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Keysar, Ariela. "Filling a data gap: the American Religious Identification Survey (ARIS) series." Religion 44, no. 3 (April 22, 2014): 383–95. http://dx.doi.org/10.1080/0048721x.2014.903648.
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Yu, Wentao, Jing Li, Qinhuo Liu, Jing Zhao, Yadong Dong, Xinran Zhu, Shangrong Lin, Hu Zhang, and Zhaoxing Zhang. "Gap Filling for Historical Landsat NDVI Time Series by Integrating Climate Data." Remote Sensing 13, no. 3 (January 29, 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 (January 4, 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, Yohana M. Indrawati, Juhan Park, Minkyu Moon, Jong-Hwan Lim, and Jung-Hwa Chun. "New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach." Atmosphere 10, no. 10 (September 22, 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 (August 1, 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, Frédéric Jacob, Nétij Ben Mechlia, Moncef Masmoudi, and Laurent Prévot. "Evaluating four gap-filling methods for eddy covariance measurements of evapotranspiration over hilly crop fields." Geoscientific Instrumentation, Methods and Data Systems 7, no. 2 (June 7, 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 (September 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 (October 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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Tardivo, Gianmarco, and Antonio Berti. "A Dynamic Method for Gap Filling in Daily Temperature Datasets." Journal of Applied Meteorology and Climatology 51, no. 6 (June 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 to time-dependent factors affecting the relationships between stations. The best sampling size is determined through an inference procedure, permitting a highly specific selection of the parameters used to fill each gap in the time series. With a proper selection of the parameters, the average errors of reconstruction are close to 0 and those corresponding to the 95th percentile are typically around 0.1°C. In comparison with similar regression-based approaches, the errors are lower, particularly for minimum temperatures, and the method limits inversions between the minimum, mean, and maximum temperatures.
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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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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 (July 1, 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 prior to spatial interpolation. Results show that the correlation between a target station and a predictor station is more important than proximity of the stations in determining the quality of a rainfall prediction. In addition, the inclusion of rain/no-rain correction on the basis of either correlation between stations or proximity between stations significantly reduces the amount of spurious rainfall added to a filled dataset. For large gaps, relative median errors ranged from 12.5% to 16.5% and no statistical differences were identified between methods. For submonthly gaps, the NR method consistently produced the lowest mean error for 1- (2.1%), 15- (16.6%), and 30-day (27.4%) gaps when the difference between filled and observed monthly totals was considered. Results indicate that gap filling prior to spatial interpolation improves the overall quality of the gridded estimates, because higher correlations and lower performance errors were found when 20% of the daily dataset is filled as opposed to leaving these data unfilled prior to spatial interpolation.
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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 (June 24, 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 mesospheric ozone, having data gaps and noise. For data gap filling and noise reduction, it is necessary to modify the Fourier spectrum before the inverse Fourier transform is done. The modification can be easily performed by selection of the relevant spectral components which are above a given confidence limit or within a certain frequency range. Examples with time series of lower mesospheric ozone show that the reconstruction method can reproduce steep ozone gradients around sunrise and sunset and superposed planetary wave-like oscillations observed by a ground-based microwave radiometer at Payerne. The importance of gap filling methods for climate change studies is demonstrated by means of long-term series of temperature and water vapor pressure at the Jungfraujoch station where data gaps from another instrument have been inserted before the linear trend is calculated. The results are encouraging but the present reconstruction algorithm is far away from being reliable and robust enough for a serious application.
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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 (March 4, 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 mesospheric ozone, having data gaps and noise. For data gap filling and noise reduction, it is necessary to modify the Fourier spectrum before the inverse Fourier transform is done. The modification can be easily performed by selection of the relevant spectral components which are above a given confidence limit or within a certain frequency range. Examples with time series of lower mesospheric ozone show that the reconstruction method can reproduce steep ozone gradients around sunrise and sunset and superposed planetary wave-like oscillations observed by a ground-based microwave radiometer at Payerne.
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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 gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.
