The determination of physical heights is of key importance for a wide spectrum of geoscientific applications and, in particular, for engineering projects. The main scope of the present work is focused on the determination of a high-accuracy and high-resolution gravimetric and hybrid geoid model, to determine orthometric heights without the need of conventional leveling. Both historical and newly acquired gravity data have been collected during dedicated gravity campaigns, around the location of a dedicated GNSS network as well as areas where the existing land gravity database presented voids. Geoid determination was based on the classical remove–compute–restore (RCR) technique and spectral and stochastic approaches. The low frequencies have been modeled based on the XGM2019e global geopotential model (GGM) and the topographic effects have been evaluated with the residual terrain model (RTM) reduction. The evaluation of the final geoid model was performed over 462 GNSS/leveling benchmarks (BMs), where the newly determined gravimetric geoid has shown an improvement of 3.1 cm, in the std of the differences to the GNSS/leveling BMs, compared to the latest national geoid model. A deterministic and stochastic fit to the GNSS/leveling data has been performed, investigating various choices for the parametric models and analytical covariance functions. The scope was to determine a hybrid geoid model, tailored to the area and GNSS/leveling data, which will be the one used for the direct estimation of high-accuracy orthometric heights from GNSS observations. After the deterministic fit, a std to the GNSS/leveling data of 10.1 cm has been achieved, with 54.8% and 83.1% of the absolute height differences being below the 1 cm and 2 cm per square root km of baseline length. The final hybrid geoid model, i.e., after the stochastic treatment of the adjusted residuals, gave a std of the difference to the GNSS/leveling data of 1.1 cm, with 99.8% and 99.9% of the height difference being smaller than the 1 cm and 2 cm standard errors, thus achieving a 1 cm accuracy regional geoid.

Extreme weather events have increasingly resulted in significant disasters, impacting human life and economic development. Recently, short-term floods have caused substantial damage worldwide. Such was the case of the catastrophic flood that struck the region of Thessaly, Greece, in September 2023. In this study, we investigate the application of Earth Observation satellite data – optical, SAR, and gravimetric – for mapping flood events, focusing on the catastrophic flood that occurred in Thessaly. The rationale for incorporating both EO imagery and gravimetric observations lies in their complementary strengths: while optical and SAR sensors provide detailed, high-resolution snapshots of surface hydrological changes, gravimetry captures changes in the total water mass, enabling an assessment of broader hydrological conditions that may not be visible in conventional imagery. Our analysis was divided into three main components. Firstly, we utilized post-event satellite images from the Sentinel-2 platform, filtered by area of interest (AOI) and date range, with a constraint of less than 20% cloud cover. The Normalized Difference Water Index (NDWI) was applied to identify water bodies in optical imagery, serving as a reference for subsequent flood mapping methodologies. Secondly, we employed Sentinel-1 Synthetic Aperture Radar (SAR) data from pre- and post-event acquisitions. SAR images underwent edge masking and were merged to generate two mosaics. Flooded areas were detected by identifying regions with low backscatter values, specifically where the vertical vertical polarisation (VV) band exhibited values below a threshold of −17 in post-event mosaics relative to pre-event counterparts. Visual inspection confirmed the accuracy of flood detection against Sentinel-2 imagery. Lastly, we integrated GRACE and GRACE-FO Level-2 spherical harmonic coefficients provided by the Jet Propulsion Laboratory (JPL) to derive Liquid Equivalent Water Thickness (EWT), representing water thickness per grid cell. Despite the spatial resolution limitation of monthly GRACE products, EWT effectively captured spatial characteristics during short-term flood events. Simulating water level rise involved calculating flow direction, accumulation, and depressions using SRTM DEM data. The water spread simulation incorporated EWT values, modeling water movement and accumulation across terrain. This approach considered filled depressions and flow dynamics, providing a realistic representation of flood scenarios by accounting for terrain elevation and water volume. Modeled flooded areas closely resembled the spatial extent of the actual event, although volumetric accuracy was constrained by the spatial resolution of GRACE mission data.