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

Author: Grafiati

Published: 25 June 2021

Last updated: 29 July 2025

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1

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

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2

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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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 dif

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Tang, Hongjie. "Missing Data Filling of Model Based on Neural Network." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 32, no. 04 (2024): 649–71. http://dx.doi.org/10.1142/s0218488524400129.

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Today’s society has entered the pace of information development era. All kinds of information are digitized, while the loss of information data also directly affects the normal operation of the application system and has become the biggest obstacle to the development of information technology. The existing missing filling methods do not take into account the time-series information of the data set. Based on the neural network method for filling missing data in time series, an end-to-end missing data filling method for time series based on the residual of regression equation is proposed. Under

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SILVA, LÍVIA MARIA CAVALCANTE, FABIANO SIMPLICIO BEZERRA, MARIA CATIANA DE VASCONCELOS, MADSON RAFAEL BARBALHO DA SILVA, ANA CLÁUDIA DAVINO DOS SANTOS, and CERES DUARTE GUEDESCABRAL DE ALMEIDA. "GAP FILLING PROCEDURES OF CLIMATOLOGICAL SERIES IN THE STATE OF PERNAMBUCO." IRRIGA 1, no. 4 (2021): 754–64. http://dx.doi.org/10.15809/irriga.2021v1n4p754-764.

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This study aimed to compare the applicability of three methods of filling gaps in rainfall and temperature data from thirteen automatic weather stations (AWS) in the state of Pernambuco, from January to December 2019. The methods used were arithmetic mean, regional weighting, and simple linear regression. The data estimated by filling techniques have been subjected to comparison using R² and descriptive statistical analysis. The estimated data of air temperature presented R2 equal or very close to 1 for the three methods. On the other hand, the estimated data of rainfall showed values similar

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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 befo

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Sharma, Nalin, Prasun Kumar Gupta, and Prabhakar Alok Verma. "Temporal Gap Filling of Nighttime Light Composites." Journal of Geomatics 19, no. 1 (2025): 29–38. https://doi.org/10.58825/jog.2025.19.1.152.

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The temporal nighttime light (NTL) data generated by DMSP-OLS sensors was discovered to have large gaps (missing values) over time. The research aims to provide a scientifically valid gap-filling mechanism for having consistent DMPS-OLS time series data (1992-2013) and predicting the historic NTL (1991-1985) for long-term studies. A deep learning neural network, Long Short Term Memory (LSTM) has been proposed in the study for temporal gap filling and historic NTL prediction. The developed LSTM model is being tested in a time distributed wrapper way having window size (3-7) for the temporal gap

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8

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/sp

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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

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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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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 &lt

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12

Fine, Lior, Antoine Richard, Josef Tanny, Cedric Pradalier, Rafael Rosa, and Offer Rozenstein. "Introducing State-of-the-Art Deep Learning Technique for Gap-Filling of Eddy Covariance Crop Evapotranspiration Data." Water 14, no. 5 (2022): 763. http://dx.doi.org/10.3390/w14050763.

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Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS)

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Sabino, Marlus, and Adilson P. de Souza. "Gap-filling meteorological data series using the GapMET software in the state of Mato Grosso, Brazil." Revista Brasileira de Engenharia Agrícola e Ambiental 27, no. 2 (2023): 149–56. http://dx.doi.org/10.1590/1807-1929/agriambi.v27n2p149-156.

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ABSTRACT This paper aimed to introduce the GapMET software, developed by the authors, and evaluate the accuracy of its six methods for gap-filling the main meteorological variables monitored by weather station in the state of Mato Grosso, Brazil, using reference time series from neighbour weather station and/or remote sensing products. The methods were tested on seven different databases, with 25 to 80% artificial gaps, and their accuracy was given by the number of gaps left unfilled, the bias, the RMSE, and Pearson’s correlation. The GapMET software showed good results in filling meteorologic

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14

Wang, Yidan, Xuewen Zhou, Zurui Ao, Kun Xiao, Chenxi Yan, and Qinchuan Xin. "Gap-Filling and Missing Information Recovery for Time Series of MODIS Data Using Deep Learning-Based Methods." Remote Sensing 14, no. 19 (2022): 4692. http://dx.doi.org/10.3390/rs14194692.