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Dengel, S., D. Zona, T. Sachs, M. Aurela, M. Jammet, F. J. W. Parmentier, W. Oechel, and T. Vesala. "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 (May 3, 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 data points, explained the method and tested its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. In keeping with the principle of parsimony, we included only five standard meteorological variables traditionally measured at CH4 flux measurement sites. These included drivers such as air and soil temperature, barometric air pressure, solar radiation, and in addition wind direction (indicator of source location). Four fuzzy sets were included representing the time of day. High Pearson correlation coefficients (r) of 0.76–0.93 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach that we showed to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.
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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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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 (June 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 for filling, statistical techniques were applied such as correlation coefficient (r), Mean Percentage Error (MPE) and others. The results showed a high relation with the recorded data, presenting indexes between 0.94 to 0.98 of (r) for maximum air temperature and between 2.32% to 1.05% of (MPE), maintaining the precision between 97% A 99%. For the relative air humidity, the index (r) with MLP remained between 0.77 and 0.94 and (MPE) between 2.41% and 1.85%, maintaining estimates between 97% and 98%. These results highlight MLP as being effective in estimating and filling missing values.
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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 (November 16, 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 fill the same pseudogaps. The pseudogaps filled were 1–6 data points in length created in 18 year-long sets of hourly energy use and weather data. More than 1000 pseudogaps of each gap length were created in each of the 18 data sets and filled using each of the 17 techniques evaluated. Use of mean bias error as the selection criterion found that linear interpolation is superior to the cubic spline and Fourier series methodologies for filling gaps of dry bulb and dew point temperature time series data. For hourly building cooling and heating use data, the Fourier series approach with 24 data points before and after each gap and six terms was found to be the most suitable; where there are insufficient data points to apply this approach, simple linear interpolation is recommended.
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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 (June 1, 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. Intervals between 5 and 120 min will be discussed in particular. Several neighbour stations of the EG/LV raingauge network are considered. On the basis of representative examples it is shown how the time intervals mentioned above influences the quality of the estimated gap-filling rainfall data.
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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 (April 28, 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 remaining data gaps in all monthly masks which leads to a gap-less surface area time series for lakes and reservoirs. The results of this new approach are validated by comparing the surface area changes with water level time series from gauging stations. For inland waters in remote areas without in situ data water level time series from satellite altimetry are used. Overall, 32 globally distributed lakes and reservoirs of different extents up to 2482.27 km 2 are investigated. The average correlation coefficients between surface area time series and water levels from in situ and satellite altimetry have increased from 0.611 to 0.862 after filling the data gaps which is an improvement of about 41%. This new approach clearly demonstrates the quality improvement for the estimated land-water masks but also the strong impact of a reliable data gap-filling approach. All presented surface area time series are freely available on the Database of Hydrological Time Series of Inland (DAHITI).
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Dengel, S., D. Zona, T. Sachs, M. Aurela, M. Jammet, F. J. W. Parmentier, W. Oechel, and T. Vesala. "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 (December 11, 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 test its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. Three different approaches were tested by including drivers such as air and soil temperature, barometric air pressure, solar radiation, wind direction (indicator of source location) and in addition the lagged effect of water table depth and precipitation. In keeping with the principle of parsimony, we included up to five of these variables traditionally measured at CH4 flux measurement sites. Fuzzy sets were included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach which we show to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.
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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 (April 27, 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 inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To mitigate this computational burden, we propose to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters θ . To demonstrate our approach hardly affects the accuracy in fitting, we used Sentinel-2 LAI time series over an agricultural region in Castile and Leon, North-West Spain. The performance of image reconstructions were compared against the standard per-pixel GPR time series processing. Results showed that accuracies were on the same order (RMSE 0.1767 vs. 0.1564 [ m 2 / m 2 ] , 12% RMSE degradation) whereas processing time accelerated about 90 times. We further evaluated the alternative option of using the same hyperparameters for all the pixels within the complete scene. It led to similar overall accuracies over crop areas and computational performance. Crop phenology indicators were also calculated for the three different approaches and compared. Results showed analogous crop temporal patterns, with differences in start and end of growing season of no more than five days. To the benefit of crop monitoring applications, all the gap-filling and phenology indicators retrieval techniques have been implemented into the freely downloadable GUI toolbox DATimeS.
