The Gravity Recovery and Climate Experiment (GRACE) gravity mission has become a leading platform for monitoring temporal changes in the Earth’s global gravity field. However, the usability of GRACE data is severely limited by 11 months of missing data between the GRACE and GRACE Follow-on (GRACE-FO) missions. To date, several approaches have been proposed to fill this data gap in the form of spherical harmonic coefficients (an expression of the Earth’s gravity field, SHCs). However, systematic analysis to reveal the characteristics and consistency of the datasets produced by these latest gap-filling techniques is yet to be carried out. Here, three SHC gap-filling products are systematically analyzed and compared: (1) Combining high–low satellite-to-satellite tracking with satellite laser ranging (SLR) observations (QuantumFrontiers, QF), (2) SLR-based recovery incorporating the GRACE empirical orthogonal function decomposition model proposed by the Institute of Geodesy and Geoinformation at the University of Bonn (hereafter, denoted as IGG), and (3) applying the singular spectrum analysis approach (SSA). The results show that (1) the SHCs of the QF, IGG, and SSA data are consistent up to degree 12; (2) the IGG and SSA data give similar results over the 11 gap months, but the IGG shows a faster increase in the mean ocean water mass and the SSA appears to better capture the interannual variation in the terrestrial water storage; and (3) the noise level increases significantly in the high-degree terms (l > 16) of the QF data, so these data are only applicable for large-scale mass migration research. These results provide a reference for users to select a gap-filling product. Finally, we propose a new scheme based on the triple collocation method to derive a weight matrix to fuse these three datasets into a more robust solution.

The catastrophic twin earthquakes that struck southern Turkey and northern Syria on February 6 2023, caused massive casualties and extensive damage to infrastructure, with the province of Hatay bearing the brunt of the impact. To investigate the potential of nighttime light (NTL) data for earthquake disaster assessment, we performed an analysis of pre- and post-earthquake NPP-VIIRS and SDGSAT-1 NTL data. The NTL change ratio (NCR) was employed to identify the extent of damage in Hatay Province following the earthquake to assess the impact on affected buildings and populations. NPP-VIIRS daily NTL Time-series data was used to establish an economic recovery framework for earthquake disasters in Hatay Province, assessing regional economic recovery after earthquake. The results revealed a strong correlation between the total reduced NTLs of NPP-VIIRS and SDGSAT-1 and the presence of severely and moderately damaged buildings. Conversely, the correlation with slightly damaged buildings was relatively weaker. Furthermore, the reduced NTLs of NPP-VIIRS provide a more precise identification of all earthquake damage compared to the SDGSAT-1 data, achieving an overall accuracy of 86.21% in earthquake damage identification and a Kappa coefficient of 0.72. In accordance with the earthquake economic recovery framework, as of the end of March 2024, neither the provincial economy nor any of its districts have fully restored to their pre-earthquake levels. The findings offered new and valuable insights into earthquake disaster assessment, assisting decision-makers in accurately assessing the level of disaster risk following an earthquake and efficiently allocating post-earthquake relief funds.