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Sensors onboard satellite platforms with short revisiting periods acquire frequent earth observation data. One limitation to the utility of satellite-based data is missing information in the time series of images due to cloud contamination and sensor malfunction. Most studies on gap-filling and cloud removal process individual images, and existing multi-temporal image restoration methods still have problems in dealing with images that have large areas with frequent cloud contamination. Considering these issues, we proposed a deep learning-based method named content-sequence-texture generation

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15

Faisal, M., and R. S. Makar. "Development of a simplified technique for gap filling of Normalize Difference Vegetation Index (NDVI) time series data." Journal of Applied and Natural Science 14, no. 4 (2022): 1500–1506. http://dx.doi.org/10.31018/jans.v14i4.4095.

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The presence of gaps or missing values in time series prevents the practical use of such data. The current research aims at developing a simplified, straightforward technique for gap-filling the time series data of the Normalize Difference Vegetation Index (NDVI) generated using Moderate Resolution Imaging Spectroradiometer (MODIS). This research assumes that a relationship exists between the pixel location, date of acquisition and its NDVI value within a defined timeline. Therefore, two relatively simple methods were tested: the Multiple Linear Regression (MLR) analysis and the Artificial Neu

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16

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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17

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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18

Tardivo, Gianmarco, and Antonio Berti. "A Dynamic Method for Gap Filling in Daily Temperature Datasets." Journal of Applied Meteorology and Climatology 51, no. 6 (2012): 1079–86. http://dx.doi.org/10.1175/jamc-d-11-0117.1.

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AbstractA regression-based approach for temperature data reconstruction has been used to fill the gaps in the series of automatic temperature records obtained from the meteorological network of Veneto Region (northeastern Italy). The method presented is characterized by a dynamic selection of the reconstructing stations and of the coupling period that can precede or follow the missing data. Each gap is considered as a specific case, identifying the best set of stations and the period that minimizes the estimated reconstruction error for the gap, thus permitting a potentially better adaptation

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Gao, Dexiang, Jingyu Yao, Shuting Yu, Yulong Ma, Lei Li, and Zhongming Gao. "Eddy Covariance CO2 Flux Gap Filling for Long Data Gaps: A Novel Framework Based on Machine Learning and Time Series Decomposition." Remote Sensing 15, no. 10 (2023): 2695. http://dx.doi.org/10.3390/rs15102695.

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Continuous long-term eddy covariance (EC) measurements of CO2 fluxes (NEE) in a variety of terrestrial ecosystems are critical for investigating the impacts of climate change on ecosystem carbon cycling. However, due to a number of issues, approximately 30–60% of annual flux data obtained at EC flux sites around the world are reported as gaps. Given that the annual total NEE is mostly determined by variations in the NEE data with time scales longer than one day, we propose a novel framework to perform gap filling in NEE data based on machine learning (ML) and time series decomposition (TSD). T

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20

Cajal, Diego, David Hernando, Jesús Lázaro, Pablo Laguna, Eduardo Gil, and Raquel Bailón. "Effects of Missing Data on Heart Rate Variability Metrics." Sensors 22, no. 15 (2022): 5774. http://dx.doi.org/10.3390/s22155774.

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Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviat

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Longman, Ryan J., Andrew J. Newman, Thomas W. Giambelluca, and Mathew Lucas. "Characterizing the Uncertainty and Assessing the Value of Gap-Filled Daily Rainfall Data in Hawaii." Journal of Applied Meteorology and Climatology 59, no. 7 (2020): 1261–76. http://dx.doi.org/10.1175/jamc-d-20-0007.1.

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AbstractAlmost all daily rainfall time series contain gaps in the instrumental record. Various methods can be used to fill in missing data using observations at neighboring sites (predictor stations). In this study, five computationally simple gap-filling approaches—normal ratio (NR), linear regression (LR), inverse distance weighting (ID), quantile mapping (QM), and single best estimator (BE)—are evaluated to 1) determine the optimal method for gap filling daily rainfall in Hawaii, 2) quantify the error associated with filling gaps of various size, and 3) determine the value of gap filling pr

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22

Tang, Zhipeng, Giuseppe Amatulli, Petri K. E. Pellikka, and Janne Heiskanen. "Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series." Remote Sensing 14, no. 1 (2021): 172. http://dx.doi.org/10.3390/rs14010172.

