Research graph
References from A spatiotemporal reconstruction framework for GRACE/GRACE-FO-derived groundwater storage anomalies based on historical phase-constrained gap filling and physics-guided global–local downscaling. Local targets link to admitted publications; unresolved targets remain external evidence.
Downscaled-GRACE data reveal anthropogenic and climate-induced water storage decline across the indus basin
10.1029/2023wr035882 · 2024 · External reference
A unified vegetation index for quantifying the terrestrial biosphere
10.1126/sciadv.abc7447 · 2021 · External reference
Improving spatial resolution of GRACE-derived water storage changes based on geographically weighted regression downscaled model
10.1109/jstars.2023.3272916 · 2023 · External reference
Impact of uncertainty estimation of hydrological models on spectral downscaling of GRACE-based terrestrial and groundwater storage variation estimations
10.3390/rs15163967 · 2023 · External reference
A new approach for generating optimal GLDAS hydrological products and uncertainties
10.1016/j.scitotenv.2020.138932 · 2020 · External reference
New spectro-spatial downscaling approach for terrestrial and groundwater storage variations estimated by GRACE models
10.1016/j.jhydrol.2022.128635 · 2022 · External reference
A new spatiotemporal estimator to downscale GRACE gravity models for terrestrial and groundwater storage variations estimation
10.3390/rs14235991 · 2022 · External reference
Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory
10.1016/j.jhydrol.2025.133754 · 2025 · External reference
An iterative ICA-based reconstruction method to produce consistent time-variable total water storage fields using GRACE and swarm satellite data
10.3390/rs12101639 · 2020 · External reference
Drought monitoring by downscaling GRACE-derived terrestrial water storage anomalies: a deep learning approach
10.1016/j.jhydrol.2022.128838 · 2023 · External reference
Unresolved reference
External reference
CIDR interpolation: an enhanced SSA-based temporal filling framework for restoring continuity in downscaled GRACE(-FO) TWSA products
10.1016/j.jhydrol.2025.134606 · 2026 · External reference
DeepRec: Global terrestrial water storage reconstruction since 1941 using spatiotemporal‐aware deep learning model
2026 · External reference
The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model
10.1007/s00190-023-01763-9 · 2023 · External reference
Unresolved reference
External reference
Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
10.1038/s44221-024-00194-w · 2024 · External reference
A fast generative adversarial network combined with transformer for downscaling GRACE terrestrial water storage data in southwestern China
2024 · External reference
Deep learning approaches to spatial downscaling of GRACE terrestrial water storage products using EALCO model over Canada
10.1080/07038992.2021.1954498 · 2021 · External reference
Machine learning assessment of hydrological model performance under localized water storage changes through downscaling
10.1016/j.jhydrol.2023.130597 · 2024 · External reference
High‐resolution terrestrial water storage estimates from GRACE and land surface models
10.1029/2023wr035483 · 2024 · External reference
HRU-based downscaling of GRACE-TWS to quantify the hydrogeological fluxes and specific yield in the lower middle ganga basin
10.1016/j.jhydrol.2024.131591 · 2024 · External reference
Accuracy of scaled GRACE terrestrial water storage estimates
10.1029/2011wr011453 · 2012 · External reference
Global GRACE data assimilation for groundwater and drought monitoring: advances and challenges
10.1029/2018wr024618 · 2019 · External reference
Reproducing GRACE total water storage change at finer spatial scales
2026 · External reference
10.1029/2021gl093492
10.1029/2021gl093492 · External reference
A new GRACE downscaling approach for deriving high‐resolution groundwater storage changes using ground‐based scaling factors
10.1029/2023wr035210 · 2024 · External reference
A novel approach to retrieving the surface soil freeze/thaw state in the Qinghai-Tibetan Plateau using the seasonality of CYGNSS time series
2025 · External reference