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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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Mahabbati, Atbin, Jason Beringer, Matthias Leopold, Ian McHugh, James Cleverly, Peter Isaac, and Azizallah Izady. "A comparison of gap-filling algorithms for eddy covariance fluxes and their drivers." Geoscientific Instrumentation, Methods and Data Systems 10, no. 1 (June 28, 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 data from five EC towers during 2013. This research's objectives were (a) to evaluate the impact of the gap lengths on the performance of each algorithm and (b) to compare the performance of traditional and new gap-filling techniques for the EC data, for fluxes, and separately for their corresponding meteorological drivers. The algorithms' performance was evaluated by generating nine gap windows with different lengths, ranging from a day to 365 d. In each scenario, a gap period was chosen randomly, and the data were removed from the dataset accordingly. After running each scenario, a variety of statistical metrics were used to evaluate the algorithms' performance. The algorithms showed different levels of sensitivity to the gap lengths; the Prophet Forecast Model (FBP) revealed the most sensitivity, whilst the performance of artificial neural networks (ANNs), for instance, did not vary as much by changing the gap length. The algorithms' performance generally decreased with increasing the gap length, yet the differences were not significant for windows smaller than 30 d. No significant differences between the algorithms were recognised for the meteorological and environmental drivers. However, the linear algorithms showed slight superiority over those of machine learning (ML), except the random forest (RF) algorithm estimating the ground heat flux (root mean square errors – RMSEs – of 28.91 and 33.92 for RF and classic linear regression – CLR, respectively). However, for the major fluxes, ML algorithms and the MDS showed superiority over the other algorithms. Even though ANNs, random forest (RF), and eXtreme Gradient Boost (XGB) showed comparable performance in gap-filling of the major fluxes, RF provided more consistent results with slightly less bias against the other ML algorithms. The results indicated no single algorithm that outperforms in all situations, but the RF is a potential alternative for the MDS and ANNs as regards flux gap-filling.
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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 (August 23, 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 image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.
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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 (May 28, 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 perform temporal smoothing and incorporation of spatial weights allows spatial smoothing. We tested our approach using Landsat NDVI data acquired for an 8000 ha study area in Western Australia using a train/test approach where 157 available Landsat-7 images between 2013 and 2019 were used to train the model, and 95 at least 80% cloud-free Landsat-8 images from the same period were used to test its performance. We found that compared to nonspatial estimation, use of spatial weights in growth curve estimation improved correlation between observed and predicted NDVI by 75%, MAE by 31% and RMSE by 75%. For cropland, the correlation is improved by 58%, the MAE by 36% and the RMSE by 76%. We conclude that spatially weighted estimation of crop growth curves can be used to fill spatial and temporal gaps in Landsat NDVI for the purpose of within-field monitoring. Our approach is also applicable to other data sources and vegetation indices.
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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 and nitrogen by each type of dissolution at different temperature were obtained through a series of data fitting calculation by using Matlab. Finally, the reason for the change of the amount of gap filling and hydration in gas-soluble water caused by temperature change was also analyzed in the paper.
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Bellido-Jiménez, Juan Antonio, Javier Estévez Gualda, and Amanda Penélope García-Marín. "Assessing Machine Learning Models for Gap Filling Daily Rainfall Series in a Semiarid Region of Spain." Atmosphere 12, no. 9 (September 9, 2021): 1158. http://dx.doi.org/10.3390/atmos12091158.
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The presence of missing data in hydrometeorological datasets is a common problem, usually due to sensor malfunction, deficiencies in records storage and transmission, or other recovery procedures issues. These missing values are the primary source of problems when analyzing and modeling their spatial and temporal variability. Thus, accurate gap-filling techniques for rainfall time series are necessary to have complete datasets, which is crucial in studying climate change evolution. In this work, several machine learning models have been assessed to gap-fill rainfall data, using different approaches and locations in the semiarid region of Andalusia (Southern Spain). Based on the obtained results, the use of neighbor data, located within a 50 km radius, highly outperformed the rest of the assessed approaches, with RMSE (root mean squared error) values up to 1.246 mm/day, MBE (mean bias error) values up to −0.001 mm/day, and R2 values up to 0.898. Besides, inland area results outperformed coastal area in most locations, arising the efficiency effects based on the distance to the sea (up to an improvement of 63.89% in terms of RMSE). Finally, machine learning (ML) models (especially MLP (multilayer perceptron)) notably outperformed simple linear regression estimations in the coastal sites, whereas in inland locations, the improvements were not such significant.