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The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Predicti

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Qian, Nijia, Jingxiang Gao, Zengke Li, et al. "Bridging the Terrestrial Water Storage Anomalies between the GRACE/GRACE-FO Gap Using BEAST + GMDH Algorithm." Remote Sensing 16, no. 19 (2024): 3693. http://dx.doi.org/10.3390/rs16193693.

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Regarding the terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-on (-FO) gravity satellite missions, a BEAST (Bayesian estimator of abrupt change, seasonal change and trend)+GMDH (group method of data handling) gap-filling scheme driven by hydrological and meteorological data is proposed. Considering these driving data usually cannot fully capture the trend changes of the TWSA time series, we propose first to use the BEAST algorithm to perform piecewise linear detrending for the TWSA series and then fill the gap of the det

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Liang, Jieyu, Chao Ren, Yi Li, et al. "Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery." ISPRS International Journal of Geo-Information 12, no. 6 (2023): 214. http://dx.doi.org/10.3390/ijgi12060214.

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Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calcu

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Chinasho, Alefu, Bobe Bedadi, Tesfaye Lemma, Tamado Tana, Tilahun Hordofa, and Bisrat Elias. "Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia." Advances in Meteorology 2021 (August 18, 2021): 1–15. http://dx.doi.org/10.1155/2021/9657460.

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Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered

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Vuolo, Francesco, Wai-Tim Ng, and Clement Atzberger. "Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data." International Journal of Applied Earth Observation and Geoinformation 57 (May 2017): 202–13. http://dx.doi.org/10.1016/j.jag.2016.12.012.

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Dengel, S., D. Zona, T. Sachs, et al. "Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands." Biogeosciences Discussions 10, no. 5 (2013): 7727–59. http://dx.doi.org/10.5194/bgd-10-7727-2013.

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Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes in order to recover missing

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Hocke, K., and N. Kämpfer. "Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram." Atmospheric Chemistry and Physics 9, no. 12 (2009): 4197–206. http://dx.doi.org/10.5194/acp-9-4197-2009.

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Abstract. The Lomb-Scargle periodogram is widely used for the estimation of the power spectral density of unevenly sampled data. A small extension of the algorithm of the Lomb-Scargle periodogram permits the estimation of the phases of the spectral components. The amplitude and phase information is sufficient for the construction of a complex Fourier spectrum. The inverse Fourier transform can be applied to this Fourier spectrum and provides an evenly sampled series (Scargle, 1989). We are testing the proposed reconstruction method by means of artificial time series and real observations of me

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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." Journal of Solar Energy Engineering 128, no. 2 (2005): 226–30. http://dx.doi.org/10.1115/1.2189872.

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A study of cubic splines and Fourier series as interpolation techniques for filling in missing hourly data in energy and meteorological time series data sets is presented. The procedure developed in this paper is based on the local patterns of the data around the gaps. Artificial gaps, or “pseudogaps,” created by deleting consecutive data points from the measured data sets, were filled using four variants of the cubic spline technique and 12 variants of the Fourier series technique. The accuracy of these techniques was compared to the accuracy of results obtained using linear interpolation to

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Li, Yue, Qiang Liu, Shuang Chen, and Xiaotong Zhang. "An Improved Gap-Filling Method for Reconstructing Dense Time-Series Images from LANDSAT 7 SLC-Off Data." Remote Sensing 16, no. 12 (2024): 2064. http://dx.doi.org/10.3390/rs16122064.

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Over recent decades, Landsat satellite data has evolved into a highly valuable resource across diverse fields. Long-term satellite data records with integrity and consistency, such as the Landsat series, provide indispensable data for many applications. However, the malfunction of the Scan Line Corrector (SLC) on the Landsat 7 satellite in 2003 resulted in stripping in subsequent images, compromising the temporal consistency and data quality of Landsat time-series data. While various methods have been proposed to improve the quality of Landsat 7 SLC-off data, existing gap-filling methods fail

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Hocke, K., and N. Kämpfer. "Gap filling and noise reduction of unevenly sampled data by means of the Lomb-Scargle periodogram." Atmospheric Chemistry and Physics Discussions 8, no. 2 (2008): 4603–23. http://dx.doi.org/10.5194/acpd-8-4603-2008.

