Constructing a high-precision and high-resolution gravimetric geoid model in the mountainous area is a quite challenging task because of the lack of terrestrial gravity observations, rough topography and the geological complexity. One way out is to use high-quality and well-distributed satellite and airborne gravity data to fill the gravity data gaps; thus, the proper combination of heterogeneous gravity datasets is critical. In a rough topographic area in Colorado, we computed a set of gravimetric geoid models based on different combination modes of satellite gravity models, terrestrial and airborne gravity data using the spectral combination method. The gravimetric geoid model obtained from the combination of satellite gravity model GOCO06S and terrestrial gravity data agrees with the GPS leveling measured geoid heights at 194 benchmarks in 5.8 cm in terms of the standard deviation of discrepancies, and the standard deviation reduces to 5.3 cm after including the GRAV-D airborne gravity data collected at ~ 6.2 km altitude into the data combination. The contributions of airborne gravity data to the signal and accuracy improvements of the geoid models were quantified for different spatial distribution and density of terrestrial gravity data. The results demonstrate that, although the airborne gravity survey was flown at a high altitude, the additions of airborne gravity data improved the accuracies of geoid models by 13.4%–19.8% in the mountainous area (elevations > 2000 m) and 12.7%–21% (elevations < 2000 m) in the moderate area in the cases of terrestrial gravity data spacings are larger than 15 km.

The Surface Water and Ocean Topography (SWOT) satellite delivers an unprecedented spatial resolution, offering new opportunities for advanced marine gravity field modeling. This study investigates the application of SWOT observational data by computing deflections of the vertical (DOVs) using the eight-directional geoid gradient method, followed by gravity field inversion through the inverse Vening–Meinesz (IVM) formula. Experimental results in the South China Sea region demonstrate that SWOT DOVs, based on 19 observation cycles, achieved accuracies of 0.86 arcseconds for the east–west component $\eta$ and 0.77 arcseconds for the north–south component $\xi$. The marine gravity field inversion accuracy reached 4.97 mGal, comparable to the multi-source altimetry-derived model SIO_v32.1. Further analysis reveals that the primary contributions of SWOT DOVs are observed within the 3.5–20 km wavelength band, with cross-track systematic errors identified as the key factor influencing both DOV calculations and gravity anomaly inversion. Additionally, extending the SWOT observation period enhances DOV accuracy, particularly for the $\eta$. These findings highlight the potential of SWOT data in advancing high-resolution marine gravity field modeling.

The redistribution of surface mass (e.g., atmosphere, soil water, oceans, and groundwater) can cause load responses, resulting in vertical deformations of the crust. Indeed, the global navigation satellite system (GNSS)-based continuously operating reference stations (CORS) are able to accurately measure the vertical deformation caused by surface mass loads. In this study, the CORS was used to invert groundwater storage anomalies (GWSA), represented by the equivalent water height (EWH), after removing the effect of the non-groundwater surface mass load (atmospheric, groundwater, and non-tidal oceanic loads) from the vertical deformation monitored by CORS. In addition, the global and regional high-resolution surface mass models were combined to calculate the high-precision load deformation field in in western Yunnan using the remove–restore method, thereby obtaining more accurate surface mass load data and improving the accuracy of the inverted GWSA results. In order to assess the feasibility of the CORS inversion for the GWSA used, 66 CORS stations in western Yunnan Province were considered, presenting weekly GWSA data from 10 January 2018 to 31 December 2020. The results revealed significant seasonal variation in GWSA in the study area, showing an amplitude range of −200–200 mm. This approach is based on the already-established CORS network without requiring additional set-up costs. In addition, the reliability of CORS inverse results was assessed using Gravity Recovery and Climate Experiment (GRACE) inverse results and actual groundwater monitoring data. According to the obtained results, GWSA can be monitored by both CORS and GRACE data; however, CORS provided a more effective spatiotemporal resolution of GWSA. Therefore, the CORS network combined with surface mass load data is able to effectively monitor the spatiotemporal dynamics of GWSA in small-scale areas and provides important references for the study of hydrology.