Frozen ground affects ecosystems, infrastructure, and farming, yet detecting it worldwide remains a challenge. To improve soil freeze/thaw detection from space, we adapted a laboratory tool called the soil freezing curve for use with NASA’s Soil Moisture Active Passive (SMAP) satellite and temperature records. This new “surface freezing curve” method can identify thresholds tied to moisture phase changes, improving the accuracy and stability of freeze/thaw monitoring in agricultural regions.

We used L-band observations from the Soil Moisture Active Passive (SMAP) mission to quantify the surface and subsurface liquid water amounts (LWAs) in the percolation zone of the Greenland ice sheet. The algorithm is described, and the validation results are provided. The results demonstrate the potential for creating an LWA data product across the Greenland ice sheet (GrIS), which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.

Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.

We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperature time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is extremely promising for monitoring ground temperature below the snowpack and studying the spatio-temporal variability thanks to 15 years of observations over the whole Arctic area.

Microwave L-band radiometry offers a promising tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.