{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T18:04:55Z","timestamp":1775585095428,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,11]],"date-time":"2022-06-11T00:00:00Z","timestamp":1654905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074001"],"award-info":[{"award-number":["42074001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41974026"],"award-info":[{"award-number":["41974026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41774005"],"award-info":[{"award-number":["41774005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019M652010"],"award-info":[{"award-number":["2019M652010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019T120477"],"award-info":[{"award-number":["2019T120477"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KYCX21_2292"],"award-info":[{"award-number":["KYCX21_2292"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021WLKXJ098"],"award-info":[{"award-number":["2021WLKXJ098"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the China Postdoctoral Science Foundation","award":["42074001"],"award-info":[{"award-number":["42074001"]}]},{"name":"the China Postdoctoral Science Foundation","award":["41974026"],"award-info":[{"award-number":["41974026"]}]},{"name":"the China Postdoctoral Science Foundation","award":["41774005"],"award-info":[{"award-number":["41774005"]}]},{"name":"the China Postdoctoral Science Foundation","award":["2019M652010"],"award-info":[{"award-number":["2019M652010"]}]},{"name":"the China Postdoctoral Science Foundation","award":["2019T120477"],"award-info":[{"award-number":["2019T120477"]}]},{"name":"the China Postdoctoral Science Foundation","award":["KYCX21_2292"],"award-info":[{"award-number":["KYCX21_2292"]}]},{"name":"the China Postdoctoral Science Foundation","award":["2021WLKXJ098"],"award-info":[{"award-number":["2021WLKXJ098"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["42074001"],"award-info":[{"award-number":["42074001"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["41974026"],"award-info":[{"award-number":["41974026"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["41774005"],"award-info":[{"award-number":["41774005"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2019M652010"],"award-info":[{"award-number":["2019M652010"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2019T120477"],"award-info":[{"award-number":["2019T120477"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX21_2292"],"award-info":[{"award-number":["KYCX21_2292"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["2021WLKXJ098"],"award-info":[{"award-number":["2021WLKXJ098"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["42074001"],"award-info":[{"award-number":["42074001"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["41974026"],"award-info":[{"award-number":["41974026"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["41774005"],"award-info":[{"award-number":["41774005"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["2019M652010"],"award-info":[{"award-number":["2019M652010"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["2019T120477"],"award-info":[{"award-number":["2019T120477"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["KYCX21_2292"],"award-info":[{"award-number":["KYCX21_2292"]}]},{"name":"the Postgraduate Innovation Program of China University of Mining and Technology","award":["2021WLKXJ098"],"award-info":[{"award-number":["2021WLKXJ098"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-frequency and correlated noise filtering is one of the important preprocessing steps for GRACE level-2 products before calculating mass anomaly. Decorrelation and denoising kernel (DDK) filters are usually considered as such optimal filters to solve this problem. In this work, a sparse DDK filter is proposed. This is achieved by replacing Tikhonov regularization in traditional DDK filters with weighted L1 norm regularization. The proposed sparse DDK filter adopts a time-varying error covariance matrix, while the equivalent signal covariance matrix is adaptively determined by the Gravity Recovery and Climate Experiment (GRACE) monthly solution. The covariance matrix of the sparse DDK filtered solution is also developed from the Bayesian and error-propagation perspectives, respectively. Furthermore, we also compare and discuss the properties of different filters. The proposed sparse DDK has all the advantages of traditional filters, such as time-varying, location inhomogeneity, and anisotropy, etc. In addition, the filtered solution is sparse; that is, some high-degree and high-order terms are strictly zeros. This sparsity is beneficial in the following sense: high-degree and high-order sparsity mean that the dominating noise in high-degree and high-order terms is completely suppressed, at a slight cost that the tiny signals of these terms are also discarded. The Center for Space Research (CSR) GRACE monthly solutions and their error covariance matrices, from January 2004 to December 2010, are used to test the performance of the proposed sparse DDK filter. The results show that the sparse DDK can effectively decorrelate and denoise these data.