“…The widespread empirical spectral filtering methods, such as Gaussian-type filter [15], [16], [17], [18] Decorrelation-type filter [19], [20], [21], Decomposition-type filter [22], [23], [13] and Root Mean Square (RMS) filter [24], are susceptible to under-filtering or over-filtering due to neglecting the precision information of GRACE gravity field models [25], [26]. In contrast, more rigorous statistical filtering methods, which incorporate signal and error covariance matrices of GRACE SHCs, such as Wiener filter [27], DDK filter [28], [29] and ANS filter [30], demonstrate significantly enhanced filtering effects. Apart from global spectral filtering, the regional pointmass modeling technique is developed to enhance the spatial resolution of regional mass changes [31], [32], [33], [34], utilizing the pseudo measurements (e.g., gravity disturbances, geoid heights, and gravity anomalies) at satellite altitudes computed from GRACE SHCs to estimate the spatial pointmass variations on the Earth's surface within a regularization framework.…”