Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Shi, Zhuoya, Wang, Zemin, Zhang, Baojun, Barrand, Nicholas E., Luo, Manman, Wu, Shuang, An, Jiachun, Geng, Hong, and Wu, Haojian, 2025. A Unified Framework for Bridging the Data Gap Between GRACE/GRACE-FO for Both Greenland and Antarctica. IEEE Geoscience and Remote Sensing Letters, 22:LGRS.2025, doi:10.1109/LGRS.2025.3605913.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2025IGRSL..22L5913S,
author = {{Shi}, Zhuoya and {Wang}, Zemin and {Zhang}, Baojun and {Barrand}, Nicholas E. and {Luo}, Manman and {Wu}, Shuang and {An}, Jiachun and {Geng}, Hong and {Wu}, Haojian},
title = "{A Unified Framework for Bridging the Data Gap Between GRACE/GRACE-FO for Both Greenland and Antarctica}",
journal = {IEEE Geoscience and Remote Sensing Letters},
keywords = {Antarctica, data gap, gravity recovery and climate experiment (GRACE), Greenland, machine learning (ML), reconstruction},
year = 2025,
month = jan,
volume = {22},
eid = {LGRS.2025},
pages = {LGRS.2025},
abstract = "{The 11-month data gap between gravity recovery and climate experiment
(GRACE) and GRACE Follow-On (GRACE-FO) hinders monitoring long-
term ice mass change and its further analysis. While many
attempts have been made to bridge water storage gaps, few
unified frameworks exist to bridge the ice mass change gaps for
both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS).
This study combined partial least squares regression (PLSR) and
the Sparrow Search Algorithm optimized back propagation (SSA-BP)
to fill this gap in GrIS and AIS. During this process, seasonal
autoregressive integrated moving average (MA) with exogenous
variables (SARIMAX) and multiple linear regression (MLR) were
introduced as comparison. PSLR is utilized to select key
variables for constructing predictive models. We found SSA-BP
outperformed SARIMAX and MLR, with correlation coefficients
(CCs) and root mean square error (RMSE) at 0.99 and 39.22 Gt for
GrIS, and 0.95 and 189.85 Gt for AIS within the testing period.
SSA-BP demonstrated a reasonable mass change trend with less
noise than other methods. SSA-BP reconstructed result shows
superiority than other researches. Moreover, the reconstructed
seasonal signals highlight the importance of filling the gap,
showing decreased mass loss for GrIS and continuous mass loss
acceleration for AIS post-2016.}",
doi = {10.1109/LGRS.2025.3605913},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025IGRSL..22L5913S},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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Mon Oct 13, F. Flechtner