<\/jats:p>","DOI":"10.3390\/rs14122810","type":"journal-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T23:55:24Z","timestamp":1655078124000},"page":"2810","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Sparse DDK: A Data-Driven Decorrelation Filter for GRACE Level-2 Products"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8551-2720","authenticated-orcid":false,"given":"Nijia","family":"Qian","sequence":"first","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands"}]},{"given":"Guobin","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Pavel","family":"Ditmar","sequence":"additional","affiliation":[{"name":"Department of Geoscience and Remote Sensing, Delft University of Technology, 2628 CN Delft, The Netherlands"}]},{"given":"Jingxiang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]},{"given":"Zhengqiang","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/s00190-017-1063-5","article-title":"Statistically optimal estimation of Greenland Ice Sheet mass variations from GRACE monthly solutions using an improved mascon approach","volume":"92","author":"Ran","year":"2018","journal-title":"J. Geod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1126\/science.abh4455","article-title":"A massive rock and ice avalanche caused the 2021 disaster at Chamoli, Indian Himalaya","volume":"373","author":"Shugar","year":"2021","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1365-246X.2008.03922.x","article-title":"The design of an optimal filter for monthly GRACE gravity models","volume":"175","author":"Klees","year":"2008","journal-title":"Geophys. J. Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1007\/s00190-009-0308-3","article-title":"Decorrelated GRACE time-variable gravity solutions by GFZ, and their validation using a hydrological model","volume":"83","author":"Kusche","year":"2009","journal-title":"J. Geod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s00190-007-0143-3","article-title":"Approximate decorrelation and non-isotropic smoothing of time-variable GRACE-type gravity field models","volume":"81","author":"Kusche","year":"2007","journal-title":"J. Geod."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Horvath, A., Murb\u00f6ck, M., Pail, R., and Horwath, M. (2018). Decorrelation of GRACE time variable gravity field solutions using full covariance information. Geosciences, 8.","DOI":"10.3390\/geosciences8090323"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s00190-012-0548-5","article-title":"Reducing errors in the GRACE gravity solutions using regularization","volume":"86","author":"Save","year":"2012","journal-title":"J. Geodesy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Swenson, S.C., and Wahr, J. (2006). Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett., 33.","DOI":"10.1029\/2005GL025285"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s00190-009-0327-0","article-title":"On the postprocessing removal of correlated errors in GRACE temporal gravity field solutions","volume":"83","author":"Duan","year":"2009","journal-title":"J. Geodesy."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Z.-Z., Chao, B.F., Lu, Y., and Hsu, H.-T. (2009). An effective filtering for GRACE time-variable gravity: Fan filter. Geophys. Res. Lett., 36.","DOI":"10.1029\/2009GL039459"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1093\/gji\/ggaa104","article-title":"A least-squares method for estimating the correlated error of GRACE models","volume":"221","author":"Crowley","year":"2020","journal-title":"Geophys. J. Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2034","DOI":"10.1093\/gji\/ggz409","article-title":"Data-adaptive spatio-temporal filtering of GRACE data","volume":"219","author":"Prevost","year":"2019","journal-title":"Geophys. J. Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.jog.2018.05.003","article-title":"Estimation and reduction of random noise in mass anomaly time-series from satellite gravity data by minimization of month-to-month year-to-year double differences","volume":"119","author":"Ditmar","year":"2018","journal-title":"J. Geodyn."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1093\/gji\/ggy272","article-title":"Identifying presence of correlated errors using machine learning algorithms for the selective de-correlation of GRACE harmonic coefficients","volume":"215","author":"Piretzidis","year":"2018","journal-title":"Geophys. J. Int."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1093\/gji\/ggu386","article-title":"On the decorrelation filtering of RL05 GRACE data for global applications","volume":"200","author":"Belda","year":"2015","journal-title":"Geophys. J. Int."