Real-time Detection of Daily Surface Snowmelt Based on FY-3 MWRI and Enhanced-resolution SSMIS Data Over the Antarctic Ice Sheet

Surface snowmelt on the Antarctic Ice Sheet (AIS) plays a crucial role in Earth’s climate system, influencing surface hydrology, ice shelf stability, surface mass and energy balance. Liquid water emerging in surface snowpacks during melt seasons increases the emissivity of microwaves, enabling the detection of snowmelt information by identifying the sudden increase in passive microwave brightness temperature (T_b). The detecting of polar ice sheet surface snowmelt state at superior timeliness and finer scale is rapidly progressing through the augmenting real-time capability and resolution of passive microwave radiometry. However, existing algorithms often rely on complete melt seasons of observation T_b data, which limits their applicability for real-time detection and typically suffer from low spatial resolution. Here, we develop a real-time detection algorithm and a corresponding system for surface snowmelt detection on the Antarctic Ice Sheet, utilizing T_b data at multiple spatial resolutions.

The three remotely sensed variables we used include diurnal amplitude variation at 37 GHz vertical polarization (i.e., DAV₃₇), the difference between the T_b at 37 GHz vertical polarization and the winter reference (i.e., ΔTB_37V), and the normalized polarization ratio at 37 GHz (NPR₃₇). In-situ observations from the AIS automatic weather stations (AWSs) are provided by the Institute for Marine and Atmospheric research of Utrecht (IMAU). The energy available for surface snowmelt was calculated using the surface energy balance (SEB) model, which has demonstrated reliability and robustness in providing consistent snowmelt flux estimates. In this study, we primarily used three remotely sensed variables and in-situ snowmelt flux as inputs for the parameterization of the Fisher Discriminant Analysis (FDA) equation D_i=ω₀+ω₁x_i,1+ω₂x_i,2. In this equation, D represents the discriminant score, and if D is above 0°C, a specific pixel is classified as a melting state, whereas if D is below 0°C, the pixel is classified as frozen. The validation of snowmelt results was conducted using snowmelt flux data from AWSs, yielding a promising overall accuracy of 96% and an F₁-score of 0.74.

The algorithm is suitable for real-time snowmelt detection, and the corresponding detection system enables high timeliness (within 24 hours) in acquiring surface snowmelt state, melting area and melting days on the Antarctic Ice Sheet. The FY-3 MWRI provides real-time T_b data stably, whereas it is limited by its low spatial resolution (25 km). The missing time series satellite observations from the SSMIS sensor are substantial, leading to random errors. However, the enhanced-resolution SSMIS dataset can provide higher spatial resolution T_b measurements (3.125 km). Here we perform the linear regression and time-line interpolation method to establish a relationship between T_b data from various spatial resolutions, and further combine measurements from the FY-3 MWRI and SSMIS sensors to enhance the spatial resolution of the system.