Spatiotemporal variability and drivers of groundwater storage change across China revealed by GRACE/GRACEFO
2026 · External reference
A hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging
10.1007/s00190-020-01460-x · 2021 · External reference
Unprecedented large-scale aquifer recovery through human intervention
10.1038/s41467-025-62719-5 · 2025 · External reference
Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China’s Yangtze River Basin
10.1016/j.rse.2015.07.003 · 2015 · External reference
A novel spatial downscaling algorithm based on deep learning considering geographical spatial heterogeneity and nonlinear changes: a case study of the yangtze river basin
10.1016/j.engappai.2025.113328 · 2026 · External reference
Assimilation of in-situ groundwater level data into the obtained groundwater storage from GRACE and GLDAS for spatial downscaling
10.1016/j.jhydrol.2025.133604 · 2025 · External reference
Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data
10.5194/essd-17-2575-2025 · 2025 · External reference
Downscaling GRACE remote sensing datasets to high-resolution groundwater storage change maps of California’s Central Valley
10.3390/rs10010143 · 2018 · External reference
Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap
10.1016/j.jhydrol.2021.127244 · 2022 · External reference
The global water resources and use model WaterGAP v2.2d: Model description and evaluation
10.5194/gmd-14-1037-2021 · 2021 · External reference
10.5194/gmd-14-1037-2021
10.5194/gmd-14-1037-2021 · External reference
ERA5-land: a state-of-the-art global reanalysis dataset for land applications
10.5194/essd-13-4349-2021 · 2021 · External reference
A physical/statistical data-fusion for the dynamical downscaling of GRACE data at daily and 1 km resolution
10.1016/j.jhydrol.2023.130565 · 2024 · External reference
1 km monthly temperature and precipitation dataset for China from 1901 to 2017
10.5194/essd-11-1931-2019 · 2019 · External reference
Global terrestrial water storage and drought severity under climate change
10.1038/s41558-020-00972-w · 2021 · External reference
Data-adaptive spatio-temporal filtering of GRACE data
10.1093/gji/ggz409 · 2019 · External reference
GRACE downscaler: a framework to develop and evaluate downscaling models for GRACE
10.3390/rs15092247 · 2023 · External reference
Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers
10.1029/2020wr027556 · 2020 · External reference
Reconstruction of GRACE mass change time series using a Bayesian framework
10.1029/2021ea002162 · 2022 · External reference
Unresolved reference
External reference
The global land data assimilation system
10.1175/bams-85-3-381 · 2004 · External reference
Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods
10.1016/j.jenvman.2022.117180 · 2023 · External reference
Global water resources and the role of groundwater in a resilient water future
10.1038/s43017-022-00378-6 · 2023 · External reference
Global evaluation of new GRACE mascon products for hydrologic applications
10.1002/2016wr019494 · 2016 · External reference
Performance of different ensemble Kalman filter structures to assimilate GRACE terrestrial water storage estimates into a high‐resolution hydrological model: a synthetic study
10.1029/2018wr022785 · 2018 · External reference
Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: a systematic review
10.1016/j.isprsjprs.2023.12.011 · 2024 · External reference
Combining physically based modeling and deep learning for fusing GRACE satellite data: can we learn from mismatch?
10.1029/2018wr023333 · 2019 · External reference
Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins
10.1029/2019wr026250 · 2020 · External reference
Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models
10.1016/j.atmosres.2023.106815 · 2023 · External reference
GRACE measurements of mass variability in the earth system
10.1126/science.1099192 · 2004 · External reference
Contributions of GRACE to understanding climate change
10.1038/s41558-019-0456-2 · 2019 · External reference
10.5194/essd-12-1385-2020
10.5194/essd-12-1385-2020 · External reference
Downscaling GRACE total water storage change using partial least squares regression
