Ahmed, M, Sultan, M, Elbayoumi, T & Tissot, P 2019, ‘Forecasting grace data over the African watersheds using artificial neural networks’, <em>Remote Sensing</em>, vol. 11, no. 15, article number 1769.
Babaeian, E, Sadeghi, M, Jones, SB, Montzka, C, Vereecken, H & Tuller, M 2019, ‘Ground, proximal, and satellite remote sensing of soil moisture’, <em>Reviews of Geophysics</em>, vol. 57, no. 2, pp. 530–616.
Becker, M, Meyssignac, B, Xavier, L, Cazenave, A, Alkama, R & Decharme, B 2011, ‘Past terrestrial water storage (1980–2008) in the amazon basin reconstructed from GRACE and in situ river Gauging data’, <em>Hydrology and Earth System Sciences</em>, vol. 15, no. 2, pp. 533–546.
Bonaccorso, G 2018, <em>Machine Learning Algorithms: Popular algorithms for data science and machine learning</em>. Packt Publishing Ltd, Birmingham, UK.
Chai, T & Draxler, RR 2014, ‘Root mean square error (RMSE) or mean absolute error (MAE)? – arguments against avoiding RMSE in the literature’, <em>Geoscientific model development</em>, vol. 7, no. 3, pp. 1247–1250.
Chen, JL, Wilson, CR, Tapley, BD & Grand, S 2007, ‘Grace detects coseismic and postseismic deformation from the Sumatra-Andaman earthquake’, <em>Geophysical Research Letters</em>, vol. 34, no. 13, pp. 1–5.
Cheng, M & Tapley, BD 2004, ‘Variations in the Earth’s oblateness during the past 28 years’, <em>Journal of Geophysical Research: Solid Earth</em>, vol. 109(B9), pp. 1–9.
Eicker, A, Schumacher, M, Kusche, J, Döll, P & Schmied, HM 2014, ‘Calibration/data assimilation approach for integrating GRACE data into the watergap global hydrology model (WGHM) using an ensemble Kalman filter: First results’, <em>Surveys in Geophysics</em>, vol. 35, no. 6, pp. 1285–1309.
Hamshaw, SD, Dewoolkar, MM, Schroth, AW, Wemple, BC & Rizzo, DM 2018, ‘A new machine-learning approach for classifying hysteresis in suspended-sediment discharge relationships using high-frequency monitoring data’, <em>Water Resources Research</em>, vol. 54, no. 6, pp. 4040–4058.
Irrgang, C, Saynisch-Wagner, J, Dill, R, Boergens, E & Thomas, M 2020, ‘Self-validating deep learning for recovering terrestrial water storage from gravity and altimetry measurements’, <em>Geophysical Research Letters</em>, vol. 47, no. 17, article number e2020GL089258.
Jing, W, Di, L, Zhao, X, Yao, L, Xia, X, Liu, Y, Yang, J, Li, Y & Zhou, C 2020, ‘A data-driven approach to generate past GRACE-like terrestrial water storage solution by calibrating the land surface model simulations’, <em>Advances in Water Resources</em>, vol. 143, article number 103683.
Kuczynska-Siehien, J, Piretzidis, D, Sideris, MG, Olszak, T & Szabó, V 2019, ‘Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data’, <em>Journal of Applied Geodesy</em>, vol. 13, no. 3, pp. 229–243.
Liu, Y, Dorigo, WA, Parinussa, R, de Jeu, RA, Wagner, W, McCabe, MF, Evans, J & Van Dijk, A 2012, ‘Trend-preserving blending of passive and active microwave soil moisture retrievals’, <em>Remote Sensing of Environment</em>, vol. 123, pp. 280–297.
Maulud, D & Abdulazeez, AM 2020, ‘A review on linear regression comprehensive in machine learning’, <em>Journal of Applied Science and Technology Trends</em>, vol. 1, no. 4, pp. 140–147.
NASA’s Goddard Earth Sciences Data and Information Services Center, 2023. Available from: <<a href="https://disc.gsfc.nasa.gov/" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://disc.gsfc.nasa.gov/</a>>. [10 December 2023].
Nash, J 1970, ‘River flow forecasting through conceptual models, I: A discussion of principles’, <em>Journal of Hydrology</em>, vol. 10, no. 3, pp. 398–409.
Njoku, EG, Ashcroft, P, Chan, TK & Li, L 2005, ‘Global survey and statistics of radio-frequency interference in AMSR-E land observations’, <em>IEEE Transactions on Geoscience and Remote Sensing</em>, vol. 43, no. 5, pp. 938–947.
Peltier, W, Argus, DF & Drummond, R 2018, ‘Comment on “an assessment of the ICE-6G_C (VM5a) glacial isostatic adjustment model” by Purcell et al.’ <em>Journal of Geophysical Research: Solid Earth</em>, vol. 123, no. 2, pp. 2019–2028.
