Freeze-Thaw Detection and Monitoring with the Cyclone Global Navigation Satellite System (CYGNSS)

Abstract:

Soil freeze–thaw transition monitoring is essential for quantifying climate change and hydrologic dynamics over cold regions. In this work, we investigate the use of Earth-reflected Global Positioning System (GPS) L-band signals as collected and processed by the Cyclone Global Navigation Satellite System (CYGNSS) [1]. CYGNSS could potentially detect the freezing and thawing of soil surface due to changes of dielectric constant when frozen, at an improved spatio-temporal sampling as compared to traditional microwave radiometry missions.

A feasibility study has been performed over several target areas in the Tibet and in the Andes Mountains. Sensitivity in CYGNSS data to freeze-thaw transitions was demonstrated by comparing reflectivity [2] differences between Summer and Winter to soil and snow temperature as provided by the European Centre for Medium-Range Weather Forecast (ECMWF) ERA 5-Land numerical reanalysis model. Temperature of the soil in layer 1 (0 - 7 cm) of the ECMWF Integrated Forecasting System (IFS) model is considered. The surface is at 0 cm. Soil temperature is set at the middle of each layer, and heat transfer is calculated at the interfaces between them. The ECMWF IFS model represents snow as a single additional layer over the uppermost soil level. Temperature of the snow layer is calculated from the ground to the snow-air interface. Additional parameters are also under evaluation, including volumetric soil moisture in layer 1 and topography roughness as derived from a Digital Elevation Model (DEM).

At present, further investigation is also being performed to develop a new physical model of the reflectivity, and a new retrieval algorithm. The modeling of the surface permittivity will account for fractions of liquid water and ice in frozen soil. Final results will be presented in the conference.

[1] C. Ruf, M. Unwin, J. Dickinson, R. Rose, D. Rose, M. Vincent, and A. Lyons, “CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites],” IEEE Geoscience and Remote Sensing Magazine, vol. 1, no. 2, pp. 52–67, Jul. 2013.

[2] H. Carreno-Luengo, G. Luzi, and M. Crosetto, “Above-Ground Biomass Retrieval over Tropical Forests: a Novel GNSS-R Approach with CyGNSS,” MDPI Remote Sensing Special Issue on Advanced RF Sensors and Remote Sensing Instruments, vol. 12, no. 9, pp. 1368, Apr. 2020.