Relevant bibliographies by topics / Data series gap-filling

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Data series gap-filling'

Author: Grafiati
Published: 25 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Data series gap-filling"

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Keysar, Ariela. "Filling a data gap: the American Religious Identification Survey (ARIS) series." Religion 44, no. 3 (2014): 383–95. http://dx.doi.org/10.1080/0048721x.2014.903648.

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Yu, Wentao, Jing Li, Qinhuo Liu, et al. "Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data." Remote Sensing 13, no. 3 (2021): 484. http://dx.doi.org/10.3390/rs13030484.

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High-quality Normalized Difference Vegetation Index (NDVI) time series are essential in studying vegetation phenology, dynamic monitoring, and global change. Gap filling is the most important issue in reconstructing NDVI time series from satellites with high spatial resolution, e.g., the Landsat series and Chinese GaoFen-1/6 series. Due to the sparse revisit frequencies of high-resolution satellites, traditional reconstruction approaches face the challenge of dealing with large gaps in raw NDVI time series data. In this paper, a climate incorporated gap-filling (CGF) method is proposed for the reconstruction of Landsat historical NDVI time series data. The CGF model considers the relationship of the NDVI time series and climate conditions between two adjacent years. Climate variables, including downward solar shortwave radiation, precipitation, and temperature, are used to characterize the constrain factors of vegetation growth. Radial basis function networks (RBFNs) are used to link the NDVI time series between two adjacent years with variabilities in climatic conditions. An RBFN predicted a background NDVI time series in the target year, and the observed NDVI values in this year were used to adjust the predicted NDVI time series. Finally, the NDVI time series were recursively reconstructed from 2018 to 1986. The experiments were performed in a heterogeneous region in the Qilian Mountains. The results demonstrate that the proposed method can accurately reconstruct and generate continuous 30 m 8-day NDVI time series using Landsat observations. The CGF method outperforms traditional time series reconstruction methods (e.g., the harmonic analysis of time series (HANTS) and Savitzky-Golay (SG) filter methods) when the raw time series is contaminated with large gaps, which widely exist in Landsat images.
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Lompar, Miloš, Branislava Lalić, Ljiljana Dekić, and Mina Petrić. "Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data." Atmosphere 10, no. 1 (2019): 13. http://dx.doi.org/10.3390/atmos10010013.

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Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for different landscapes, latitudes, and altitudes, including tropical and midlatitudes. An evaluation of results in terms of root mean square error (RMSE) obtained using hourly and daily data is provided. The study shows very low average RMSE for all gap lengths ranging from 1.1 °C (Montecristo, Italy) to 1.9 °C (Gumpenstein, Austria).
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Kang, Minseok, Kazuhito Ichii, Joon Kim, et al. "New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach." Atmosphere 10, no. 10 (2019): 568. http://dx.doi.org/10.3390/atmos10100568.

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In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach.
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STAUCH, VANESSA J., and ANDREW J. JARVIS. "A semi-parametric gap-filling model for eddy covariance CO2 flux time series data." Global Change Biology 12, no. 9 (2006): 1707–16. http://dx.doi.org/10.1111/j.1365-2486.2006.01227.x.

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Zhao, Junbin, Holger Lange, and Helge Meissner. "Gap-filling continuously-measured soil respiration data: A highlight of time-series-based methods." Agricultural and Forest Meteorology 285-286 (May 2020): 107912. http://dx.doi.org/10.1016/j.agrformet.2020.107912.

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Boudhina, Nissaf, Rim Zitouna-Chebbi, Insaf Mekki, et al. "Evaluating four gap-filling methods for eddy covariance measurements of evapotranspiration over hilly crop fields." Geoscientific Instrumentation, Methods and Data Systems 7, no. 2 (2018): 151–67. http://dx.doi.org/10.5194/gi-7-151-2018.