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Ghafarian Malamiri, Hamid Reza, Hadi Zare, Iman Rousta, Haraldur Olafsson, Emma Izquierdo Verdiguier, Hao Zhang, and Terence Darlington Mushore. "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 (August 25, 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 complete time series data. This study investigated the performance of HANTS (Harmonic ANalysis of Time Series) algorithm and (M)-SSA ((Multi-channel) Singular Spectrum Analysis) algorithm in reconstruction of wide-gap of missing data. The time series of Normalized Difference Vegetation Index (NDVI) retrieved from Landsat TM in combination with 250m MODIS NDVI time image products are used to simulate and find periodic components of the NDVI time series from 1986 to 2000 and from 2000 to 2015, respectively. This paper presents the evaluation of the performance of gap filling capability of HANTS and M-SSA by filling artificially created gaps in data using Landsat and MODIS data. The results showed that the RMSEs (Root Mean Square Errors) between the original and reconstructed data in HANTS and M-SSA algorithms were 0.027 and 0.023 NDVI value, respectively. Further, RMSEs among 15 NDVI images extracted from the time series artificially and reconstructed by HANTS and M-SSA algorithms were 0.030 and 0.025 NDVI value, respectively. RMSEs of the original and reconstructed data in HANTS and M-SSA algorithms were 0.10 and 0.04 for time series 6, respectively. The findings of this study present a favorable option for solving the missing data challenge in NDVI time series.
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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 (May 31, 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 hydrological measurements often have data gaps for different reasons. The study focused on solving a problem of gap-filling in hourly time series of soil temperature and moisture, measured at the 30 cm depth using a weighted gravitation lysimeter station while meteorological data were recorded simultaneously by a weather station. The equipment was installed in the Kulunda Steppe in the Altai Krai, Russia. Considering that climate conditions affect soil temperature and moisture content directly, we did a comparative analysis of the gap-filling performance using the three imputation methods—linear interpolation, multiple linear regression, and extended ARMA (p,q) models with exogenous climatic variables. The results showed that, according to the minimum of the mean absolute error, ARMA (p,q) models with optimally selected order parameters, and an adaptive window, had some advantages compared to other single-imputation methods. The ARMA (p,q) model produced a good quality of gap-filling in time series with the mean absolute error of 0.19 °C and 0.08 Vol. % for soil temperature and moisture content, respectively. The findings supplemented the methodology of hydrological data processing and the development of digital tools for the online monitoring of climate and soil properties in agriculture.
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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 (August 18, 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 developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.
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Zhou, Qiang, George Xian, and Hua Shi. "Gap Fill of Land Surface Temperature and Reflectance Products in Landsat Analysis Ready Data." Remote Sensing 12, no. 7 (April 9, 2020): 1192. http://dx.doi.org/10.3390/rs12071192.
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The recently released Landsat analysis ready data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy, which result in different capabilities for seasonality modeling. We present a new algorithm that focuses on data gap filling using clear observations from orbit overlap regions. Multiple linear regression models were established for each pixel time series to estimate stable predictions and uncertainties. The model’s training data came from stratified random samples based on the time series similarity between the pixel and data from the overlap regions. The algorithm was first evaluated using four tiles (5000 × 5000 30-m pixels for each tile) from 2018 land surface temperature data (LST) in Atlanta, Georgia. The accuracy was assessed using randomly masked clear observations with an average Root Mean Square Error (RMSE) of 3.88 and an average bias of −0.37, which were comparable to the product accuracy. We also applied the method on ARD surface reflectance bands at Fairbanks, Alaska. The accuracy assessment suggested a majority RMSE of less than 0.04 and a bias of less than 0.0023. The gap-filled time series can be of help for reliable seasonal modeling and reducing artifacts related to data availability. This approach can also be applied to other datasets, vegetation indexes, or spectral reflectance bands of other sensors.
