253 citations as recorded by crossref.

1. State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales T. Wang et al. https://doi.org/10.1016/j.ecolmodel.2012.07.017
2. Techniques for characterizing weak transients in cosmic ray records, as measured by neutron monitor networks T. de Wit Dudok et al. https://doi.org/10.2478/s11600-008-0038-6
3. Water vapor‐weighted mean temperature and its impact on the determination of precipitable water vapor and its linear trend X. Wang et al. https://doi.org/10.1002/2015JD024181
4. A Two-Step Method for Missing Spatio-Temporal Data Reconstruction S. Cheng & F. Lu https://doi.org/10.3390/ijgi6070187
5. Geometric Piecewise Cubic Bézier Interpolating Polynomial with C2 Continuity M. Fadhel & Z. Omar https://doi.org/10.15622/ia.2021.20.1.5
6. Variability modes of precipitation along a Central Mediterranean area and their relations with ENSO, NAO, and other climatic patterns A. Kalimeris et al. https://doi.org/10.1016/j.atmosres.2017.07.031
7. Conceptualization in catchment modelling: simply learning? S. Dunn et al. https://doi.org/10.1002/hyp.7070
8. Optimal frequency bands for pupillography for maximal correlation with HRV J. Medeiros et al. https://doi.org/10.1038/s41598-025-85663-2
9. Granger-causality inference in the presence of gaps: An equidistant missing-data problem for non-synchronous recorded time series data H. Elsegai https://doi.org/10.1016/j.physa.2019.01.002
10. Reconstructing multi-scale physical fields from extremely sparse measurements with an autoencoder-diffusion cascade L. Yi et al. https://doi.org/10.1016/j.jcp.2026.115214
11. An effective toolkit for the interpolation and gross error detection of GPS time series X. Wang et al. https://doi.org/10.1179/1752270615Y.0000000023
12. Joint short-term prediction of polar motion and length of day with multi-task deep learning methods S. Guessoum et al. https://doi.org/10.1186/s40623-025-02150-8
13. From interannual to decadal: 17 years of boundary current transports at the exit of the Labrador Sea R. Zantopp et al. https://doi.org/10.1002/2016JC012271
14. Singular Spectrum Analysis With Conditional Predictions for Real‐Time State Estimation and Forecasting H. Ogrosky et al. https://doi.org/10.1029/2018GL081100
15. Singular spectrum analysis of storm surges and wave climates R. Briganti & G. Beltrami https://doi.org/10.1080/00221686.2008.9521960
16. TubeDB: An on-demand processing database system for climate station data S. Wöllauer et al. https://doi.org/10.1016/j.cageo.2020.104641
17. Analysis of Surface Atrial Signals: Time Series with Missing Data? R. Sassi et al. https://doi.org/10.1007/s10439-009-9757-3
18. Investigation of time_changeable seasonal components in the GPS height time series: A case study for Central Europe M. Gruszczynska https://doi.org/10.13168/AGG.2016.0010
19. Differences in Energy Consumption in Electric Vehicles: An Exploratory Real-World Study in Beijing K. Hu et al. https://doi.org/10.1155/2017/4695975
20. Altered seasonal sensitivity of net ecosystem exchange to controls driven by nutrient balances in a semi-arid savanna L. Nadolski et al. https://doi.org/10.5194/bg-22-2935-2025
21. A Missing Type-Aware Adaptive Interpolation Framework for Sensor Data L. Chen et al. https://doi.org/10.1109/TIM.2021.3089783
22. Spatio-Temporal Missing Data Imputation: A Systematic Literature Review with a Focus on Statistical and Machine Learning-Based Approaches S. Zahmatkesh & P. Zech https://doi.org/10.1145/3797903
23. Surface Ozone-Induced Yield Losses and Economic Costs of Winter Wheat Across China: Spatiotemporal Estimates Based on Reconstructed M7 and AOT40 Exposures (2015–2022) H. Zheng et al. https://doi.org/10.3390/agronomy16171671
24. The “Caterpillar”-SSA method for analysis of time series with missing values N. Golyandina & E. Osipov https://doi.org/10.1016/j.jspi.2006.05.014
25. Technical Note: Comparing the effectiveness of recent algorithms to fill and smooth incomplete and noisy time series J. Musial et al. https://doi.org/10.5194/acp-11-7905-2011
26. Estimation of the Cyclopean Eye From Binocular Smooth Pursuit Tests E. Luque-Buzo et al. https://doi.org/10.1109/TCDS.2024.3410110
27. Reconstruction of Zonal Precipitation From Sparse Historical Observations Using Climate Model Information and Statistical Learning M. Egli et al. https://doi.org/10.1029/2022GL099826
28. Analyzing state-dependent model–data comparison in multi-regime systems A. Aretxabaleta & K. Smith https://doi.org/10.1007/s10596-011-9229-3
29. SPACE-TA L. Wang et al. https://doi.org/10.1145/3131671
30. A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data J. Fu et al. https://doi.org/10.3390/en6062804
31. An extended approach for spatiotemporal gapfilling: dealing with large and systematic gaps in geoscientific datasets J. v. Buttlar et al. https://doi.org/10.5194/npg-21-203-2014
32. Realization of an Optimal Dynamic Geodetic Reference Frame in China: Methodology and Applications P. Cheng et al. https://doi.org/10.1016/j.eng.2020.08.004
