Seasonal snow depth dataset over flat terrains in the Northern Hemisphere based on ICESat-2 data from 2018 to 2020

This is the authors' abstract. We don't add key points for this paper.

Snowmelt water is a crucial freshwater resource in mid-to-high-latitude mountain areas. Snow depth is a key parameter in estimating snowmelt water. Current snow depth datasets have low spatial resolution and insufficient observational data for validating snow depth inversion in high-altitude areas. Based on ICESat-2 data, this study constructed a snow depth estimation model considering terrain and photon geometric relationships, obtaining a seasonal snow depth dataset for the Northern Hemisphere from 2018 to 2020 in flat areas (slope ≤ 5°). Results indicate that ICESat-2 is effective for estimating snow depth in areas with gentle slopes, sparse vegetation, deep snowpacks, varying altitudes, and non-forest regions. (1) The ICESat-2 dataset shows 60.5% of biases within ±5 cm, with an average RMSE of 20.2 cm. (2) The best estimation comes from bare ground and herbaceous vegetation, with R² values of estimated and site observed values reaching 0.88 and 0.79, respectively. (3) The dataset tends to overestimate shallow snow and underestimate deep snow. This dataset offers the possibility of snow depth validation in high-elevation, flat, and sparsely vegetated regions.