Data Quality Evaluation of Sentinel-1 and GF-3 SAR for Wind Field Inversion

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.

Uncertainty analysis of 3D potential-field deterministic inversion using mixed Lp norms

The non-uniqueness problem in geophysical inversion, especially potential-field inversion, is widely recognized. It is argued that uncertainty analysis of a recovered model should be as important as finding an optimal model. However, quantifying uncertainty still remains challenging, especially for 3D inversions in both deterministic and Bayesian frameworks. Our objective is to develop an efficient method to empirically quantify the uncertainty of the physical property models recovered from 3D potential-field inversion. We worked in a deterministic framework where an objective function consisting of a data misfit term and a regularization term is minimized. We performed inversions using a mixed Lp-norm formulation where various combinations of L p (0 <= p <= 2) norms can be implemented on different components of the regularization term. Specifically, we randomly sampled the p-norm values in multiple times, and generated a large and diverse sequence of physical property models that all reproduce the observed geophysical data equally well. This suite of models offers practical insights into the uncertainty of the recovered model features. We quantified the uncertainty through calculation of standard deviations and interquartile range, as well as visualizations in box plots and histograms. The numerical results for a realistic synthetic density model created based on a ring-shaped igneous intrusive body quantitatively illustrate uncertainty reduction due to different amounts of prior information imposed on inversions. We also applied the method to a field data set over the Decorah area in the northeastern Iowa. We adopted an acceptance-rejection strategy to generate 31 equivalent models based on which the uncertainties of the inverted models as well as the volume and mass estimates are quantified.