33. Multi-regime non-Gaussian data filling for incomplete ocean datasets A. Aretxabaleta & K. Smith https://doi.org/10.1016/j.jmarsys.2013.03.001
34. Estimation of connectivity measures in gappy time series G. Papadopoulos & D. Kugiumtzis https://doi.org/10.1016/j.physa.2015.05.032
35. Climate Change and the Macroeconomic Structure in Pre-Industrial Europe: New Evidence from Wavelet Analysis Q. Pei et al. https://doi.org/10.1371/journal.pone.0126480
36. Low‐Rank Gap Filling and Downscaling for SMAP Soil Moisture Datasets K. Beale et al. https://doi.org/10.1002/eco.70024
37. Gap Filling and Anomaly Separation Using Inverse Principal Components Analysis Q. Li & X. Liu https://doi.org/10.1007/s00024-026-04053-5
38. Learning spatiotemporal dynamics with a pretrained generative model Z. Li et al. https://doi.org/10.1038/s42256-024-00938-z
39. Modeling composite effects of marine and freshwater processes on migratory species W. Friedman et al. https://doi.org/10.1002/ecs2.2743
40. Earth orientation parameters prediction based on the hybrid SSA + LS + SVM model Y. Yang et al. https://doi.org/10.1088/1361-6501/ac8ec6
41. Missing values imputation in ocean buoy time series data S. Chakraborty et al. https://doi.org/10.1016/j.oceaneng.2024.120145
42. Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods J. Zhao et al. https://doi.org/10.1016/j.agrformet.2020.107912
43. Research on Groundwater Drought and Sustainability in Badain Jaran Desert and Surrounding Areas Based on GRACE Satellite X. Liu et al. https://doi.org/10.3390/land14010173
44. Multichannel singular spectrum analysis (M-SSA) of InSAR data sets: data-adaptive interpolation and decomposition of Sentinel-1 time-series at Pacaya Volcano, Guatemala D. Walwer et al. https://doi.org/10.1093/gji/ggaf257
45. What we talk about when we talk about seasonality – A transdisciplinary review O. Kwiecien et al. https://doi.org/10.1016/j.earscirev.2021.103843
46. On the application of Monte Carlo singular spectrum analysis to GPS position time series S. Khazraei & A. Amiri-Simkooei https://doi.org/10.1007/s00190-019-01253-x
47. Iteration empirical mode decomposition method for filling the missing data of GNSS position time series X. Qiu et al. https://doi.org/10.13168/AGG.2022.0012
48. Temporal and Spatial Variation Pattern of Groundwater Storage and Response to Environmental Changes in Shandong Province Y. Bi & X. Tan https://doi.org/10.3390/w18020189
49. An Improved Data Interpolating Empirical Orthogonal Function Method for Data Reconstruction: A Case Study of the Chlorophyll-a Concentration in the Bohai Sea, China T. Hong et al. https://doi.org/10.3390/app14072803
50. Tracking the US business cycle with a singular spectrum analysis M. de Carvalho et al. https://doi.org/10.1016/j.econlet.2011.09.007
51. Approach to optimize 3-dimensional brain functional activation image with high resolution: a study on functional near-infrared spectroscopy Z. Shoaib et al. https://doi.org/10.1364/BOE.10.004684
52. How Well Do AR5 Sea Surface-Height Model Projections Match Observational Rates of Sea-Level Rise at the Regional Scale? P. Watson https://doi.org/10.3390/jmse6010011
53. Hydrological variability and loading deformation in the Yangtze river basin based on modern geodetic means D. Ren et al. https://doi.org/10.1080/27669645.2022.2098895
54. A New Highly Parameterized Linear Inversion of Water Table Change and Groundwater Depletion Rate Tested With the High Plains Aquifer, U.S.A. J. Jiao et al. https://doi.org/10.1029/2022WR034342
55. A MATLAB-based Kriged Kalman Filter software for interpolating missing data in GNSS coordinate time series N. Liu et al. https://doi.org/10.1007/s10291-017-0689-3
56. The Interannual Fluctuations in Mass Changes and Hydrological Elasticity on the Tibetan Plateau from Geodetic Measurements M. He et al. https://doi.org/10.3390/rs13214277
57. Timescale effects on the environmental control of carbon and water fluxes of an apple orchard L. Montagnani et al. https://doi.org/10.1002/ece3.3633
58. Comparing observations and process‐based simulations of biosphere‐atmosphere exchanges on multiple timescales M. Mahecha et al. https://doi.org/10.1029/2009JG001016
59. A combined EOF/variational approach for mapping radar-derived sea surface currents M. Yaremchuk & A. Sentchev https://doi.org/10.1016/j.csr.2011.01.009
60. Extreme Value Analysis of Tide Gauge Record at the Port of Busan, South Korea P. Watson & H. Lim https://doi.org/10.3390/geohazards4040028
61. Missing value imputation in time series using Singular Spectrum Analysis R. Mahmoudvand & P. Rodrigues https://doi.org/10.1142/S2335680416500058
62. A Modified Spatiotemporal Mixed-Effects Model for Interpolating Missing Values in Spatiotemporal Observation Data Series Q. Shi et al. https://doi.org/10.1155/2020/1070831
63. Restoration of long-term missing gaps in solar radiation O. Ogunsola & L. Song https://doi.org/10.1016/j.enbuild.2014.07.088
