INTRODUCING THE GLOBAL MAPPING OF FLOOD DYNAMICS USING GNSS-REFLECTOMETRY AND THE CYGNSS MISSION

This study presents a quantitative and synthetic classification of the snowpack in 10 dry-wet status by aggregating separate binary indicators derived from satellite observations. The classification follows the expected evolution of the melt season: night refreezing is frequent at the onset, sustained melting is observed during the summer peak, and remnant liquid water at depth occurs at the end. This dataset improves the knowledge of melt processes using passive microwave remote sensing.

Radar altimeters measure ice-sheet elevation and sea-ice thickness. To improve their accuracy, we developed a model that simulates altimeter waveforms based on the physical properties of snow and sea ice. It computes the power, time, and Doppler shift of radar echoes as waves travel to the surface, interact with snow and ice, and return to the satellite. The model was verified internally, validated against other models and applied in Antarctica using in-situ snow measurements.

Satellite observations of Earth's land surface are important for tracking soil and vegetation water. We use data from the Soil Moisture and Ocean Salinity satellite to build a new product that cleans the raw microwave signal and yields more reliable estimates of soil moisture and vegetation water content. Tests against ground stations and other satellites show that the new record exceeds existing products and can support applications such as drought, freeze–thaw, and carbon monitoring.

The Greenland Ice Sheet has been losing mass since the 1990s, driven by atmospheric and ocean interactions. Analyzing 2002–2023 gravimetric satellite data, this study identifies five principal modes of ice mass variability. Correlations between them, climatic indices, and meteorological parameters such as the temperature and precipitations reveal the presence of interactions at annual to decadal scales. Understanding these drivers is key to predicting future changes and sea-level rise.

The surface water storage (SWS) in the Congo River basin (CB) remains unknown. In this study, the multi-satellite and hypsometric curve approaches are used to estimate SWS in the CB over 1992–2015. The results provide monthly SWS characterized by strong variability with an annual mean amplitude of ~101 ± 23 km³. The evaluation of SWS against independent datasets performed well. This SWS dataset contributes to the better understanding of the Congo basin’s surface hydrology using remote sensing.