},{"key":"ref_16","first-page":"1135","article-title":"Removing correlative errors in GRACE data by the smoothness priors method","volume":"58","author":"Zhan","year":"2015","journal-title":"Chin. J. Geophys. -Chin. Ed."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1093\/gji\/ggaa339","article-title":"Improved multichannel singular spectrum analysis for post-processing GRACE monthly gravity field models","volume":"223","author":"Wang","year":"2020","journal-title":"Geophys. J. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5578","DOI":"10.1002\/2017JB014379","article-title":"Correlated error reduction in GRACE data over Greenland using extended empirical orthogonal functions","volume":"122","author":"Eom","year":"2017","journal-title":"J. Geophys. Res. -Solid Earth."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1007\/s11200-006-0031-y","article-title":"Wiener optimal filtering of GRACE data","volume":"50","author":"Sasgen","year":"2006","journal-title":"Studia Geophys. Geod."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Society. Ser. B Methodol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations, CRC Press.","DOI":"10.1201\/b18401"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_23","unstructured":"Burnham, K.P., and Anderson, D.R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"109209","DOI":"10.1016\/j.measurement.2021.109209","article-title":"Optimal filtering for state space model with time-integral measurements","volume":"176","author":"Qian","year":"2021","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1061\/(ASCE)0733-9453(2003)129:1(37)","article-title":"Formulation of L1 Norm Minimization in Gauss-Markov Models","volume":"129","year":"2003","journal-title":"J. Surv. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1137\/080716542","article-title":"A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems","volume":"2","author":"Beck","year":"2009","journal-title":"Siam J. Imaging Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1418","DOI":"10.1198\/016214506000000735","article-title":"The adaptive Lasso and its oracle properties","volume":"101","author":"Zou","year":"2006","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107803","DOI":"10.1016\/j.measurement.2020.107803","article-title":"Precise instantaneous velocimetry and accelerometry with a stand-alone GNSS receiver based on sparse kernel learning","volume":"159","author":"Chang","year":"2020","journal-title":"Measurement"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3597","DOI":"10.1007\/s11071-020-05698-0","article-title":"Smoothing for continuous dynamical state space models with sampled system coefficients based on sparse kernel learning","volume":"100","author":"Qian","year":"2020","journal-title":"Nonlinear Dyn."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1007\/s10291-020-01061-3","article-title":"Improved atmospheric weighted mean temperature modeling using sparse kernel learning","volume":"25","author":"Yang","year":"2021","journal-title":"GPS Solut."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1561\/2400000003","article-title":"Proximal algorithms","volume":"1","author":"Parikh","year":"2014","journal-title":"Found. Trends Optim."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/00401706.1979.10489751","article-title":"Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter","volume":"21","author":"Golub","year":"1979","journal-title":"Technometrics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/BF01404567","article-title":"Smoothing noisy data with spline functions","volume":"31","author":"Craven","year":"1979","journal-title":"Numer. Math."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2173","DOI":"10.1214\/009053607000000127","article-title":"On the \u201cdegrees of freedom\u201d of the lasso","volume":"35","author":"Zou","year":"2007","journal-title":"Ann. Stat."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s00190-002-0302-5","article-title":"A Monte-Carlo technique for weight estimation in satellite geodesy","volume":"76","author":"Kusche","year":"2003","journal-title":"J. Geod."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1007\/s00190-002-0245-x","article-title":"Regularization of geopotential determination from satellite data by variance components","volume":"76","author":"Koch","year":"2002","journal-title":"J. Geod."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00190-006-0025-0","article-title":"Multiple Parameter Regularization: Numerical Solutions and Applications to the Determination of Geopotential from Precise Satellite Orbits","volume":"80","author":"Xu","year":"2006","journal-title":"J. Geod."