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Abstract. The Lomb-Scargle periodogram is widely used for the estimation of the power spectral density of unevenly sampled data. A small extension of the algorithm of the Lomb-Scargle periodogram permits the estimation of the phases of the spectral components. The amplitude and phase information is sufficient for the construction of a complex Fourier spectrum. The inverse Fourier transform can be applied to this Fourier spectrum and provides an evenly sampled series (Scargle, 1989). We are testing the proposed reconstruction method by means of artificial time series and real observations of me

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Schwatke, Christian, Daniel Scherer, and Denise Dettmering. "Automated Extraction of Consistent Time-Variable Water Surfaces of Lakes and Reservoirs Based on Landsat and Sentinel-2." Remote Sensing 11, no. 9 (2019): 1010. http://dx.doi.org/10.3390/rs11091010.

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In this study, a new approach for the automated extraction of high-resolution time-variable water surfaces is presented. For that purpose, optical images from Landsat and Sentinel-2 are used between January 1984 and June 2018. The first part of this new approach is the extraction of land-water masks by combining five water indexes and using an automated threshold computation. In the second part of this approach, all data gaps caused by voids, clouds, cloud shadows, or snow are filled by using a long-term water probability mask. This mask is finally used in an iterative approach for filling rem

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Todeschini, Thaiana, and Frederico Carlos Martins de Menezes Filho. "Gap Filling and consistency analysis in monthly rainfall series: case study in southeastern Mato Grosso." Revista Brasileira de Ciência, Tecnologia e Inovação 9, no. 2 (2024): 140–46. https://doi.org/10.18554/rbcti.v9i2.8085.

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The analysis of precipitation time series is crucial for understanding climate patterns and managing water resources. However, data gaps caused by missing information, technical issues, and data transmission failures compromise the accuracy of climate analyses and forecasts. This study aims to address these gaps and assess the consistency of monthly rainfall series in southeastern Mato Grosso, using the regional weighting method and the double-mass analysis. Data from five rainfall stations were selected: Vale Rico (01654005), Alto Araguaia (01753000), Fazenda Taquari (01853000), Alto Garças (

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Dengel, S., D. Zona, T. Sachs, et al. "Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands." Biogeosciences 10, no. 12 (2013): 8185–200. http://dx.doi.org/10.5194/bg-10-8185-2013.

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Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes, explain the method and tes

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Sriyanto, Sesar Prabu Dwi, Ping Astony Angmalisang, Lusia Manu, et al. "Automatic tsunami arrival detection algorithm for sea level observation system." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (2021): 180–90. http://dx.doi.org/10.14710/jtsiskom.2021.14009.

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The automatic tsunami detection algorithm needs to be put in the sea level observation system to give society a quick warning when a tsunami happens. This study designs an automatic tsunami detection algorithm consisting of three sub-algorithm: spike elimination, gap data filling, and tsunami detection. Spike elimination and gap data filling are used to improve the sea level data, which is often disturbed by spikes and gap data due to electronic factors. This algorithm was tested using time-series tide gauge data that contain tsunami waveforms in Indonesia from 2007 to 2019. About 54.52 % of 4

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Coutinho, Eluã Ramos, Robson Mariano da Silva, Jonni Guiller Ferreira Madeira, Pollyanna Rodrigues de Oliveira dos Santos Coutinho, Ronney Arismel Mancebo Boloy, and Angel Ramon Sanchez Delgado. "Application of Artificial Neural Networks (ANNs) in the Gap Filling of Meteorological Time Series." Revista Brasileira de Meteorologia 33, no. 2 (2018): 317–28. http://dx.doi.org/10.1590/0102-7786332013.

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Abstract This study estimates and fills real flaws in a series of meteorological data belonging to four regions of the state of Rio de Janeiro. For this, an Artificial Neural Network (ANN) of Multilayer Perceptron (MLP) was applied. In order to evaluate its adequacy, the monthly variables of maximum air temperature and relative humidity of the period between 05/31/2002 and 12/31/2014 were estimated and compared with the results obtained by Multiple Linear Regression (MLR) and Regions Average (RA), and still faced with the recorded data. To analyze the estimated values and define the best model

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Johann, G., I. Papadakis, and A. Pfister. "Historical precipitation time series for applications in urban hydrology." Water Science and Technology 37, no. 11 (1998): 147–53. http://dx.doi.org/10.2166/wst.1998.0456.