64. An evaluation of monthly impervious surface dynamics by fusing Landsat and MODIS time series in the Pearl River Delta, China, from 2000 to 2015 L. Zhang et al. https://doi.org/10.1016/j.rse.2017.08.036
65. Comparison of Harmonic Analysis of Time Series (HANTS) and Multi-Singular Spectrum Analysis (M-SSA) in Reconstruction of Long-Gap Missing Data in NDVI Time Series H. Ghafarian Malamiri et al. https://doi.org/10.3390/rs12172747
66. Acceleration in U.S. Mean Sea Level? A New Insight using Improved Tools P. Watson https://doi.org/10.2112/JCOASTRES-D-16-00086.1
67. Data-adaptive spatio-temporal filtering of GRACE data P. Prevost et al. https://doi.org/10.1093/gji/ggz409
68. The operational method of filling information gaps in satellite imagery using numerical models M. Konik et al. https://doi.org/10.1016/j.jag.2018.09.002
69. Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach C. Beneki et al. https://doi.org/10.1002/for.1220
70. Analysis of linear and bilinear spatial temporal models in the case of missing observations M. Gabr et al. https://doi.org/10.1080/03610918.2023.2290460
71. Filling the gap in the IERS C01 polar motion series in 1858.9–1860.9 Z. Malkin et al. https://doi.org/10.1007/s00190-025-01978-y
72. Diagnosing the Temperature Sensitivity of Ecosystem Respiration in Northern High‐Latitude Regions D. Wu et al. https://doi.org/10.1029/2020JG005998
73. Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics Z. Baldysz et al. https://doi.org/10.3390/atmos12121698
74. Finding the best proxies for the solar UV irradiance T. Dudok de Wit et al. https://doi.org/10.1029/2009GL037825
75. Reconstruction of Helio-Latitudinal Structure of the Solar Wind Proton Speed and Density J. Sokół et al. https://doi.org/10.1007/s11207-015-0800-2
76. Application of Empirical Orthogonal Function Interpolation to Reconstruct Hourly Fine Particulate Matter Concentration Data in Tianjin, China H. Zhou et al. https://doi.org/10.1155/2020/9724367
77. Reconstruction of gappy GPS coordinate time series using empirical orthogonal functions C. Xu https://doi.org/10.1002/2016JB013188
78. GRACE-derived groundwater variability and its resilience in north India: impact of climatic and socioeconomic factors S. Rana & A. Chamoli https://doi.org/10.1080/02626667.2024.2403720
79. A review of empirical orthogonal function (EOF) with an emphasis on the co-seismic crustal deformation analysis . Neha & S. Pasari https://doi.org/10.1007/s11069-021-04967-4
80. A review on missing hydrological data processing Y. Gao et al. https://doi.org/10.1007/s12665-018-7228-6
81. Multivariate reconstruction of missing data in sea surface temperature, chlorophyll, and wind satellite fields A. Alvera‐Azcárate et al. https://doi.org/10.1029/2006JC003660
82. Feature-preserving interpolation and filtering of environmental time series G. Mariethoz et al. https://doi.org/10.1016/j.envsoft.2015.07.001
83. Spatio-temporal MODIS EVI gap filling under cloud cover: An example in Scotland L. Poggio et al. https://doi.org/10.1016/j.isprsjprs.2012.06.003
84. Adaptive dynamic-variational wavelet-based compressed sensing: Advanced methods for sea water level data imputation A. Altunkaynak et al. https://doi.org/10.1016/j.rsma.2026.105098
85. Deducing Energy Consumer Behavior from Smart Meter Data E. Ebeid et al. https://doi.org/10.3390/fi9030029
86. EM-EOF: Gap-Filling in Incomplete SAR Displacement Time Series A. Hippert-Ferrer et al. https://doi.org/10.1109/TGRS.2020.3015087
87. Spatial Gap-Filling of ESA CCI Satellite-Derived Soil Moisture Based on Geostatistical Techniques and Multiple Regression R. Llamas et al. https://doi.org/10.3390/rs12040665
88. Towards a zero-difference approach for homogenizing GNSS tropospheric products M. Hoseini et al. https://doi.org/10.1007/s10291-019-0915-2
89. Filtering Unevenly Spaced Geophysical Time Series as an Ill-Posed Problem K. Ji et al. https://doi.org/10.1007/s10712-025-09924-5
90. Climate reconstruction for the Entre‐Douro‐e‐Minho region (NWPortugal) betweenAD1626 andAD1820: synthesis of viticulture data and foraminiferal evidence J. Moreno et al. https://doi.org/10.1111/bor.12331
91. EdgeSense: Edge-Mediated Spatial- Temporal Crowdsensing S. Yang et al. https://doi.org/10.1109/ACCESS.2018.2870298
92. Sparse Mobile Crowdsensing With Differential and Distortion Location Privacy L. Wang et al. https://doi.org/10.1109/TIFS.2020.2975925
93. Revitalizing temperature records: A novel framework towards continuous data reconstruction using univariate and multivariate imputation techniques H. Yashas Kumar & K. Varija https://doi.org/10.1016/j.atmosres.2024.107754
94. Is chlorophyll‐a the best surrogate for organic matter enrichment in submicron primary marine aerosol? M. Rinaldi et al. https://doi.org/10.1002/jgrd.50417
95. Crustal movement derived by GNSS technique considering common mode error with MSSA M. Zhou et al. https://doi.org/10.1016/j.asr.2020.06.018
96. Recurrence‐Based Quantification of Dynamical Complexity in the Earth's Magnetosphere at Geospace Storm Timescales R. Donner et al. https://doi.org/10.1029/2018JA025318