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4190","DOI":"10.1109\/TVT.2021.3076056","article-title":"Vehicle\u2019s Instantaneous Velocity Reconstruction by Combining GNSS Doppler and Carrier Phase Measurements Through Tikhonov Regularized Kernel Learning","volume":"70","author":"Qian","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/00401706.1995.10484371","article-title":"Better subset regression using the nonnegative garrote","volume":"37","author":"Breiman","year":"1995","journal-title":"Technometrics"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","article-title":"A new look at the statistical model identification","volume":"19","author":"Akaike","year":"1974","journal-title":"IEEE Trans. Autom. Control."},{"key":"ref_41","unstructured":"Tikhonov, A.N., and Arsenin, V.Y. (1977). Solutions of Ill-Posed Problems, John Wiley & Sons."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/cpa.20132","article-title":"For most large underdetermined systems of linear equations the minimal L1-norm solution is also the sparsest solution","volume":"59","author":"Donoho","year":"2006","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)SU.1943-5428.0000031","article-title":"Recursive algorithm for L1 norm estimation in linear models","volume":"137","author":"Khodabandeh","year":"2011","journal-title":"J. Surv. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7547","DOI":"10.1002\/2016JB013007","article-title":"High-resolution CSR GRACE RL05 mascons","volume":"121","author":"Save","year":"2016","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1002\/2014JB011547","article-title":"Improved methods for observing Earth\u2019s time variable mass distribution with GRACE using spherical cap mascons","volume":"120","author":"Watkins","year":"2015","journal-title":"J. Geophys. Res.-Solid Earth"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1002\/jgrb.50058","article-title":"Deceleration in the Earth\u2019s oblateness","volume":"118","author":"Cheng","year":"2013","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"B8","DOI":"10.1029\/2007JB005338","article-title":"Estimating geocenter variations from a combination of GRACE and ocean model output","volume":"113","author":"Swenson","year":"2008","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1002\/2016JB013844","article-title":"Comment on \u201cAn Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model\u201d by Purcell et al","volume":"123","author":"Peltier","year":"2018","journal-title":"J. Geophys. Res.-Solid Earth"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"B12","DOI":"10.1029\/98JB02844","article-title":"Time variability of the Earth\u2019s gravity field: Hydrological and oceanic effects and their possible detection using GRACE","volume":"103","author":"Wahr","year":"1998","journal-title":"J. Geophy. Res.-Solid Earth"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Luthcke, S.B., Rowlands, D.D., Lemoine, F.G., Klosko, S.M., Chinn, D., and McCarthy, J.J. (2006). Monthly spherical harmonic gravity field solutions determined from GRACE inter-satellite range-rate data alone. Geophys. Res. Lett., 33.","DOI":"10.1029\/2005GL024846"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1007\/s00190-016-0889-6","article-title":"An improved GRACE monthly gravity field solution by modeling the non-conservative acceleration and attitude observation errors","volume":"90","author":"Chen","year":"2016","journal-title":"J. Geod."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1007\/s00190-018-1128-0","article-title":"Conversion of time-varying Stokes coefficients into mass anomalies at the Earth\u2019s surface considering the Earth\u2019s oblateness","volume":"92","author":"Ditmar","year":"2018","journal-title":"J. Geod."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ditmar, P. (2022). How to quantify the accuracy of mass anomaly time-series based on grace data in the absence of knolwedeg about true signal?. J. Geod., under review.","DOI":"10.1007\/s00190-022-01640-x"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1007\/s12145-018-0368-0","article-title":"GRAMAT: A comprehensive Matlab toolbox for estimating global mass variations from GRACE satellite data","volume":"12","author":"Feng","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1002\/2013EO450001","article-title":"Generic mapping tools: Improved version released","volume":"94","author":"Wessel","year":"2013","journal-title":"Eos Trans. Am. Geophys. Union"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2810\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:28:06Z","timestamp":1760138886000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/12\/2810"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,11]]},"references-count":55,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["rs14122810"],"URL":"https:\/\/doi.org\/10.3390\/rs14122810","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,11]]}}}