Fri Apr 10, F. Flechtner
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Shen, Zhanglin, Chen, Qiujie, Shen, Yunzhong, and Zhang, Xingfu, 2026. Impacts of FES2022 and AOD1B RL07 Background Models on KBR– and LRI–Based GRACE–FO Monthly Gravity Field Estimations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19:4487–4500, doi:10.1109/JSTARS.2026.3651751.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2026IJSTA..19.4487S,
author = {{Shen}, Zhanglin and {Chen}, Qiujie and {Shen}, Yunzhong and {Zhang}, Xingfu},
title = "{Impacts of FES2022 and AOD1B RL07 Background Models on KBR- and LRI-Based GRACE-FO Monthly Gravity Field Estimations}",
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
keywords = {GRACE-FO, gravity field estimation, KBR and LRI, nontidal atmosphere and ocean dealiasing, ocean tide, Satellite gravimetry},
year = 2026,
month = jan,
volume = {19},
pages = {4487-4500},
abstract = "{Temporal gravity field solutions from the Gravity Recovery and Climate
Experiment Follow-on (GRACE-FO) mission are inherently
constrained by aliasing effects stemming from imperfect
background models. Recent advancements in ocean tide modeling
(e.g., FES2022) and nontidal dealiasing products (e.g.,
Atmosphere and Ocean De-Aliasing Level-1B (AOD1B) RL07) have the
potential to enhance signal retrieval; however, their combined
impact on gravity field estimation from the K-band Ranging
System (KBR) and the more precise Laser Ranging Interferometer
(LRI) remains insufficiently quantified. In this study, we
assess the influence of these updated background models using
eight sets of monthly GRACE-FO gravity field solutions spanning
June 2018 to December 2022. Our analysis demonstrates that LRI-
based solutions achieve lower noise levels than KBR-based ones
while maintaining consistent temporal signal characteristics.
The adoption of FES2022 and AOD1B RL07 effectively reduces noise
levels across both oceanic and desert regions and enhances the
temporal consistency of mass variation signals. Moreover, LRI-
based solutions exhibit more pronounced noise reduction than
KBR-based ones, with decreases of 5.3\% and 8.7\% over oceans
after applying P4M6 decorrelation filtering, suggesting the LRIs
superior measurement sensitivity. Overall, this study provides
quantitative evidence that refining background models is crucial
for realizing the potential of LRI observations to improve
monthly gravity field solutions. This advancement is expected to
hold more significant implications for the design of future
satellite gravimetry missions.}",
doi = {10.1109/JSTARS.2026.3651751},
adsurl = {https://ui.adsabs.harvard.edu/abs/2026IJSTA..19.4487S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Fri Apr 10, F. Flechtner