Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method
Terrestrial water storage represents both surface and subsurface water resources and plays a crucial role in the global hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides large-scale and high-stability terrestrial water storage anomaly (TWSA) data for...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468418 |
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| author | Fengmin Hu Beibei Yang Zushuai Wei Changlu Cui Lingkui Meng |
| author_facet | Fengmin Hu Beibei Yang Zushuai Wei Changlu Cui Lingkui Meng |
| author_sort | Fengmin Hu |
| collection | DOAJ |
| description | Terrestrial water storage represents both surface and subsurface water resources and plays a crucial role in the global hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides large-scale and high-stability terrestrial water storage anomaly (TWSA) data for water resource analysis. However, an 11-month data gap exists between the two generations of gravity satellites, GRACE and GRACE Follow-On (July 2017 to May 2018), posing challenges for TWSA analysis and applications. This study proposed a long short-term memory (LSTM) and Bayesian convolutional neural network (BCNN) combined in an LSTM-BCNN reconstruction model. By extracting long-term temporal change features and recent environmental spatial features, the model reconstructs and corrects the trend and random terms of the TWSA decomposition to reconstruct the TWSA data during the gap period. The model was evaluated on a global scale across 40 basins and at a grid scale. At the grid scale, the LSTM-BCNN model achieves a correlation coefficient (CC) of 0.89 ± 0.06, Nash-Sutcliffe efficiency coefficient (NSE) of 0.78 ± 0.12, and normalized root mean square error (NRMSE) of 0.11 ± 0.02. Across the 40 basins, the LSTM-BCNN model effectively reconstructed the TWSA data during the gap period. |
| format | Article |
| id | doaj-art-dba6adf55590460da43ca8f6b2733a2c |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-dba6adf55590460da43ca8f6b2733a2c2025-08-25T11:31:34ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2468418Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction methodFengmin Hu0Beibei Yang1Zushuai Wei2Changlu Cui3Lingkui Meng4College of Surveying and Geo-Informatics, North China University of Water Resources and Electric Power, Zhengzhou, People’s Republic of ChinaChangjiang Spatial Information Technology Engineering Co., Ltd, Wuhan, People’s Republic of ChinaSchool of Artificial Intelligence, Jianghan University, Wuhan, People’s Republic of ChinaChangjiang River Scientific Research Institute, Changjiang River Water Resources Commission, Wuhan, People’s Republic of ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, People’s Republic of ChinaTerrestrial water storage represents both surface and subsurface water resources and plays a crucial role in the global hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides large-scale and high-stability terrestrial water storage anomaly (TWSA) data for water resource analysis. However, an 11-month data gap exists between the two generations of gravity satellites, GRACE and GRACE Follow-On (July 2017 to May 2018), posing challenges for TWSA analysis and applications. This study proposed a long short-term memory (LSTM) and Bayesian convolutional neural network (BCNN) combined in an LSTM-BCNN reconstruction model. By extracting long-term temporal change features and recent environmental spatial features, the model reconstructs and corrects the trend and random terms of the TWSA decomposition to reconstruct the TWSA data during the gap period. The model was evaluated on a global scale across 40 basins and at a grid scale. At the grid scale, the LSTM-BCNN model achieves a correlation coefficient (CC) of 0.89 ± 0.06, Nash-Sutcliffe efficiency coefficient (NSE) of 0.78 ± 0.12, and normalized root mean square error (NRMSE) of 0.11 ± 0.02. Across the 40 basins, the LSTM-BCNN model effectively reconstructed the TWSA data during the gap period.https://www.tandfonline.com/doi/10.1080/17538947.2025.2468418GRACEGRACE-FOdata gapLong short-term memory Bayesian convolutional neural network (LSTM-BCNN) |
| spellingShingle | Fengmin Hu Beibei Yang Zushuai Wei Changlu Cui Lingkui Meng Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method International Journal of Digital Earth GRACE GRACE-FO data gap Long short-term memory Bayesian convolutional neural network (LSTM-BCNN) |
| title | Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method |
| title_full | Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method |
| title_fullStr | Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method |
| title_full_unstemmed | Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method |
| title_short | Filling the data gap between GRACE and GRACE-FO based on a two-step reconstruction method |
| title_sort | filling the data gap between grace and grace fo based on a two step reconstruction method |
| topic | GRACE GRACE-FO data gap Long short-term memory Bayesian convolutional neural network (LSTM-BCNN) |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2468418 |
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