Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis

Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis

The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GFO) missions have provided valuable data for monitoring global terrestrial water storage anomalies (TWSA) over the past two decades. However, the nearly one-year gap between these missions pose challenges for long-term TWSA me...

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Bibliographic Details
Main Authors: Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:Science of Remote Sensing
Subjects:
GRACE
Gap filling
Mass change
Machine learning
Deep learning
Online Access:http://www.sciencedirect.com/science/article/pii/S2666017225000045
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