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Lee, Jun-Whan, Sun-Cheon Park, Duk Kee Lee, and Jong Ho Lee. "Tsunami arrival time detection system applicable to discontinuous time series data with outliers." Natural Hazards and Earth System Sciences 16, no. 12 (December 9, 2016): 2603–22. http://dx.doi.org/10.5194/nhess-16-2603-2016.
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Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.
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Singh, Vishal, and Xiaosheng Qin. "Rainfall variability in Malay Peninsula region of Southeast Asia using gridded data." E3S Web of Conferences 81 (2019): 01002. http://dx.doi.org/10.1051/e3sconf/20198101002.
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Southeast Asia is recognized as a climate-change vulnerable region as it has been significantly affected by many extreme events in the past. This study carried out a rainfall analysis over the Malay Peninsula region of Southeast Asia utilizing historical (1981-2007) gridded rainfall datasets (0.5°×0.5°). The rainfall variability was analyzed in an intra-decadal time series duration. The uncertainty involved in all datasets was also checked based on the comparison of multiple global rainfall datasets. Rainfall gap filling analysis was conducted for producing more accurate rainfall time series after testing multiple mathematical functions. Frequency-based rainfall extreme indices such as Dry Days and Wet days are generated to assess the rainfall variability over the study area. Our results revealed a notable variation existed in the rainfalls over Malay Peninsula as per the long historical duration (1981-2007).
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Maul-Kötter, B., and Th Einfalt. "Correction and preparation of continuously measured raingauge data: a standard method in North Rhine-Westphalia." Water Science and Technology 37, no. 11 (June 1, 1998): 155–62. http://dx.doi.org/10.2166/wst.1998.0458.
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Continuous raingauge measurements are an important input variable for detailed rainfall-runoff simulation. In North Rhine-Westphalia, more than 150 continuous raingauges are used for local hydrological design through the use of site specific rainfall runoff models. Requiring gap-free data, the State Environmental Agency developed methods to use a combination of daily measurements and neighbouring continuous measurements for filling periods of lacking data in a given raindata series. The objective of such a method is to obtain plausible data for water balance simulations. For more than 3500 station years the described methodology has been applied.
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Shiff, Shilo, Itamar M. Lensky, and David J. Bonfil. "Using Satellite Data to Optimize Wheat Yield and Quality under Climate Change." Remote Sensing 13, no. 11 (May 22, 2021): 2049. http://dx.doi.org/10.3390/rs13112049.
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Climatic conditions during the grain-filling period are a major factor affecting wheat grain yield and quality. Wheat in many semi-arid and arid areas faces high-temperature stress during this period. Remote sensing can be used to monitor both crops and environmental temperature. The objective of this study was to develop a tool to optimize field management (cultivar and sowing time). Analysis of 155 cultivar experiments (from 10 growth seasons) representing different environmental conditions revealed the required degree-days for each Israeli spring wheat cultivar to reach heading (from emergence). We developed a Google Earth Engine (GEE) app to analyze time series of gap-filled 1 km MODIS land surface temperature (LSTcont). By changing the cultivar and/or emergence date in the GEE app, the farmer can “expose” each wheat field to different climatic conditions during the grain-filling period, thereafter enabling him to choose the best cultivar to be sown in the field with the right timing. This approach is expected to reduce the number of fields that suffer from heat stress during the grain-filling period. The app can be also used to assess the effects of different global warming scenarios and to plan adaptation strategies in other regions too.
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de Oliveira Santos, Cecília Lira Melo, Rubens Augusto Camargo Lamparelli, Gleyce Kelly Dantas Araújo Figueiredo, Stéphane Dupuy, Julie Boury, Ana Cláudia dos Santos Luciano, Ricardo da Silva Torres, and Guerric le Maire. "Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region." Remote Sensing 11, no. 3 (February 8, 2019): 334. http://dx.doi.org/10.3390/rs11030334.
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Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.
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Aono, Yasuyuki, and Shizuka Saito. "Clarifying springtime temperature reconstructions of the medieval period by gap-filling the cherry blossom phenological data series at Kyoto, Japan." International Journal of Biometeorology 54, no. 2 (October 23, 2009): 211–19. http://dx.doi.org/10.1007/s00484-009-0272-x.
