{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:53:19Z","timestamp":1776300799497,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T00:00:00Z","timestamp":1630713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Groundwater has a significant contribution to water storage and is considered to be one of the sources for agricultural irrigation; industrial; and domestic water use. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a unique opportunity to evaluate terrestrial water storage (TWS) and groundwater storage (GWS) at a large spatial scale. However; the coarse resolution of GRACE limits its ability to investigate the water storage change at a small scale. It is; therefore; needed to improve the resolution of GRACE data at a spatial scale applicable for regional-level studies. In this study; a machine-learning-based downscaling random forest model (RFM) and artificial neural network (ANN) model were developed to downscale GRACE data (TWS and GWS) from 1\u00b0 to a higher resolution (0.25\u00b0). The spatial maps of downscaled TWS and GWS were generated over the Indus basin irrigation system (IBIS). Variations in TWS of GRACE in combination with geospatial variables; including digital elevation model (DEM), slope; aspect; and hydrological variables; including soil moisture; evapotranspiration; rainfall; surface runoff; canopy water; and temperature; were used. The geospatial and hydrological variables could potentially contribute to; or correlate with; GRACE TWS. The RFM outperformed the ANN model and results show Pearson correlation coefficient (R) (0.97), root mean square error (RMSE) (11.83 mm), mean absolute error (MAE) (7.71 mm), and Nash\u2013Sutcliffe efficiency (NSE) (0.94) while comparing with the training dataset from 2003 to 2016. These results indicate the suitability of RFM to downscale GRACE data at a regional scale. The downscaled GWS data were analyzed; and we observed that the region has lost GWS of about \u22129.54 \u00b1 1.27 km3 at the rate of \u22120.68 \u00b1 0.09 km3\/year from 2003 to 2016. The validation results showed that R between downscaled GWS and observational wells GWS are 0.67 and 0.77 at seasonal and annual scales with a confidence level of 95%, respectively. It can; therefore; be concluded that the RFM has the potential to downscale GRACE data at a spatial scale suitable to predict GWS at regional scales.<\/jats:p>","DOI":"10.3390\/rs13173513","type":"journal-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T13:18:26Z","timestamp":1630934306000},"page":"3513","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":111,"title":["Improving the Resolution of GRACE Data for Spatio-Temporal Groundwater Storage Assessment"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6377-6610","authenticated-orcid":false,"given":"Shoaib","family":"Ali","sequence":"first","affiliation":[{"name":"School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Dong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China"},{"name":"Key Laboratory of Effective Utilization of Agricultural Water Resources of Ministry of Agriculture, Northeast Agricultural University, Harbin 150030, China"},{"name":"Heilongjiang Provincial Key Laboratory of Water Resources and Water Conservancy Engineering in Cold Region, Northeast Agricultural University, Harbin 150030, China"},{"name":"Key Laboratory of Water-Saving Agriculture of Ordinary University in Heilongjiang Province, Northeast Agricultural University, Harbin 150030, China"}]},{"given":"Qiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7911-7548","authenticated-orcid":false,"given":"Muhammad Jehanzeb Masud","family":"Cheema","sequence":"additional","affiliation":[{"name":"Faculty of Agricultural Engineering and Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi 46000, Pakistan"}]},{"given":"Quoc Bao","family":"Pham","sequence":"additional","affiliation":[{"name":"Institute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City 75000, Vietnam"}]},{"given":"Md. Mafuzur","family":"Rahaman","sequence":"additional","affiliation":[{"name":"AECOM, 2380 McGee St Suite 200, Kansas City, MO 64108, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9303-9056","authenticated-orcid":false,"given":"Thanh Duc","family":"Dang","sequence":"additional","affiliation":[{"name":"Institute for Water and Environment Research, Thuyloi University, Ho Chi Minh City 08084, Vietnam"}]},{"given":"Duong Tran","family":"Anh","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1038\/nature08238","article-title":"Satellite-based estimates of groundwater depletion in India","volume":"460","author":"Rodell","year":"2009","journal-title":"Nature"},{"key":"ref_2","unstructured":"Wang, Y. (2010). 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