High-Resolution Mapping of Glacial Lakes in Central Asia Using PlanetScope Imagery and Google Earth Engine: A New Algorithm and Comprehensive Inventory

The formation and expansion of glacial lakes, driven by ongoing climate warming, is a potentially hazardous phenomenon with significant impacts on glacier melt and regional hydrology. In regions like Central Asia, decades of glacier retreat and thinning have fostered the development and growth of various types of glacial lakes, including ice-marginal, moraine-dammed, and supraglacial lakes. These water bodies store meltwater and accelerate glacier mass loss and terminus retreat through heat exchange and intensified melt, ice calving, and ice-marginal destabilization. Once formed, glacial lakes can trigger positive feedback mechanisms that decouple their evolution from direct climate forcing, resulting in rapid glacier downwasting and retreat.

Despite advances in remote sensing, accurately capturing the full size and spatial distribution of these lakes remains challenging. Current inventories, largely based on moderate-resolution imagery (Landsat, Sentinel-2), often overlook smaller lakes. These smaller lakes are a critical yet underappreciated component of the cryosphere and can expand rapidly, posing risks of Glacier Lake Outburst Floods (GLOFs).

In this study, we present a comprehensive, high-resolution glacial lake inventory for Central Asia, derived from Planet’s PlanetScope imagery. With a spatial resolution of approximately 3 m/pixel—an order of magnitude finer than Landsat and Sentinel-2 —PlanetScope data enables the delineation of lakes as small as tens of meters in size, overcoming the spatial limitations of previous satellite-based inventories. The study focuses on Central Asia, covering regions including Dzhungarsky Alatau, Tien Shan, Pamir-Alay, and Pamir.

Our approach consists of four main steps: (i) Water Pixel Identification: Water pixels are detected from PlanetScope images using the Normalized Difference Water Index (NDWI) and Coloured Dissolved Organic Matter (CDOM), with thresholds determined by Otsu’s algorithm. (ii) Lake Clustering: Detected water pixels are grouped into clusters, with glacial lakes defined as clusters exceeding 22 connected pixels (~200 m²). (iii) Boundary Refinement: Lake boundaries are further refined using an NDWI- and CDOM--based Otsu method. (iv) Lake Inventory Compilation: A detailed inventory is produced, including geographical coordinates, elevation, area, and NDWI statistics for each identified lake. The algorithm is implemented within the Google Earth Engine (GEE) environment, enhancing computational efficiency and minimizing the need for local data storage.

The new Central Asia Glacial Lake Inventory (CAGLI) comprises approximately 14,000 water bodies, each with an area exceeding 200 m² with a combined area of 470 km² as of August-September 2023. This dataset allows for a detailed characterization of lake size distribution in the region, efficient monitoring of lake formation and growth, and analysis of the impact of surface ponds on glacier evolution. It also enhances the delineation of debris-covered glaciers, where water pond formation is more likely than on non-glacierized terrain. Importantly, the new algorithm provides practitioners in Central Asia and other glacierized mountain regions with a highly efficient tool for monitoring lake changes, supporting early warning systems and risk reduction strategies.