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Abstract. Estimating evapotranspiration in hilly watersheds is paramount for managing water resources, especially in semiarid/subhumid regions. The eddy covariance (EC) technique allows continuous measurements of latent heat flux (LE). However, time series of EC measurements often experience large portions of missing data because of instrumental malfunctions or quality filtering. Existing gap-filling methods are questionable over hilly crop fields because of changes in airflow inclination and subsequent aerodynamic properties. We evaluated the performances of different gap-filling methods before and after tailoring to conditions of hilly crop fields. The tailoring consisted of splitting the LE time series beforehand on the basis of upslope and downslope winds. The experiment was setup within an agricultural hilly watershed in northeastern Tunisia. EC measurements were collected throughout the growth cycle of three wheat crops, two of them located in adjacent fields on opposite hillslopes, and the third one located in a flat field. We considered four gap-filling methods: the REddyProc method, the linear regression between LE and net radiation (Rn), the multi-linear regression of LE against the other energy fluxes, and the use of evaporative fraction (EF). Regardless of the method, the splitting of the LE time series did not impact the gap-filling rate, and it might improve the accuracies on LE retrievals in some cases. Regardless of the method, the obtained accuracies on LE estimates after gap filling were close to instrumental accuracies, and they were comparable to those reported in previous studies over flat and mountainous terrains. Overall, REddyProc was the most appropriate method, for both gap-filling rate and retrieval accuracy. Thus, it seems possible to conduct gap filling for LE time series collected over hilly crop fields, provided the LE time series are split beforehand on the basis of upslope–downslope winds. Future works should address consecutive vegetation growth cycles for a larger panel of conditions in terms of climate, vegetation, and water status.
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Pascual-Granado, J., R. Garrido, J. Gutirrez-Soto, and S. Martín-Ruiz. "Towards a More General Method for Filling Gaps in Time Series." Proceedings of the International Astronomical Union 7, S285 (2011): 392–93. http://dx.doi.org/10.1017/s1743921312001172.

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AbstractThe need for a proper interpolation method for data coming from space missions like CoRoT is emphasized. A new gap-filling method is introduced which is based on auto-regressive moving average interpolation (ARMA) models. The method is tested on light curves from stars observed by the CoRoT satellite, filling the gaps caused by the South Atlantic Anomaly.
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Beguería, Santiago, Miquel Tomas-Burguera, Roberto Serrano-Notivoli, Dhais Peña-Angulo, Sergio M. Vicente-Serrano, and José-Carlos González-Hidalgo. "Gap Filling of Monthly Temperature Data and Its Effect on Climatic Variability and Trends." Journal of Climate 32, no. 22 (2019): 7797–821. http://dx.doi.org/10.1175/jcli-d-19-0244.1.

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Abstract Observational datasets of climatic variables are frequently composed of fragmentary time series covering different time spans and plagued with data gaps. Most statistical methods and environmental models, however, require serially complete data, so gap filling is a routine procedure. However, very often this preliminary stage is undertaken with no consideration of the potentially adverse effects that it can have on further analyses. In addition to numerical effects and trade-offs that are inherent to any imputation method, observational climatic datasets often exhibit temporal changes in the number of available records, which result in further spurious effects if the gap-filling process is sensitive to it. We examined the effect of data reconstruction in a large dataset of monthly temperature records spanning over several decades, during which substantial changes occurred in terms of data availability. We made a thorough analysis in terms of goodness of fit (mean error) and bias in the first two moments (mean and variance), in the extreme quantiles, and in long-term trend magnitude and significance. We show that gap filling may result in biases in the mean and the variance of the reconstructed series, and also in the magnitude and significance of temporal trends. Introduction of a two-step bias correction in the gap-filling process solved some of these problems, although it did not allow us to produce completely unbiased trend estimates. Using only one (the best) neighbor and performing a one-step bias correction, being a simpler approach, closely rivaled this method, although it had similar problems with trend estimates. A trade-off must be assumed between goodness of fit (error minimization) and variance bias.
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Santos, Janaina Cassiano dos, Gustavo Bastos Lyra, Marcel Carvalho Abreu, and Daniel Carlos de Menezes. "An approach to quality analysis, gap filling and homogeneity of monthly rainfall series." Revista Engenharia na Agricultura - Reveng 29 (August 16, 2021): 157–68. http://dx.doi.org/10.13083/reveng.v29i1.11738.