97. Filling missing values of multi-station GNSS coordinate time series based on matrix completion Z. Bao et al. https://doi.org/10.1016/j.measurement.2021.109862
98. Investigating the temporal patterns of terrestrial water storage and the climatic drivers in the Tianshan mountains and their surrounding regions J. Liu et al. https://doi.org/10.1016/j.geog.2026.05.007
99. Ultra‐Oligotrophic Waters Expansion in the North Atlantic Subtropical Gyre Revealed by 21 Years of Satellite Observations F. Leonelli et al. https://doi.org/10.1029/2021GL096965
100. An efficient improved singular spectrum analysis for processing GNSS position time series with missing data K. Ji et al. https://doi.org/10.1093/gji/ggae381
101. Hourly Regional Precipitation Forecasting Using GNSS PWV and the ConvLSTM-ED Algorithm Y. Liu et al. https://doi.org/10.3390/rs18213716
102. Performance evaluation of image processing algorithms for eye blinking detection F. Attivissimo et al. https://doi.org/10.1016/j.measurement.2023.113767
103. Determining Extreme Still Water Levels for Design and Planning Purposes Incorporating Sea Level Rise: Sydney, Australia P. Watson https://doi.org/10.3390/atmos13010095
104. A novel framework for filling data gaps in groundwater level observations P. Oikonomou et al. https://doi.org/10.1016/j.advwatres.2018.06.008
105. Hydrological drought evaluation using GRACE satellite-based drought index over the lake basins, East Africa A. Seka et al. https://doi.org/10.1016/j.scitotenv.2022.158425
106. Improved singular spectrum analysis for time series with missing data Y. Shen et al. https://doi.org/10.5194/npg-22-371-2015
107. Big Data's Dirty Secret H. Stein & Y. Zhang https://doi.org/10.2139/ssrn.3205524
108. Grouping in Singular Spectrum Analysis of Time Series P. Unnikrishnan & V. Jothiprakash https://doi.org/10.1061/(ASCE)HE.1943-5584.0002198
109. Reconstructing the data gap between GRACE and GRACE follow-on at the basin scale using artificial neural network Y. Lai et al. https://doi.org/10.1016/j.scitotenv.2022.153770
110. Selection of window length for singular spectrum analysis R. Wang et al. https://doi.org/10.1016/j.jfranklin.2015.01.011
111. Filling gaps and extending inland tide gauge records to support accurate long-term and extreme water level analyses P. Matte & S. Innocenti https://doi.org/10.1016/j.csr.2026.105668
112. Biophysical Controls of Ecosystem‐Scale Methane Fluxes From a Subtropical Estuarine Mangrove: Multiscale, Nonlinearity, Asynchrony and Causality J. Liu et al. https://doi.org/10.1029/2021GB007179
113. Intrinsic vs. spurious long-range memory in high-frequency records of environmental radioactivity R. Donner et al. https://doi.org/10.1140/epjst/e2015-02404-1
114. Reconstruction of Time Series Data of Environmental Parameters: Methods and Application D. JIANG et al. https://doi.org/10.3724/SP.J.1047.2011.00439
115. Identifying multiple spatiotemporal patterns: A refined view on terrestrial photosynthetic activity M. Mahecha et al. https://doi.org/10.1016/j.patrec.2010.06.021
116. AFCS: Aggregation-Free Spatial-Temporal Mobile Community Sensing J. Bian et al. https://doi.org/10.1109/TMC.2022.3178885
117. Hydroclimatic variability of opposing Late Pleistocene climates in the Levant revealed by deep Dead Sea sediments Y. Ben Dor et al. https://doi.org/10.5194/cp-17-2653-2021
118. Discerning dominant temporal patterns of bio-optical properties in the northwestern Mediterranean Sea (BOUSSOLE site) M. Bellacicco et al. https://doi.org/10.1016/j.dsr.2019.04.006
119. Integrating MODIS and CYCLOPES Leaf Area Index Products Using Empirical Orthogonal Functions D. Wang & S. Liang https://doi.org/10.1109/TGRS.2010.2086463
120. An enhanced singular spectrum analysis method for constructing nonsecular model of GPS site movement X. Wang et al. https://doi.org/10.1002/2015JB012573
121. Acceleration in European Mean Sea Level? A New Insight Using Improved Tools P. Watson https://doi.org/10.2112/JCOASTRES-D-16-00134.1
122. Which solar EUV indices are best for reconstructing the solar EUV irradiance? T. Dudok de Wit et al. https://doi.org/10.1016/j.asr.2007.04.019
123. Missing data imputation for an ocean research station based on multi-layer perceptron neural network N. Kim et al. https://doi.org/10.1016/j.apor.2026.104956
124. Enhancing Soil Moisture Prediction in Drought-Prone Agricultural Regions Using Remote Sensing and Machine Learning Approaches X. Zha et al. https://doi.org/10.3390/rs17020181
125. A Multivariate Empirical Orthogonal Function Method to Construct Nitrate Maps in the Southern Ocean Y. Liang et al. https://doi.org/10.1175/JTECH-D-18-0018.1
126. Interpolation in Time Series: An Introductive Overview of Existing Methods, Their Performance Criteria and Uncertainty Assessment M. Lepot et al. https://doi.org/10.3390/w9100796
127. Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8 X. Ma et al. https://doi.org/10.3390/rs12081339
128. Application of a novel hybrid method for spatiotemporal data imputation: A case study of the Minqin County groundwater level Z. Zhang et al. https://doi.org/10.1016/j.jhydrol.2017.07.053