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Chen, Yang, Ruyin Cao, Jin Chen, Licong Liu, and Bunkei Matsushita. "A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter." ISPRS Journal of Photogrammetry and Remote Sensing 180 (October 2021): 174–90. http://dx.doi.org/10.1016/j.isprsjprs.2021.08.015.
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Phan, Thi-Thu-Hong, André Bigand, and Émilie Poisson Caillault. "A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series." Applied Computational Intelligence and Soft Computing 2018 (August 9, 2018): 1–15. http://dx.doi.org/10.1155/2018/9095683.
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The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windowsQbandQa. We then find the most similar subsequence (Qbs) to the subsequence before this gapQband the most similar one (Qas) to the subsequence after the gapQa. To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window followingQbsand the one precedingQas. The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.
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Zhao, Xiaosong, and Yao Huang. "A Comparison of Three Gap Filling Techniques for Eddy Covariance Net Carbon Fluxes in Short Vegetation Ecosystems." Advances in Meteorology 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/260580.
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Missing data is an inevitable problem when measuring CO2, water, and energy fluxes between biosphere and atmosphere by eddy covariance systems. To find the optimum gap-filling method for short vegetations, we review three-methods mean diurnal variation (MDV), look-up tables (LUT), and nonlinear regression (NLR) for estimating missing values of net ecosystem CO2exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on benchmark datasets from marsh and cropland sites in China. The cumulative errors for three methods have no consistent bias trends, which ranged between −30 and +30 mgCO2 m−2from May to October at three sites. To reduce sum bias in maximum, combined gap-filling methods were selected for short vegetation. The NLR or LUT method was selected after plant rapidly increasing in spring and before the end of plant growing, and MDV method was used to the other stage. The sum relative error (SRE) of optimum method ranged between −2 and +4% for four-gap level at three sites, except for 55% gaps at soybean site, which also obviously reduced standard deviation of error.
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Kandasamy, S., F. Baret, A. Verger, P. Neveux, and M. Weiss. "A comparison of methods for smoothing and gap filling time series of remote sensing observations: application to MODIS LAI products." Biogeosciences Discussions 9, no. 12 (December 4, 2012): 17053–97. http://dx.doi.org/10.5194/bgd-9-17053-2012.
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Abstract. Moderate resolution satellite sensors including MODIS already provide more than 10 yr of observations well suited to describe and understand the dynamics of the Earth surface. However, these time series are incomplete because of cloud cover and associated with significant uncertainties. This study compares eight methods designed to improve the continuity by filling gaps and the consistency by smoothing the time course. It includes methods exploiting the time series as a whole (Iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of few weeks to few months (Adaptive Savitzky-Golay filter (SGF), temporal smoothing and gap filling (TSGF) and asymmetric Gaussian function (AGF)) in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to MODIS leaf area index product for the period 2000–2008 over 25 sites showing a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that EMD, LPF and AGF methods were failing in case of significant fraction of gaps (%Gap > 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (respectively Clim) was able to fill more than 50% of the gaps for sites with more than 60% (resp. 80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the several methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with %Gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with %Gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with %Gap < 10% and increases rapidly with %Gap. TSGF provides the more accurate estimates of phenological timing up to %Gap < 60%.
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Sarafanov, Mikhail, Eduard Kazakov, Nikolay O. Nikitin, and Anna V. Kalyuzhnaya. "A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI." Remote Sensing 12, no. 23 (November 25, 2020): 3865. http://dx.doi.org/10.3390/rs12233865.
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Satellite remote sensing has now become a unique tool for continuous and predictable monitoring of geosystems at various scales, observing the dynamics of different geophysical parameters of the environment. One of the essential problems with most satellite environmental monitoring methods is their sensitivity to atmospheric conditions, in particular cloud cover, which leads to the loss of a significant part of data, especially at high latitudes, potentially reducing the quality of observation time series until it is useless. In this paper, we present a toolbox for filling gaps in remote sensing time-series data based on machine learning algorithms and spatio-temporal statistics. The first implemented procedure allows us to fill gaps based on spatial relationships between pixels, obtained from historical time-series. Then, the second procedure is dedicated to filling the remaining gaps based on the temporal dynamics of each pixel value. The algorithm was tested and verified on Sentinel-3 SLSTR and Terra MODIS land surface temperature data and under different geographical and seasonal conditions. As a result of validation, it was found that in most cases the error did not exceed 1 °C. The algorithm was also verified for gaps restoration in Terra MODIS derived normalized difference vegetation index and land surface broadband albedo datasets. The software implementation is Python-based and distributed under conditions of GNU GPL 3 license via public repository.
