remote sensing
• Arctic‐Boreal lakes
• arctic hydrology
• machine learning