129. Missing Values in Time Series: A Brief Review and a New Versatile Imputation Method S. Huang et al. https://doi.org/10.1111/insr.70049
130. The application of the transfer entropy to gappy time series C. Kulp & E. Tracy https://doi.org/10.1016/j.physleta.2009.02.009
131. The oceanic physical injection pump of organic carbon M. Bellacicco et al. https://doi.org/10.1038/s41467-025-62363-z
132. Evolution of water collection, storage, and supply functions and their coupling relationships in the Asian water tower from 2003 to 2023 based on GRACE and multi-source data H. Tang et al. https://doi.org/10.1016/j.ejrh.2026.104168
133. Reconstruction of gaps in the past history of solar wind parameters D. Kondrashov et al. https://doi.org/10.1002/2014GL059741
134. Effect of data gaps: comparison of different spectral analysis methods C. Munteanu et al. https://doi.org/10.5194/angeo-34-437-2016
135. Analysis of the Spatial Distribution and Common Mode Error Correlation in a Small-Scale GNSS Network A. Li et al. https://doi.org/10.3390/s24175731
136. Data Spike Cleaning with MSSA J. Dash & Y. Zhang https://doi.org/10.2139/ssrn.2811696
137. Interannual hydrological variability in the Mississippi River Basin based on contemporary geodetic measurements and land surface models D. Ren et al. https://doi.org/10.1016/j.jhydrol.2023.130232
138. Improved multichannel singular spectrum analysis for post-processing GRACE monthly gravity field models F. Wang et al. https://doi.org/10.1093/gji/ggaa339
139. Temporal patterns of water quality in the Pantanal floodplain and its contributing Cerrado upland rivers: implications for the interpretation of freshwater integrity P. Zeilhofer et al. https://doi.org/10.1007/s11273-016-9497-8
140. A Cloud Framework for High Spatial Resolution Soil Moisture Mapping from Radar and Optical Satellite Imageries T. Guo et al. https://doi.org/10.1007/s11769-023-1365-x
141. Summertime Primary and Secondary Contributions to Southern Ocean Cloud Condensation Nuclei K. Fossum et al. https://doi.org/10.1038/s41598-018-32047-4
142. An empirical stochastic model of sea-surface temperatures and surface winds over the Southern Ocean S. Kravtsov et al. https://doi.org/10.5194/os-7-755-2011
143. Updated Mean Sea-Level Analysis: South Korea P. Watson https://doi.org/10.2112/JCOASTRES-D-18-00138.1
144. Analyzing Error Bounds for Seasonal-Trend Decomposition of Antarctica Temperature Time Series Involving Missing Data C. Kwok et al. https://doi.org/10.3390/atmos14020193
145. Nuclear norm minimization for gap-filling GNSS precipitable water vapor C. Geng et al. https://doi.org/10.1088/1361-6501/ae83d6
146. Characterizing ecosystem-atmosphere interactions from short to interannual time scales M. Mahecha et al. https://doi.org/10.5194/bg-4-743-2007
147. Long-term prediction of polar motion using a combined SSA and ARMA model Y. Shen et al. https://doi.org/10.1007/s00190-017-1065-3
148. Uncertainty Assessments of Load Deformation from Different GPS Time Series Products, GRACE Estimates and Model Predictions: A Case Study over Europe S. Wang et al. https://doi.org/10.3390/rs13142765
149. Unveiling the deep ocean warming: observed bottom ocean dataset across Mediterranean Sea N. Lo Bue et al. https://doi.org/10.5194/essd-18-5093-2026
150. Gap Filling of Precipitation Data by SSA - Singular Spectrum Analysis A. Filho & G. Lima https://doi.org/10.1088/1742-6596/759/1/012085
151. Foraminiferal evidence of major environmental changes driven by the sun-climate coupling in the western Portuguese coast (14th century to present) J. Moreno et al. https://doi.org/10.1016/j.ecss.2018.11.030
152. Time Series Imputation via L1 Norm-Based Singular Spectrum Analysis M. Kalantari et al. https://doi.org/10.1142/S0219477518500177
153. Filling the Data Gaps Within GRACE Missions Using Singular Spectrum Analysis S. Yi & N. Sneeuw https://doi.org/10.1029/2020JB021227
154. Singular spectral and control chart analysis of soil radon and thoron time series for forecasting seismic activities A. Rasheed et al. https://doi.org/10.1016/j.jastp.2023.106108
155. Simulating the Effect of Climate Extremes on Groundwater Flow Through a Lakebed M. Virdi et al. https://doi.org/10.1111/j.1745-6584.2012.00969.x
156. Reconstruction of geodetic time series with missing data and time-varying seasonal signals using Gaussian process for machine learning K. Xu et al. https://doi.org/10.1007/s10291-024-01616-8
157. An operational approach to forecast the Earth’s radiation belts dynamics G. Bernoux et al. https://doi.org/10.1051/swsc/2021045
158. An Update on the Status of Mean Sea Level Rise around the Korean Peninsula P. Watson & H. Lim https://doi.org/10.3390/atmos11111153
159. Inter-comparisons of Spatially Interpolated Short-term and Long-term PM2.5 Concentrations of Local Authorities in South Korea 2015~2017 K. Son et al. https://doi.org/10.5572/KOSAE.2020.36.2.185