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The quality of results of rainfall runoff modelling depends strongly on the hydrologic input data. In particular, for urban hydrology applications long-term rainfall series without gaps and of high quality and reliability are required in rainfall runoff and hydraulic simulation of the investigated drainage and receiving water system. The presented study discusses a method for filling gaps in precipitation time series provided by the Emschergenossenschaft and Lippeverband (EG/LV) in north west Germany. Various time intervals based on deterministic and statistical approaches are investigated. In

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Julien, Yves, and José A. Sobrino. "Optimizing and comparing gap-filling techniques using simulated NDVI time series from remotely sensed global data." International Journal of Applied Earth Observation and Geoinformation 76 (April 2019): 93–111. http://dx.doi.org/10.1016/j.jag.2018.11.008.

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Belda, Santiago, Luca Pipia, Pablo Morcillo-Pallarés, and Jochem Verrelst. "Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring." Agronomy 10, no. 5 (2020): 618. http://dx.doi.org/10.3390/agronomy10050618.

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Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally in

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Mahabbati, Atbin, Jason Beringer, Matthias Leopold, et al. "A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers." Geoscientific Instrumentation, Methods and Data Systems 10, no. 1 (2021): 123–40. http://dx.doi.org/10.5194/gi-10-123-2021.

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Abstract. The errors and uncertainties associated with gap-filling algorithms of water, carbon, and energy fluxes data have always been one of the main challenges of the global network of microclimatological tower sites that use the eddy covariance (EC) technique. To address these concerns and find more efficient gap-filling algorithms, we reviewed eight algorithms to estimate missing values of environmental drivers and nine algorithms for the three major fluxes typically found in EC time series. We then examined the algorithms' performance for different gap-filling scenarios utilising the dat

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Arriagada, Pedro, Bruno Karelovic, and Oscar Link. "Automatic gap-filling of daily streamflow time series in data-scarce regions using a machine learning algorithm." Journal of Hydrology 598 (July 2021): 126454. http://dx.doi.org/10.1016/j.jhydrol.2021.126454.

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Evans, Fiona H., and Jianxiu Shen. "Spatially Weighted Estimation of Broadacre Crop Growth Improves Gap-Filling of Landsat NDVI." Remote Sensing 13, no. 11 (2021): 2128. http://dx.doi.org/10.3390/rs13112128.

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Seasonal climate is the main driver of crop growth and yield in broadacre grain cropping systems. With a 40-year record of 30 m resolution images and 16-day revisits, the Landsat satellite series is ideal for producing long-term records of remotely sensed phenology to build understanding of how climate affects crop growth. However, the time-series of Landsat images exhibits gaps caused by cloud cover, which is common in wet periods when crops reach maximum growth. We propose a novel spatial–temporal approach to gap-filling that avoids data fusion. Crop growth curve estimation is used to perfor

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Ghafarian Malamiri, Hamid Reza, Hadi Zare, Iman Rousta, et al. "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." Remote Sensing 12, no. 17 (2020): 2747. http://dx.doi.org/10.3390/rs12172747.

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Monitoring vegetation changes over time is very important in dry areas such as Iran, given its pronounced drought-prone agricultural system. Vegetation indices derived from remotely sensed satellite imageries are successfully used to monitor vegetation changes at various scales. Atmospheric dust as well as airborne particles, particularly gases and clouds, significantly affect the reflection of energy from the surface, especially in visible, short and infrared wavelengths. This results in imageries with missing data (gaps) and outliers while vegetation change analysis requires integrated and c

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Fan, Yu Guang, Jian Han, Jing Ming Li, Bing Chen, and San Ping Zhou. "The Study on the Dissolution Process of Oxygen and Nitrogen in Gas-Soluble Water." Advanced Materials Research 830 (October 2013): 331–36. http://dx.doi.org/10.4028/www.scientific.net/amr.830.331.