10.1038/s41597-021-00862-6 · 2021 · External reference
Bridging the gap between GRACE and GRACE follow-on monthly gravity field solutions using improved multichannel singular spectrum analysis
10.1016/j.jhydrol.2021.125972 · 2021 · External reference
Filling GRACE data gap using an innovative transformer-based deep learning approach
10.1016/j.rse.2024.114465 · 2024 · External reference
Underground well water level observation grid dataset from 2005 to 2022
10.1038/s41597-025-04799-y · 2025 · External reference
Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with random forests
10.1016/j.jhydrol.2024.131708 · 2024 · External reference
A high-accuracy map of global terrain elevations
10.1002/2017gl072874 · 2017 · External reference
Reconstruction of continuous GRACE/GRACE-FO terrestrial water storage anomalies based on time series decomposition
10.1016/j.jhydrol.2021.127018 · 2021 · External reference
A two‐step linear model to fill the data gap between GRACE and GRACE‐FO terrestrial water storage anomalies
10.1029/2022wr034139 · 2023 · External reference
A spatially promoted SVM model for GRACE downscaling: using ground and satellite-based datasets
10.1016/j.jhydrol.2023.130214 · 2023 · External reference
Filling the Data Gaps within GRACE Missions using Singular Spectrum Analysis
10.1029/2020jb021227 · 2021 · External reference
Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes
10.1016/j.jhydrol.2022.128447 · 2022 · External reference
A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly
10.1016/j.rse.2024.114359 · 2024 · External reference
Unexpected groundwater recovery with decreasing agricultural irrigation in the Yellow River basin
10.1016/j.agwat.2018.12.009 · 2019 · External reference
Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index
2025 · External reference
10.1016/j.jhydrol.2025.134280
10.1016/j.jhydrol.2025.134280 · External reference
Bridging the gap between GRACE and GRACE-FO using a hydrological model
10.1016/j.scitotenv.2022.153659 · 2022 · External reference
Gap-filling GRACE and GRACE-FO data with a climate adjustment scheme using singular spectrum analysis
10.1016/j.jhydrol.2025.132782 · 2025 · External reference
Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017
10.1016/j.rse.2018.12.031 · 2019 · External reference
Spatiotemporal downscaling of GRACE total water storage using land surface model outputs
10.3390/rs13050900 · 2021 · External reference
Separating the precipitation‐ and non‐precipitation‐ driven water storage trends in China
10.1029/2022wr033261 · 2023 · External reference
Global evaluation of new GRACE mascon products for hydrologic applications
10.1002/2016wr019494 · ExternalCitation · doi-reference
A high-accuracy map of global terrain elevations
10.1002/2017gl072874 · ExternalCitation · doi-reference
A hybrid approach for recovering high-resolution temporal gravity fields from satellite laser ranging
10.1007/s00190-020-01460-x · ExternalCitation · doi-reference
The global land water storage data set release 2 (GLWS2.0) derived via assimilating GRACE and GRACE-FO data into a global hydrological model
10.1007/s00190-023-01763-9 · ExternalCitation · doi-reference
Unexpected groundwater recovery with decreasing agricultural irrigation in the Yellow River basin
10.1016/j.agwat.2018.12.009 · ExternalCitation · doi-reference
Development of high-resolution gridded data for water availability identification through GRACE data downscaling: Development of machine learning models
10.1016/j.atmosres.2023.106815 · ExternalCitation · doi-reference
A novel spatial downscaling algorithm based on deep learning considering geographical spatial heterogeneity and nonlinear changes: a case study of the yangtze river basin
10.1016/j.engappai.2025.113328 · ExternalCitation · doi-reference
Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: a systematic review
10.1016/j.isprsjprs.2023.12.011 · ExternalCitation · doi-reference
Enhancing spatial resolution of GRACE-derived groundwater storage anomalies in Urmia catchment using machine learning downscaling methods
10.1016/j.jenvman.2022.117180 · ExternalCitation · doi-reference