160. Meteorological Time Series Interpolation: A Literature Review of Classical, Machine Learning and Deep Learning Approaches V. Landassuri Moreno et al. https://doi.org/10.61467/2007.1558.2027.v18i1.1516
161. A method for filling gaps in solar irradiance and solar proxy data T. Dudok de Wit https://doi.org/10.1051/0004-6361/201117024
162. Interpolation of Missing Values in AMSR-E Soil Moisture Data Using Modified SSA A. Turlapaty et al. https://doi.org/10.1109/LGRS.2010.2071852
163. Middle atmosphere response to the solar cycle in irradiance and ionizing particle precipitation K. Semeniuk et al. https://doi.org/10.5194/acp-11-5045-2011
164. Spectral modeling of time series with missing data P. Rodrigues & M. de Carvalho https://doi.org/10.1016/j.apm.2012.09.040
165. A new hybrid model for filling gaps and forecast in sea level: application to the eastern English Channel and the North Atlantic Sea (western France) I. Turki et al. https://doi.org/10.1007/s10236-015-0824-z
166. Updated Mean Sea-Level Analysis: Australia P. Watson https://doi.org/10.2112/JCOASTRES-D-20-00026.1
167. Spatiotemporal Filling of Missing Data in Remotely Sensed Displacement Measurement Time Series A. Hippert-Ferrer et al. https://doi.org/10.1109/LGRS.2020.3015149
168. 2D singular spectrum analysis for hydrological data processing R. Korotchenko & A. Kosheleva https://doi.org/10.1016/j.rsma.2020.101347
169. Data reconstruction for complex flows using AI: Recent progress, obstacles, and perspectives M. Buzzicotti https://doi.org/10.1209/0295-5075/acc88c
170. Reconstruction of sea surface temperature by means of DINEOF: a case study during the fishing season in the Bay of Biscay U. Ganzedo et al. https://doi.org/10.1080/01431160903491420
171. Nonlinear analysis of groundwater levels: investigating trends and the impact of El Niño on groundwater drought in a southern region of India K. Poojitha & B. Dodamani https://doi.org/10.1007/s11600-025-01582-w
172. A three-dimensional gap filling method for large geophysical datasets: Application to global satellite soil moisture observations G. Wang et al. https://doi.org/10.1016/j.envsoft.2011.10.015
173. Measuring inequality persistence in OECD 1963–2008 using fractional integration and cointegration L. Gil-Alana et al. https://doi.org/10.1016/j.qref.2018.12.006
174. Data-driven reconstruction of nonlinear dynamics from sparse observation K. Yeo https://doi.org/10.1016/j.jcp.2019.06.039
175. FOCCA: Fog–cloud continuum architecture for data imputation and load balancing in Smart Grids M. Barbosa et al. https://doi.org/10.1016/j.comnet.2024.111031
176. Analysis of fingerprint-derived geocentre motion time-series using multichannel singular spectrum analysis H. Yu et al. https://doi.org/10.1093/gji/ggaf310
177. Modeling the propagation characteristics of chorus using CRRES suprathermal electron fluxes J. Bortnik et al. https://doi.org/10.1029/2006JA012237
178. Orbital cycles in a Late Cretaceous shallow platform (Iberian Ranges, Spain) J. Gil et al. https://doi.org/10.1016/j.palaeo.2008.12.017
179. Gappy Spectral Proper Orthogonal Decomposition A. Nekkanti & O. Schmidt https://doi.org/10.2139/ssrn.4159544
180. A forecasting algorithm for Singular Spectrum Analysis based on bootstrap Linear Recurrent Formula coefficients D. Rahmani https://doi.org/10.1142/S2335680414500197
181. Addressing local sparsity in massive agricultural machinery trajectories: A BiLSTM-GRU approach W. Zhai et al. https://doi.org/10.1016/j.compag.2024.109376
182. Evidence against a long-term control on Earth climate by Galactic Cosmic Ray Flux L. Lanci et al. https://doi.org/10.1016/j.gloplacha.2019.103095
183. Transient Variations in Glacial Mass Near Upernavik Isstrøm (West Greenland) Detected by the Combined Use of GPS and GRACE Data B. Zhang et al. https://doi.org/10.1002/2017JB014529
184. Bridging the data gap between GRACE and GRACE-FO using artificial neural network in Greenland B. Zhang et al. https://doi.org/10.1016/j.jhydrol.2022.127614
185. Global modelling of direct and indirect effects of sea spray aerosol using a source function encapsulating wave state A. Partanen et al. https://doi.org/10.5194/acp-14-11731-2014
186. Conditioned empirical orthogonal functions for interpolation of runoff time series along rivers: Application to reconstruction of missing monthly records L. Li et al. https://doi.org/10.1016/j.jhydrol.2017.11.014
187. Evaluating the future total water storage change and hydrological drought under climate change over lake basins, East Africa A. Seka et al. https://doi.org/10.1016/j.jclepro.2024.141552
188. Singular spectrum-based matrix completion for time series recovery and prediction G. Tsagkatakis et al. https://doi.org/10.1186/s13634-016-0360-0
189. Fault-related fold kinematics recorded by terrestrial growth strata, Sant Llorenç de Morunys, Pyrenees Mountains, NE Spain J. Carrigan et al. https://doi.org/10.1016/j.jsg.2016.09.003
190. Spatiotemporal Responses of Global Vegetation Growth to Terrestrial Water Storage C. Wang et al. https://doi.org/10.3390/rs17101701
