Unveiling lake ice phenology in Central Asia under climate change with MODIS data and a two-step classification approach

Abstract

Lakes in Central Asia, characterized largely by a semiarid climate and delicate ecosystems, are quite sensitive and vulnerable to climate change but remain less exploited, due mostly to a lack of in-situ measurements. Lake ice phenology is an essential climate variable identified by the Global Climate Observation System. To overcome the overestimation of clouds by the MODIS cloud mask, we developed and distributed a two-step Random Forest algorithm based on MODIS data on the Google Earth Engine. This algorithm first distinguishes cloudy pixels from non-cloudy pixels, and then ice pixels are discriminated from non-cloudy pixels using additional thermal infrared information. We validated the algorithm by implementing it over several boreal lakes with in-situ records in North America and compared our results with existing lake ice phenology datasets. We subsequently retrieved lake ice phenology for 20 large lakes (> 100 km2) in Central Asia from 2001 to 2021 and examined trends in lake ice phenology over this period. On average, lakes in Central Asia underwent later freezing at 0.13 d/yr, earlier thawing at 0.32 d/yr, and shorter ice duration at 0.45 d/yr, with over half of the lakes witnessing significant trends in at least one ice phenology indicator. To evaluate the impact of meteorological factors on lake ice phenology, we built a linear mixed model that predicts ice phenology using meteorological variables from ERA5. Daily mean air temperature dominates ice phenology, with mean air temperature during October to June in the following year explaining approximately 35% of the variance of the duration between freezing and thawing for each lake. Our study offers a comprehensive analysis of lake ice phenology in Central Asia under climate change.