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In air flotation process, different gas produce different gas content of gas-soluble water. According to the difference of solubility of nitrogen and oxygen in water, the affect of the difference of molecule structures between nitrogen and oxygen on their solubility in water was discussed in the paper. Then, Two types of gas dissolution in water was introduced in the paper---gap filling and hydration. The concept of effective gap degree was proposed. And According to the effective gap degrees and hydration coefficient of nitrogen and oxygen, the change rules of the dissolved amount of oxygen a

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Ghafarian Malamiri, Hamid, Iman Rousta, Haraldur Olafsson, Hadi Zare, and Hao Zhang. "Gap-Filling of MODIS Time Series Land Surface Temperature (LST) Products Using Singular Spectrum Analysis (SSA)." Atmosphere 9, no. 9 (2018): 334. http://dx.doi.org/10.3390/atmos9090334.

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Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an

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Ruezzene, Camila Bermond, Renato Billia de Miranda, Talyson de Melo Bolleli, and Frederico Fábio Mauad. "Filling and validating rainfall data based on statistical techniques and artificial intelligence." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 16, no. 6 (2021): 1–14. http://dx.doi.org/10.4136/ambi-agua.2767.

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The study of the hydric regime of rainfall helps in management analysis and decision-making in hydrographic basins, but a fundamental condition is the need for continuous time series of data. Therefore, this study compared gap filling methods in precipitation data and validated them using robust statistical techniques. Precipitation data from the municipality of Itirapina, which has four monitoring stations, were used. Four gap filling techniques were used, namely: normal ratio method, inverse distance weighting, multiple regression and artificial neural networks, in the period from 1979 to 19

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Ponkina, Elena, Patrick Illiger, Olga Krotova, and Andrey Bondarovich. "Do ARMA Models Provide Better Gap Filling in Time Series of Soil Temperature and Soil Moisture? The Case of Arable Land in the Kulunda Steppe, Russia." Land 10, no. 6 (2021): 579. http://dx.doi.org/10.3390/land10060579.

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The adoption of climate-smart agriculture requires the comprehensive development of environmental monitoring tools, including online observation of climate and soil settings. They are often designed to measure soil properties automatically at different depths at hour or minute intervals. It is essential to have a complete dataset to use statistical models for the prediction of soil properties and to make short-term decisions regarding soil tillage operations and irrigation during a vegetation period. This is also important in applied hydrological studies. Nevertheless, the time series of soil

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Shi, Hua, George Xian, Roger Auch, Kevin Gallo, and Qiang Zhou. "Urban Heat Island and Its Regional Impacts Using Remotely Sensed Thermal Data—A Review of Recent Developments and Methodology." Land 10, no. 8 (2021): 867. http://dx.doi.org/10.3390/land10080867.

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Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been dev

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Markert, Kel N., Gustavious P. Williams, E. James Nelson, Daniel P. Ames, Hyongki Lee, and Robert E. Griffin. "Dense Time Series Generation of Surface Water Extents through Optical–SAR Sensor Fusion and Gap Filling." Remote Sensing 16, no. 7 (2024): 1262. http://dx.doi.org/10.3390/rs16071262.

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Surface water is a vital component of the Earth’s water cycle and characterizing its dynamics is essential for understanding and managing our water resources. Satellite-based remote sensing has been used to monitor surface water dynamics, but cloud cover can obscure surface observations, particularly during flood events, hindering water identification. The fusion of optical and synthetic aperture radar (SAR) data leverages the advantages of both sensors to provide accurate surface water maps while increasing the temporal density of unobstructed observations for monitoring surface water spatial

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Hadjipetrou, Stylianos, Gregoire Mariethoz, and Phaedon Kyriakidis. "Gap-Filling Sentinel-1 Offshore Wind Speed Image Time Series Using Multiple-Point Geostatistical Simulation and Reanalysis Data." Remote Sensing 15, no. 2 (2023): 409. http://dx.doi.org/10.3390/rs15020409.

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Offshore wind is expected to play a key role in future energy systems. Wind energy resource studies often call for long-term and spatially consistent datasets to assess the wind potential. Despite the vast amount of available data sources, no current means can provide relevant sub-daily information at a fine spatial scale (~1 km). Synthetic aperture radar (SAR) delivers wind field estimates over the ocean at fine spatial resolution but suffers from partial coverage and irregular revisit times. Physical model outputs, which are the basis of reanalysis products, can be queried at any time step b

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