Bridging the gap between GRACE and GRACE follow-on monthly gravity field solutions using improved multichannel singular spectrum analysis
10.1016/j.jhydrol.2021.125972 · ExternalCitation · doi-reference
Reconstruction of continuous GRACE/GRACE-FO terrestrial water storage anomalies based on time series decomposition
10.1016/j.jhydrol.2021.127018 · ExternalCitation · doi-reference
Bayesian convolutional neural networks for predicting the terrestrial water storage anomalies during GRACE and GRACE-FO gap
10.1016/j.jhydrol.2021.127244 · ExternalCitation · doi-reference
Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes
10.1016/j.jhydrol.2022.128447 · ExternalCitation · doi-reference
New spectro-spatial downscaling approach for terrestrial and groundwater storage variations estimated by GRACE models
10.1016/j.jhydrol.2022.128635 · ExternalCitation · doi-reference
Drought monitoring by downscaling GRACE-derived terrestrial water storage anomalies: a deep learning approach
10.1016/j.jhydrol.2022.128838 · ExternalCitation · doi-reference
A spatially promoted SVM model for GRACE downscaling: using ground and satellite-based datasets
10.1016/j.jhydrol.2023.130214 · ExternalCitation · doi-reference
A physical/statistical data-fusion for the dynamical downscaling of GRACE data at daily and 1 km resolution
10.1016/j.jhydrol.2023.130565 · ExternalCitation · doi-reference
Machine learning assessment of hydrological model performance under localized water storage changes through downscaling
10.1016/j.jhydrol.2023.130597 · ExternalCitation · doi-reference
HRU-based downscaling of GRACE-TWS to quantify the hydrogeological fluxes and specific yield in the lower middle ganga basin
10.1016/j.jhydrol.2024.131591 · ExternalCitation · doi-reference
Spatial downscaling of GRACE-derived groundwater storage changes across diverse climates and human interventions with random forests
10.1016/j.jhydrol.2024.131708 · ExternalCitation · doi-reference
Gap-filling GRACE and GRACE-FO data with a climate adjustment scheme using singular spectrum analysis
10.1016/j.jhydrol.2025.132782 · ExternalCitation · doi-reference
Assimilation of in-situ groundwater level data into the obtained groundwater storage from GRACE and GLDAS for spatial downscaling
10.1016/j.jhydrol.2025.133604 · ExternalCitation · doi-reference
Retrieving snow water equivalent from GRACE/GRACE-FO terrestrial water storage anomalies using modified spectral combination theory
10.1016/j.jhydrol.2025.133754 · ExternalCitation · doi-reference
10.1016/j.jhydrol.2025.134280
10.1016/j.jhydrol.2025.134280 · ExternalCitation · doi-reference
CIDR interpolation: an enhanced SSA-based temporal filling framework for restoring continuity in downscaled GRACE(-FO) TWSA products
10.1016/j.jhydrol.2025.134606 · ExternalCitation · doi-reference
Deriving scaling factors using a global hydrological model to restore GRACE total water storage changes for China’s Yangtze River Basin
10.1016/j.rse.2015.07.003 · ExternalCitation · doi-reference
Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017
10.1016/j.rse.2018.12.031 · ExternalCitation · doi-reference
A machine learning downscaling framework based on a physically constrained sliding window technique for improving resolution of global water storage anomaly
10.1016/j.rse.2024.114359 · ExternalCitation · doi-reference
Filling GRACE data gap using an innovative transformer-based deep learning approach
10.1016/j.rse.2024.114465 · ExternalCitation · doi-reference
A new approach for generating optimal GLDAS hydrological products and uncertainties
10.1016/j.scitotenv.2020.138932 · ExternalCitation · doi-reference
Bridging the gap between GRACE and GRACE-FO using a hydrological model
10.1016/j.scitotenv.2022.153659 · ExternalCitation · doi-reference
Accuracy of scaled GRACE terrestrial water storage estimates
10.1029/2011wr011453 · ExternalCitation · doi-reference
Performance of different ensemble Kalman filter structures to assimilate GRACE terrestrial water storage estimates into a high‐resolution hydrological model: a synthetic study
10.1029/2018wr022785 · ExternalCitation · doi-reference
Combining physically based modeling and deep learning for fusing GRACE satellite data: can we learn from mismatch?