191. Numerical Simulation and Forecasting of Water Level for Qinghai Lake Using Multi-Altimeter Data Between 2002 and 2012 J. Liao et al. https://doi.org/10.1109/JSTARS.2013.2291516
192. A physics‐informed Bayesian framework for characterizing ground motion process in the presence of missing data Y. Chen et al. https://doi.org/10.1002/eqe.3877
193. Application of singular spectrum analysis in reconstruction of the annual signal from GRACE C. Zhu et al. https://doi.org/10.1515/jag-2020-0005
194. 3D seismic data de-noising and reconstruction using Multichannel Time Slice Singular Spectrum Analysis R. Rekapalli et al. https://doi.org/10.1016/j.jappgeo.2017.04.001
195. Downscaling climate variability associated with quasi-periodic climate signals: A new statistical approach using MSSA J. Cañón et al. https://doi.org/10.1016/j.jhydrol.2010.12.010
196. Revealing the spatiotemporal patterns of water vapor and its link to North Atlantic Oscillation over Greenland using GPS and ERA5 data Y. Liu et al. https://doi.org/10.1016/j.scitotenv.2024.170596
197. Singular Spectrum Analytic (SSA) Decomposition and Reconstruction of Flowering: Signatures of Climatic Impacts I. Hudson & M. Keatley https://doi.org/10.1007/s10666-016-9516-4
198. Filling the gap of seismic ambient noise taken from the earth by modification of the frequency content of the existing time series M. Nameni et al. https://doi.org/10.1007/s12517-024-11863-1
199. Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations M. He et al. https://doi.org/10.3390/rs17152739
200. An analog period method for gap‐filling of latent heat flux measurements L. Hoeltgebaum et al. https://doi.org/10.1002/hyp.14105
201. Complex system approach to interpretation of monitoring time series: two case histories from NW Bohemia Z. Vařilová et al. https://doi.org/10.1007/s10346-010-0243-7
202. Principal components of short-term variability in the ultraviolet albedo of Venus P. Kopparla et al. https://doi.org/10.1051/0004-6361/201935388
203. Gravity tides extracted from SSA-denoised superconducting gravity data with the harmonic analysis: a case study at Wuhan station, China W. Gao et al. https://doi.org/10.1007/s40328-020-00310-8
204. Gappy spectral proper orthogonal decomposition A. Nekkanti & O. Schmidt https://doi.org/10.1016/j.jcp.2023.111950
205. Sensor noise suppression A. de Cheveigné & J. Simon https://doi.org/10.1016/j.jneumeth.2007.09.012
206. Atlantic Multidecadal Oscillation and Northern Hemisphere’s climate variability M. Wyatt et al. https://doi.org/10.1007/s00382-011-1071-8
207. Status of Mean Sea Level Rise around the USA (2020) P. Watson https://doi.org/10.3390/geohazards2020005
208. Singular spectrum analysis for heterogeneous time series by taking its formal errors into account F. Wang https://doi.org/10.13168/AGG.2018.0029
209. Particularities and commonalities of singular spectrum analysis as a method of time series analysis and signal processing N. Golyandina https://doi.org/10.1002/wics.1487
210. Data-driven modeling of spring discharge in a dynamic karst aquifer using multivariate models and spectral analysis G. Vivaldo et al. https://doi.org/10.1371/journal.pone.0351623
211. Gap filling in long-term AOD time series using Iterative Singular Spectrum Analysis at a tropical AERONET site T. Schmidt et al. https://doi.org/10.1007/s10661-026-16067-6
212. Assessing the Potential Benefits of the Geostationary Vantage Point for Generating Daily Chlorophyll-a Maps in the Baltic Sea M. Bellacicco et al. https://doi.org/10.3390/rs10121944
213. Infilling missing precipitation records – A comparison of a new copula-based method with other techniques A. Bárdossy & G. Pegram https://doi.org/10.1016/j.jhydrol.2014.08.025
214. Assessing coupling between lakes and layered aquifers in a complex Pleistocene landscape based on water level dynamics G. Lischeid et al. https://doi.org/10.1016/j.advwatres.2010.08.002
215. An effective approach for mapping permafrost in a large area using subregion maps and satellite data J. Hu et al. https://doi.org/10.1002/ppp.2068
216. CUTOFF: A spatio-temporal imputation method L. Feng et al. https://doi.org/10.1016/j.jhydrol.2014.11.012
217. High-resolution global groundwater storage anomalies: a 1 km downscaled dataset C. Zhang et al. https://doi.org/10.5194/essd-18-7593-2026
218. Reconstruction of groundwater levels to impute missing values using singular and multichannel spectrum analysis: application to the Ardabil Plain, Iran M. Taie Semiromi & M. Koch https://doi.org/10.1080/02626667.2019.1669793
219. An Innovative Method of Singular Spectrum Analysis to Conduct Gap-filling and Denoising on Time Series Data J. Yang & A. Buu https://doi.org/10.6339/25-JDS1164
220. Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks H. Ren et al. https://doi.org/10.5194/hess-26-1727-2022
221. Quantitatively Analyzing the Impacts of Seasonal Water Storage Changes in the Three Gorges Reservoir on Nearby Crust Y. Lai et al. https://doi.org/10.1007/s00024-021-02928-3