Physical Oceanography Distributed Active Archive Center, 2023. Available from: <<a href="https://podaac-tools.jpl.nasa.gov/" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://podaac-tools.jpl.nasa.gov/</a>>. [10 December 2023].
Ries, J, Bettadpur, S, Eanes, R, Kang, Z, Ko, U, McCullough, C, Nagel, P, Pie, N, Poole, S, Richter, T, Save, H & Tapley, B, 2016, <em>The combined gravity model GGM05C</em>, GFZ Data Services.
Robinson, DA, Campbell, CS, Hopmans, JW, Hornbuckle, BK, Jones, SB, Knight, R, Ogden, F, Selker, J & Wendroth, O 2008, ‘Soil moisture measurement for ecological and hydrological watershed-scale observatories: A review’, <em>Vadose Zone Journal</em>, vol. 7, no. 1, pp. 358–389.
Seyoum, WM, Kwon, D & Milewski, AM 2019, ‘Downscaling GRACE TWSA data into high-resolution groundwater level anomaly using machine learning-based models in a glacial aquifer system’, <em>Remote Sensing</em>, vol. 11, no. 7, article number 824.
Seyoum, WM & Milewski, AM 2017, ‘Improved methods for estimating local terrestrial water dynamics from GRACE in the northern high plains’, <em>Advances in water resources</em>, vol. 110, pp. 279–290.
Sun, AY, Scanlon, BR, Save, H & Rateb, A 2021, ‘Reconstruction of GRACE total water storage through automated machine learning’, <em>Water Resources Research</em>, vol. 57, no. 2, article number e2020WR028666.
Sun, AY, Scanlon, BR, Zhang, Z, Walling, D, Bhanja, SN, Mukherjee, A & Zhong, Z 2019, ‘Combining physically based modeling and deep learning for fusing GRACE satellite data: can we learn from mismatch?’, <em>Water Resources Research</em>, vol. 55, no. 2, pp. 1179–1195.
Sun, Z, Long, D, Yang, W, Li, X & Pan, Y 2020, ‘Reconstruction of GRACE data on changes in total water storage over the global land surface and 60 basins’, <em>Water Resources Research</em>, vol. 56(4), article number e2019WR026250.
Swenson, S, Chambers, D & Wahr, J 2008a, ‘Estimating geocenter variations from a combination of GRACE and ocean model output’, <em>Journal of Geophysical Research: Solid Earth</em>, vol. 113, no. B8, pp. 1–12.
Swenson, S & Wahr, J 2006, ‘Post-processing removal of correlated errors in GRACE data’, <em>Geophysical research letters</em>, vol. 33, no. 8, pp. 1–4.
Szabó, V & Marjańska, D 2020, ‘Accuracy analysis of gravity field changes from GRACE RL06 and RL05 data compared to in situ gravimetric measurements in the context of choosing optimal filtering type’, <em>Artificial Satellites</em>, vol. 55, no. 3, pp. 100–117.
Szabó, V & Osińska-Skotak, K 2023, ‘Similarities and differences in the Earth’s water variations signal provided by GRACE and AMSR-E observations using maximum covariance analysis at various land cover data backgrounds’ <em>Artificial Satellites</em>, vol. 58, no. 2, pp. 63–87.
Szabó, V 2023, ‘Comparison features importance for temporal and spatial-temporal approaches in GRACE and GRACE-FO signal reconstruction based on GLDAS data’, <em>International Journal of Hydrology Science and Technology</em>, vol. 16, no. 4, pp. 370–389.
Tapley, BD, Bettadpur, S, Watkins, M & Reigber, C 2004, ‘The gravity recovery and climate experiment: Mission overview and early results’, <em>Geophysical research letters</em>, vol. 31, no. 9.
van der Vliet, M, van der Schalie, R, Rodriguez-Fernandez, N, Colliander, A, de Jeu, R, Preimesberger, W, Scanlon, T & Dorigo, W 2020, ‘Reconciling flagging strategies for multi-sensor satellite soil moisture climate data records’, <em>Remote Sensing</em>, vol. 12, no. 20, article number 3439.
Wahr, J, Molenaar, M & Bryan, F 1998, ‘Time variability of the Earth’s gravity field: Hydrological and oceanic effects and their possible detection using GRACE’, <em>Journal of Geophysical Research: Solid Earth</em>, vol. 103, no. B12, pp. 30205–30229.
Wziontek, H, Bonvalot, S, Falk, R, Gabalda, G, Mäkinen, J, Pálinkáš, V, Rülke, A & Vitushkin, L 2021, ‘Status of the International Gravity Reference System and Frame’, <em>Journal of Geodesy</em> vol. 95 no. 7.