Snow Depth derived from Sentinel-1 compared to in-situ observations in northern Finland
- Finnish Meteorological Institute, Helsinki, Finland (email@example.com)
Seasonal snow in the northern regions plays an important role providing water resources for both consumption and hydropower generation. Moreover, the snow depth in the northern Finland during winter exceeds 1 m, impacting local agriculture, vegetation, tourism and recreational activities. The objective of this study is to estimate snow depth using an empirical methodology applied to synthetic aperture radar (SAR) images and compare with in situ measurements collected by automatic weather stations (AWS) and snow courses in northern Finland. Snow depth estimates with high spatiotemporal resolution can improve our understand of seasonal snow mass in complex access areas. Here, we use an adapted version of the empirical methodology developed by Lievens et al. (2019) to estimate snow depth using Sentinel-1 constellation (C-band). The algorithm utilizes changes in the cross-polarized backscatter measurements of SAR images repeatedly acquired on the same orbit to avoid geometry distortions. We use SNAP toolbox, combined with the Copernicus digital elevation model (DEM), posted at 30 meters, in the pre-processing stage. The snow retrievals between 2019 and 2022 are compared to three automatic weather stations and four snow courses measurements collected over the same period. The ongoing Sentinel-1 snow depth retrievals during the winter 2021/2022 demonstrate a correlation of 0.76, when compared to in situ measurements, supporting the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Despite the good agreement between the empirical algorithm and the collected datasets on land, further investigation is still necessary to better understand the backscatter response over frozen lake areas. Thanks to the effort of international space agencies, we have available currently, and in a near future, global coverage at high resolution SAR imagery and, combined with installed automatic weather stations, opens the possibility of a wide spatial monitoring of snow variations.
How to cite: Lemos, A. and Riihelä, A.: Snow Depth derived from Sentinel-1 compared to in-situ observations in northern Finland, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-998, https://doi.org/10.5194/egusphere-egu23-998, 2023.