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The aim of this work was to propose a method for the consistency of climatic series of monthly rainfall using a supervised and unsupervised approach. The methodology was applied for the series (1961-2010) of rainfall from weather stations located in the State of Rio de Janeiro (RJ) and in the borders with the states of São Paulo, Minas Gerais and Espírito Santo with the State of Rio de Janeiro. The data were submitted to quality analysis (physical and climatic limit and, space-time tendency) and gap filling, based on simple linear regression analysis, associated with the prediction band (p < 0.05 or 0.01), in addition to the Z-score (3, 4 or 5). Next, homogeneity analysis was applied to the continuous series, using the method of cumulative residuals. The coefficients of determination (r²) between the assessed series and the reference series were greater than 0.70 for gap filling both for the supervised and unsupervised approaches. In the analysis of data homogeneity, supervised and unsupervised approaches were effective in selecting homogeneous series, in which five out of the nine final stations were homogeneous (p > 0.9). In the other series, the homogeneity break points were identified and the simple linear regression method was applied for their homogenization. The proposed method was effective to consist of the rainfall series and allows the use of these data in climate studies.
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Dissertations / Theses on the topic "Data series gap-filling"

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Rodrigues, Mutti Pedro. "Caractérisation de la sécheresse dans le nord-est du Brésil : une analyse multi-échelle des bassins versants et suivi par télédétection." Thesis, Rennes 2, 2020. http://www.theses.fr/2020REN20036.

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La sécheresse est un phénomène récurrent dans la région du Nord-Est du Brésil (NEB), en particulier dans les régions semi-arides de l’intérieur. Bien que plusieurs études de la sécheresse aient été menées au NEB, certains aspects méthodologiques inhérents à la qualité des données, aux spécificités des techniques utilisées et à l'échelle spatiale ont été encore peu discutés. Dans ce contexte, l'objectif de cette thèse est de caractériser les différents aspects de la sécheresse au NEB en considérant les caractéristiques des données météorologiques, les différentes échelles spatiales et les alternatives de surveillance par télédétection. Cette caractérisation a été réalisée dans le bassin du fleuve São Francisco (BFSF), qui présente une grande diversité climatique, dans le bassin du fleuve Piranhas- Açu (BFPA), qui est complètement inséré dans le domaine semi-aride du NEB, et dans des secteurs de désertification. Dans la première étude, nous avons utilisé le déficit d'évaporation dans le suivi de la sécheresse dans le BFSF. Les résultats montrent que les périodes de pénurie d'eau deviennent plus fréquentes et plus intenses dans les zones côtières et centrales du bassin. Dans la deuxième étude, le Rainfall Anomaly Index a été utilisé dans le BFPA pour identifier les épisodes de sécheresse, qui sont principalement associés aux épisodes El Niño et au réchauffement anormal des eaux de l'océan Atlantique Nord tropical. Enfin, dans la troisième étude, différents modèles stochastiques ont été testés afin de prévoir le Normalized Difference Vegetation Index obtenu par télédétection sur six secteurs de désertification dans le NEB. Les résultats montrent que les modèles testés prévoient de manière satisfaisante les états de végétation sèche et dégradée à court terme<br>Drought is a recurrent phenomenon in the Northeast Brazil (NEB) region, especially in its semiarid inlands. Although several drought studies have been carried out at the NEB, some important methodological aspects inherent to data quality and control, specificities of the used techniques, and spatial scale still need to be further discussed. Therefore, the objective of this thesis is to characterize different aspects of drought in the NEB considering different spatial scales, meteorological data characteristics, and remote sensing monitoring alternatives. This characterization was carried out in the São Francisco watershed (SFW), which presents a remarkable climate diversity, in the Piranhas-Açu watershed (PAW), which is entirely located in the semiarid NEB, and in desertification hotspots. In the first study, we used the evaporation deficit as drought index in the SFW. Results show that periods of water shortage are becoming more frequent and more intense in the coastal and middle zones of the basin. In the second study, a rainfall anomaly index was used in the PAW to identify drought events, which are mostly associated with El Niño events and the anomalous warming of the Tropical North Atlantic Ocean. Finally, in the third study, different stochastic models were tested in order to forecast remotely sensed Normalized Difference Vegetation Index data over six desertification hotspots in the NEB. Results show that the tested models satisfactorily forecast short-term dry and degraded vegetation states
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Book chapters on the topic "Data series gap-filling"

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Mordick, Briana E. "Filling the Data Gap: What We Know (and Don’t Know) about Hydraulic Fracturing and Acidizing in California." In ACS Symposium Series. American Chemical Society, 2015. http://dx.doi.org/10.1021/bk-2015-1216.ch010.