222. Improved interpolation method based on singular spectrum analysis iteration and its application to missing data recovery H. Wang et al. https://doi.org/10.1007/s10483-008-1010-x
223. Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram K. Hocke & N. Kämpfer https://doi.org/10.5194/acp-9-4197-2009
224. A novel spatial filter to reduce north–south striping noise in GRACE spherical harmonic coefficients S. Yi & N. Sneeuw https://doi.org/10.1007/s00190-022-01614-z
225. Regime Changes in Global Sea Surface Salinity Trend A. Aretxabaleta et al. https://doi.org/10.3390/jmse5040057
226. Robust Nonparametric Regression for Heavy-Tailed Data F. Gorji & M. Aminghafari https://doi.org/10.1007/s13253-019-00382-2
227. Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests M. Migliavacca et al. https://doi.org/10.1111/gcb.12671
228. Gap-filling snow-depth time-series with Kalman Filtering-Smoothing and Expectation Maximization: Proof of concept using spatially dense wireless-sensor-network data F. Avanzi et al. https://doi.org/10.1016/j.coldregions.2020.103066
229. Deep Learning for Imputation and Forecasting Tidal Level C. Yang et al. https://doi.org/10.1109/JOE.2021.3073931
230. A gap-filling algorithm selection strategy for GRACE and GRACE Follow-On time series based on hydrological signal characteristics of the individual river basins H. Karimi et al. https://doi.org/10.1515/jogs-2022-0129
231. Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA) H. Ghafarian Malamiri et al. https://doi.org/10.3390/atmos9090334
232. Revisiting the Intraseasonal Variability of Chlorophyll-a in the Adjacent Luzon Strait With a New Gap-Filled Remote Sensing Data Set T. Wang et al. https://doi.org/10.1109/TGRS.2021.3067646
233. On the imputation of missing data for road traffic forecasting: New insights and novel techniques I. Laña et al. https://doi.org/10.1016/j.trc.2018.02.021
234. A modeling study of ≥2 MeV electron fluxes in GEO at different prediction time scales based on LSTM and transformer networks X. Sun et al. https://doi.org/10.1051/swsc/2024021
235. A latent-coupled neural network for multiphysics long-term forecasting in reactor transients using sparse observations Y. Xu et al. https://doi.org/10.1016/j.engappai.2025.112496
236. Reanalyses of the radiation belt electron phase space density using nearly equatorial CRRES and polar‐orbiting Akebono satellite observations B. Ni et al. https://doi.org/10.1029/2008JA013933
237. Randomized singular spectrum analysis for long time series P. Rodrigues et al. https://doi.org/10.1080/00949655.2018.1462810
238. Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect X. Ma et al. https://doi.org/10.1016/j.rse.2013.07.030
239. Abrupt transition to irreversible damage in the overdrafted Sacramento Valley aquifer system S. Larochelle et al. https://doi.org/10.1073/pnas.2526041123
240. Gap filling of solar wind data by singular spectrum analysis D. Kondrashov et al. https://doi.org/10.1029/2010GL044138
241. A critical review of statistical, signal processing and machine learning methods for continuous and high-frequency water quality data improvement A. Badrudeen et al. https://doi.org/10.1016/j.ecoinf.2026.103619
242. Assessing the ability of MODIS EVI to estimate terrestrial ecosystem gross primary production of multiple land cover types H. Shi et al. https://doi.org/10.1016/j.ecolind.2016.08.022
243. Use of recent geoid models to estimate mean dynamic topography and geostrophic currents in South Atlantic and Brazil Malvinas confluence A. Lopes & J. Harari https://doi.org/10.1590/S1679-87592012000100005
244. Modelling the Abundances of Two Major Culicoides (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal M. Diarra et al. https://doi.org/10.1371/journal.pone.0131021
245. Speleothem stable isotope records for east-central Europe: resampling sedimentary proxy records to obtain evenly spaced time series with spectral guidance I. Hatvani et al. https://doi.org/10.5194/essd-10-139-2018
246. Technology and social equality in the United States M. Skare & M. Porada-Rochoń https://doi.org/10.1016/j.techfore.2022.121947
247. Harmonic cross-correlation decomposition for multivariate time series T. Zerenner et al. https://doi.org/10.1103/PhysRevE.103.062213
248. A 4‐year zonal climatology of lower tropospheric CO2 derived from ocean‐only Atmospheric Infrared Sounder observations L. Strow & S. Hannon https://doi.org/10.1029/2007JD009713
249. Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data H. Hassani et al. https://doi.org/10.3390/stats2040032
250. Extreme Value Analysis of Ocean Still Water Levels along the USA East Coast—Case Study (Key West, Florida) P. Watson https://doi.org/10.3390/coasts3040018
251. Glitches in time C. Haley https://doi.org/10.1038/532450a
252. Data‐Driven Gap Filling and Spatio‐Temporal Filtering of the GRACE and GRACE‐FO Records L. Gauer et al. https://doi.org/10.1029/2022JB025561
253. A review of uncertainty in in situ measurements and data sets of sea surface temperature J. Kennedy https://doi.org/10.1002/2013RG000434