Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China

Chu, Jiangdong; Su, Xiaoling; Jiang, Tianliang; Qi, Jixia; Zhang, Gengxi; Wu, Haijiang

doi:10.1007/s12665-022-10716-y

Environ Earth Sci

2023

DOI: 10.1007/s12665-022-10716-y

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Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China

Jiangdong Chu

Xiaoling Su

Tianliang Jiang

et al.

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Cited by 8 publications

(1 citation statement)

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“…Another approach involves leveraging artificial intelligence techniques, such as machine learning and deep learning [14,[27][28][29][30][31]. Machine learning is a popular branch of artificial intelligence widely used for solving scientific and engineering problems.…”

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confidence: 99%

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Mohasseb,

Shen,

Jiao

2024

Remote Sensing

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Over the past two decades, the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its successor, GRACE-follow on (GRACE-FO), have played a vital role in climate research. However, the absence of certain observations during and between these missions has presented a persistent challenge. Despite numerous studies attempting to address this issue with mathematical and statistical methods, no definitive optimal approach has been established. This study introduces a practical solution using Linear Regression Analysis (LRA) to overcome data gaps in both GRACE data types—mascon and spherical harmonic coefficients (SHCs). The proposed methodology is tailored to monsoon patterns and demonstrates efficacy in filling data gaps. To validate the approach, a global analysis was conducted across eight basins, monitoring changes in total water storage (TWS) using the technique. The results were compared with various geodetic products, including data from the Swarm mission, Institute of Geodesy and Geoinformation (IGG), Quantum Frontiers (QF), and Singular Spectrum Analysis (SSA) coefficients. Artificial data gaps were introduced within GRACE observations for further validation. This research highlights the effectiveness of the monsoon method in comparison to other gap-filling approaches, showing a strong similarity between gap-filling results and GRACE’s SHCs, with an absolute relative error approaching zero. In the mascon approach, the coefficient of determination (R2) exceeded 91% for all months. This study offers a readily usable gap-filling product—SHCs and smoothed gridded observations—with accurate error estimates. These resources are now accessible for a wide range of applications, providing a valuable tool for the scientific community.

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confidence: 99%

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Mohasseb,

Shen,

Jiao

2024

Remote Sensing

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Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Su,

Feng,

et al. 2024

Geosci J

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Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

Azizi,

Akhtar,

Tischbein

et al. 2024

Journal of Environmental Management

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Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China

Cited by 8 publications

References 57 publications

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Monsoon-Based Linear Regression Analysis for Filling Data Gaps in Gravity Recovery and Climate Experiment Satellite Observations

Analysis of watershed terrestrial water storage anomalies by Bi-LSTM with X-11 time series prediction combined model

Assessing long-term water storage dynamics in Afghanistan: An integrated approach using machine learning, hydrological models, and remote sensing

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