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Wang, Yueqi, Zhiqiang Gao, and Jicai Ning. "An Adaptive Piecewise Harmonic Analysis Method for Reconstructing Multi-Year Sea Surface Chlorophyll-A Time Series." Remote Sensing 13, no. 14 (July 11, 2021): 2727. http://dx.doi.org/10.3390/rs13142727.
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High-quality remotely sensed satellite data series are important for many ecological and environmental applications. Unfortunately, irregular spatiotemporal samples, frequent image gaps and inevitable observational biases can greatly hinder their application. As one of the most effective gap filling and noise reduction approaches, the harmonic analysis of time series (HANTS) method has been widely used to reconstruct geographical variables; however, when applied on multi-year time series over large spatial areas, the optimal harmonic formulas are generally varied in different locations or change across different years. The question of how to choose the optimal harmonic formula is still unanswered due to the deficiency of appropriate criteria. In this study, an adaptive piecewise harmonic analysis method (AP-HA) is proposed to reconstruct multi-year seasonal data series. The method introduces a cross-validation scheme to adaptively determine the optimal harmonic model and employs an iterative piecewise scheme to better track the local traits. Whenapplied to the satellite-derived sea surface chlorophyll-a time series over the Bohai and Yellow Seas of China, the AP-HA obtains reliable reconstruction results and outperforms the conventional HANTS methods, achieving improved accuracy. Due to its generic approach to filling missing observations and tracking detailed traits, the AP-HA method has a wide range of applications for other seasonal geographical variables.
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Taki, Rezvan, Claudia Wagner-Riddle, Gary Parkin, Rob Gordon, and Andrew VanderZaag. "Comparison of two gap-filling techniques for nitrous oxide fluxes from agricultural soil." Canadian Journal of Soil Science 99, no. 1 (March 1, 2019): 12–24. http://dx.doi.org/10.1139/cjss-2018-0041.
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Micrometeorological methods are ideally suited for continuous measurements of N2O fluxes, but gaps in the time series occur due to low-turbulence conditions, power failures, and adverse weather conditions. Two gap-filling methods including linear interpolation and artificial neural networks (ANN) were utilized to reconstruct missing N2O flux data from a corn–soybean–wheat rotation and evaluate the impact on annual N2O emissions from 2001 to 2006 at the Elora Research Station, ON, Canada. The single-year ANN method is recommended because this method captured flux variability better than the linear interpolation method (average R2 of 0.41 vs. 0.34). Annual N2O emission and annual bias resulting from linear and single-year ANN were compatible with each other when there were few and short gaps (i.e., percentage of missing values <30%). However, with longer gaps (>20 d), the bias error in annual fluxes varied between 0.082 and 0.344 kg N2O-N ha−1 for linear and 0.069 and 0.109 kg N2O-N ha−1 for single-year ANN. Hence, the single-year ANN with lower annual bias and stable approach over various years is recommended, if the appropriate driving inputs (i.e., soil temperature, soil water content, precipitation, N mineral content, and snow depth) needed for the ANN model are available.
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Kandasamy, S., F. Baret, A. Verger, P. Neveux, and M. Weiss. "A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products." Biogeosciences 10, no. 6 (June 20, 2013): 4055–71. http://dx.doi.org/10.5194/bg-10-4055-2013.