10.1029/2018wr023333 · ExternalCitation · doi-reference
Global GRACE data assimilation for groundwater and drought monitoring: advances and challenges
10.1029/2018wr024618 · ExternalCitation · doi-reference
Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins
10.1029/2019wr026250 · ExternalCitation · doi-reference
Filling the Data Gaps within GRACE Missions using Singular Spectrum Analysis
10.1029/2020jb021227 · ExternalCitation · doi-reference
Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers
10.1029/2020wr027556 · ExternalCitation · doi-reference
Reconstruction of GRACE mass change time series using a Bayesian framework
10.1029/2021ea002162 · ExternalCitation · doi-reference
10.1029/2021gl093492
10.1029/2021gl093492 · ExternalCitation · doi-reference
Separating the precipitation‐ and non‐precipitation‐ driven water storage trends in China
10.1029/2022wr033261 · ExternalCitation · doi-reference
A two‐step linear model to fill the data gap between GRACE and GRACE‐FO terrestrial water storage anomalies
10.1029/2022wr034139 · ExternalCitation · doi-reference
A new GRACE downscaling approach for deriving high‐resolution groundwater storage changes using ground‐based scaling factors
10.1029/2023wr035210 · ExternalCitation · doi-reference
High‐resolution terrestrial water storage estimates from GRACE and land surface models
10.1029/2023wr035483 · ExternalCitation · doi-reference
Downscaled-GRACE data reveal anthropogenic and climate-induced water storage decline across the indus basin
10.1029/2023wr035882 · ExternalCitation · doi-reference
Unprecedented large-scale aquifer recovery through human intervention
10.1038/s41467-025-62719-5 · ExternalCitation · doi-reference
Contributions of GRACE to understanding climate change
10.1038/s41558-019-0456-2 · ExternalCitation · doi-reference
Global terrestrial water storage and drought severity under climate change
10.1038/s41558-020-00972-w · ExternalCitation · doi-reference
Downscaling GRACE total water storage change using partial least squares regression
10.1038/s41597-021-00862-6 · ExternalCitation · doi-reference
Underground well water level observation grid dataset from 2005 to 2022
10.1038/s41597-025-04799-y · ExternalCitation · doi-reference
Global water resources and the role of groundwater in a resilient water future
10.1038/s43017-022-00378-6 · ExternalCitation · doi-reference
Global high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
10.1038/s44221-024-00194-w · ExternalCitation · doi-reference
Deep learning approaches to spatial downscaling of GRACE terrestrial water storage products using EALCO model over Canada
10.1080/07038992.2021.1954498 · ExternalCitation · doi-reference
Data-adaptive spatio-temporal filtering of GRACE data
10.1093/gji/ggz409 · ExternalCitation · doi-reference
Improving spatial resolution of GRACE-derived water storage changes based on geographically weighted regression downscaled model
10.1109/jstars.2023.3272916 · ExternalCitation · doi-reference
A unified vegetation index for quantifying the terrestrial biosphere
10.1126/sciadv.abc7447 · ExternalCitation · doi-reference
GRACE measurements of mass variability in the earth system
10.1126/science.1099192 · ExternalCitation · doi-reference
The global land data assimilation system
10.1175/bams-85-3-381 · ExternalCitation · doi-reference
Downscaling GRACE remote sensing datasets to high-resolution groundwater storage change maps of California’s Central Valley
10.3390/rs10010143 · ExternalCitation · doi-reference
An iterative ICA-based reconstruction method to produce consistent time-variable total water storage fields using GRACE and swarm satellite data
10.3390/rs12101639 · ExternalCitation · doi-reference
Spatiotemporal downscaling of GRACE total water storage using land surface model outputs
10.3390/rs13050900 · ExternalCitation · doi-reference
A new spatiotemporal estimator to downscale GRACE gravity models for terrestrial and groundwater storage variations estimation
10.3390/rs14235991 · ExternalCitation · doi-reference
GRACE downscaler: a framework to develop and evaluate downscaling models for GRACE
10.3390/rs15092247 · ExternalCitation · doi-reference
Impact of uncertainty estimation of hydrological models on spectral downscaling of GRACE-based terrestrial and groundwater storage variation estimations
10.3390/rs15163967 · ExternalCitation · doi-reference
1 km monthly temperature and precipitation dataset for China from 1901 to 2017
10.5194/essd-11-1931-2019 · ExternalCitation · doi-reference
10.5194/essd-12-1385-2020
10.5194/essd-12-1385-2020 · ExternalCitation · doi-reference
ERA5-land: a state-of-the-art global reanalysis dataset for land applications
10.5194/essd-13-4349-2021 · ExternalCitation · doi-reference
Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data
10.5194/essd-17-2575-2025 · ExternalCitation · doi-reference
10.5194/gmd-14-1037-2021
10.5194/gmd-14-1037-2021 · ExternalCitation · doi-reference