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Sarafanov, Mikhail, Nikolay O. Nikitin, and Anna V. Kalyuzhnaya. "Automated Data-Driven Approach for Gap Filling in the Time Series Using Evolutionary Learning." In 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87869-6_60.

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Conference papers on the topic "Data series gap-filling"

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Fischer, Raphael, Nico Piatkowski, Charlotte Pelletier, Geoffrey I. Webb, Francois Petitjean, and Katharina Morik. "No Cloud on the Horizon: Probabilistic Gap Filling in Satellite Image Series." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00069.

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Golestani, Maziar, and Mostafa Zeinoddini. "Gap-Filling and Predicting Wave Parameters Using Support Vector Regression Method." In ASME 2011 30th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2011. http://dx.doi.org/10.1115/omae2011-49814.

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Knowledge of relevant oceanographic parameters is of utmost importance in the rational design of coastal structures and ports. Therefore, an accurate prediction of wave parameters is especially important for safety and economic reasons. Recently, statistical learning methods, such as Support Vector Regression (SVR) have been successfully employed by researchers in problems such as lake water level predictions, and significant wave height prediction. The current study reports potential application of a SVR approach to predict the wave spectra and significant wave height. Also the capability of the model to fill data gaps was tested using different approaches. Concurrent wind and wave records (standard meteorological and spectral density data) from 4 stations in 2003, 2007, 2008 and 2009 were used both for the training the SVR system and its verification. The choice of these four locations facilitated the comparison of model performances in different geographical areas. The SVR model was then used to obtain predictions for the wave spectra and also time series of wave parameters (separately for each station) such as its Hs and Tp from spectra and wind records. New approach was used to predict wave spectra comparing to similar studies. Reasonably well correlation was found between the predicted and measured wave parameters. The SVR model was first trained and tested using various methods for selecting training data. Also different values for SVM parameters (e.g. tolerance of termination criterion, cost, and gamma in kernel function) were tested. The best possible results were obtained using a Unix shell script (in Linux) which automatically implements different values for different input parameters and finds the best regression by calculating statistical scores like correlation of coefficient, RMSE, bias and scatter index. Finally for a better understanding of the results, Quantile-Quantile plots were produced. The results show that SVR can be successfully used for prediction of Hs and wave spectrum out of a series of wind and spectral wave parameters inputs. Also it was noticed that SVR is an efficient tool to be used when data gaps are present in the data.
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Baltazar, Juan-Carlos, and David E. Claridge. "Study of Cubic Splines and Fourier Series as Interpolation Techniques for Filling in Short Periods of Missing Building Energy Use and Weather Data." In ASME Solar 2002: International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1031.

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A study of cubic splines and Fourier series as interpolation techniques for filling in missing data in energy and meteorological time series is presented. The followed procedure created artificially missing points (pseudo-gaps) in measured data sets and was based on the local behavior of the data set around those pseudo-gaps. Five variants of the cubic spline technique and 12 variants of Fourier series were tested and compared with linear interpolation, for filling in gaps of 1 to 6 hours of data in 20 samples of energy use and weather data. Each of the samples is at least one year in length. The analysis showed that linear interpolation is superior to the spline and Fourier series techniques for filling in 1–6 hour gaps in time series dry bulb and dew point temperature data. For filling 1–6 hour gaps in building cooling and heating use, the Fourier series approach with 24 data points before and after each gap and six constants was found to be the most suitable. In cases where there are insufficient data points for the application of this approach, simple linear interpolation is recommended.
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