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Abstract. Moderate resolution satellite sensors including MODIS (Moderate Resolution Imaging Spectroradiometer) already provide more than 10 yr of observations well suited to describe and understand the dynamics of earth's surface. However, these time series are associated with significant uncertainties and incomplete because of cloud cover. This study compares eight methods designed to improve the continuity by filling gaps and consistency by smoothing the time course. It includes methods exploiting the time series as a whole (iterative caterpillar singular spectrum analysis (ICSSA), empirical mode decomposition (EMD), low pass filtering (LPF) and Whittaker smoother (Whit)) as well as methods working on limited temporal windows of a few weeks to few months (adaptive Savitzky–Golay filter (SGF), temporal smoothing and gap filling (TSGF), and asymmetric Gaussian function (AGF)), in addition to the simple climatological LAI yearly profile (Clim). Methods were applied to the MODIS leaf area index product for the period 2000–2008 and over 25 sites showed a large range of seasonal patterns. Performances were discussed with emphasis on the balance achieved by each method between accuracy and roughness depending on the fraction of missing observations and the length of the gaps. Results demonstrate that the EMD, LPF and AGF methods were failing because of a significant fraction of gaps (more than 20%), while ICSSA, Whit and SGF were always providing estimates for dates with missing data. TSGF (Clim) was able to fill more than 50% of the gaps for sites with more than 60% (80%) fraction of gaps. However, investigation of the accuracy of the reconstructed values shows that it degrades rapidly for sites with more than 20% missing data, particularly for ICSSA, Whit and SGF. In these conditions, TSGF provides the best performances that are significantly better than the simple Clim for gaps shorter than about 100 days. The roughness of the reconstructed temporal profiles shows large differences between the various methods, with a decrease of the roughness with the fraction of missing data, except for ICSSA. TSGF provides the smoothest temporal profiles for sites with a % gap > 30%. Conversely, ICSSA, LPF, Whit, AGF and Clim provide smoother profiles than TSGF for sites with a % gap < 30%. Impact of the accuracy and smoothness of the reconstructed time series were evaluated on the timing of phenological stages. The dates of start, maximum and end of the season are estimated with an accuracy of about 10 days for the sites with a % gap < 10% and increases rapidly with the % gap. TSGF provides more accurate estimates of phenological timing up to a % gap < 60%.
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Pipia, Luca, Eatidal Amin, Santiago Belda, Matías Salinero-Delgado, and Jochem Verrelst. "Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine." Remote Sensing 13, no. 3 (January 24, 2021): 403. http://dx.doi.org/10.3390/rs13030403.
Full textAbstract:
For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.
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Sakowska, K., L. Vescovo, B. Marcolla, R. Juszczak, J. Olejnik, and D. Gianelle. "Monitoring of carbon dioxide fluxes in a subalpine grassland ecosystem of the Italian Alps using a multispectral sensor." Biogeosciences 11, no. 17 (September 8, 2014): 4695–712. http://dx.doi.org/10.5194/bg-11-4695-2014.
Full textAbstract:
Abstract. The study investigates the potential of a commercially available proximal sensing system – based on a 16-band multispectral sensor – for monitoring mean midday gross ecosystem production (GEPm) in a subalpine grassland of the Italian Alps equipped with an eddy covariance flux tower. Reflectance observations were collected for 5 consecutive years, characterized by different climatic conditions, together with turbulent carbon dioxide fluxes and their meteorological drivers. Different models based on linear regression (vegetation indices approach) and on multiple regression (reflectance approach) were tested to estimateGEPm from optical data. The overall performance of this relatively low-cost system was positive. Chlorophyll-related indices including the red-edge part of the spectrum in their formulation (red-edge normalized difference vegetation index, NDVIred-edge; chlorophyll index, CIred-edge) were the best predictors of GEPm, explaining most of its variability during the observation period. The use of the reflectance approach did not lead to considerably improved results in estimating GEPm: the adjusted R2 (adjR2) of the model based on linear regression – including all the 5 years – was 0.74, while the adjR2 for the multiple regression model was 0.79. Incorporating mean midday photosynthetically active radiation (PARm) into the model resulted in a general decrease in the accuracy of estimates, highlighting the complexity of the GEPm response to incident radiation. In fact, significantly higher photosynthesis rates were observed under diffuse as regards direct radiation conditions. The models which were observed to perform best were then used to test the potential of optical data for GEPm gap filling. Artificial gaps of three different lengths (1, 3 and 5 observation days) were introduced in the GEPm time series. The values of adjR2 for the three gap-filling scenarios showed that the accuracy of the gap filling slightly decreased with gap length. However, on average, the GEPm gaps were filled with an accuracy of 73% with the model fed with NDVIred-edge, and of 76% with the model using reflectance at 681, 720 and 781 nm and PARm data.