TY  - JOUR
AU  - Tadić-Percec, Melita
AU  - Kilibarda, Milan
PY  - 2018
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/954
PB  - Geofizicki Zavod
T2  - Geofizika
T1  - Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"
EP  - 224
IS  - 2
SP  - 223
VL  - 34
UR  - https://hdl.handle.net/21.15107/rcub_grafar_954
ER  - 

@article{
author = "Tadić-Percec, Melita and Kilibarda, Milan",
year = "2018",
publisher = "Geofizicki Zavod",
journal = "Geofizika",
title = "Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"",
pages = "224-223",
number = "2",
volume = "34",
url = "https://hdl.handle.net/21.15107/rcub_grafar_954"
}

Tadić-Percec, M.,& Kilibarda, M.. (2018). Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences". in Geofizika
Geofizicki Zavod., 34(2), 223-224.
https://hdl.handle.net/21.15107/rcub_grafar_954

Tadić-Percec M, Kilibarda M. Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences". in Geofizika. 2018;34(2):223-224.
https://hdl.handle.net/21.15107/rcub_grafar_954 .

Tadić-Percec, Melita, Kilibarda, Milan, "Preface to special issue "GeoMLA Conference - Geostatistics and Machine Learning Applications in Climate and Environmental Sciences"" in Geofizika, 34, no. 2 (2018):223-224,
https://hdl.handle.net/21.15107/rcub_grafar_954 .

TY  - JOUR
AU  - Kilibarda, Milan
AU  - Tadić-Percec, Melita
AU  - Hengl, Tomislav
AU  - Luković, Jelena
AU  - Bajat, Branislav
PY  - 2015
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/695
AB  - This article highlights the results of an assessment of representation and usability of global temperature station data for global spatio-temporal analysis. Datasets from the Global Surface Summary of Day (GSOD) and the European Climate Assessment & Dataset (ECA&D) were merged and consisted of 10,695 global stations for the year 2011. Three aspects of data quality were considered: (a) representation in the geographical domain, (b) representation in the feature space (based on the MaxEnt method), and
PB  - Elsevier
T2  - Spatial Statistics
T1  - Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
EP  - 38
SP  - 22
VL  - 14
DO  - 10.1016/j.spasta.2015.04.005
ER  - 

@article{
author = "Kilibarda, Milan and Tadić-Percec, Melita and Hengl, Tomislav and Luković, Jelena and Bajat, Branislav",
year = "2015",
abstract = "This article highlights the results of an assessment of representation and usability of global temperature station data for global spatio-temporal analysis. Datasets from the Global Surface Summary of Day (GSOD) and the European Climate Assessment & Dataset (ECA&D) were merged and consisted of 10,695 global stations for the year 2011. Three aspects of data quality were considered: (a) representation in the geographical domain, (b) representation in the feature space (based on the MaxEnt method), and",
publisher = "Elsevier",
journal = "Spatial Statistics",
title = "Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation",
pages = "38-22",
volume = "14",
doi = "10.1016/j.spasta.2015.04.005"
}

Kilibarda, M., Tadić-Percec, M., Hengl, T., Luković, J.,& Bajat, B.. (2015). Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation. in Spatial Statistics
Elsevier., 14, 22-38.
https://doi.org/10.1016/j.spasta.2015.04.005

Kilibarda M, Tadić-Percec M, Hengl T, Luković J, Bajat B. Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation. in Spatial Statistics. 2015;14:22-38.
doi:10.1016/j.spasta.2015.04.005 .

Kilibarda, Milan, Tadić-Percec, Melita, Hengl, Tomislav, Luković, Jelena, Bajat, Branislav, "Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation" in Spatial Statistics, 14 (2015):22-38,
https://doi.org/10.1016/j.spasta.2015.04.005 . .

TY  - JOUR
AU  - Kilibarda, Milan
AU  - Hengl, Tomislav
AU  - Heuvelink, Gerard B. M.
AU  - Graeler, Benedikt
AU  - Pebesma, Edzer
AU  - Tadić-Percec, Melita
AU  - Bajat, Branislav
PY  - 2014
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/639
AB  - Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points  Global spatio-temporal regression-kriging daily temperature interpolation   Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures   Time series of MODIS 8 day images as explanatory variables in regression part
PB  - Wiley-Blackwell
T2  - Journal of Geophysical Research-Atmospheres
T1  - Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution
EP  - 2313
IS  - 5
SP  - 2294
VL  - 119
DO  - 10.1002/2013JD020803
ER  - 

@article{
author = "Kilibarda, Milan and Hengl, Tomislav and Heuvelink, Gerard B. M. and Graeler, Benedikt and Pebesma, Edzer and Tadić-Percec, Melita and Bajat, Branislav",
year = "2014",
abstract = "Combined Global Surface Summary of Day and European Climate Assessment and Dataset daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1km for the global land mass. Predictions in space and time were made for the mean, maximum, and minimum temperatures using spatio-temporal regression-kriging with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) 8 day images, topographic layers (digital elevation model and topographic wetness index), and a geometric temperature trend as covariates. The accuracy of predicting daily temperatures was assessed using leave-one-out cross validation. To account for geographical point clustering of station data and get a more representative cross-validation accuracy, predicted values were aggregated to blocks of land of size 500x500km. Results show that the average accuracy for predicting mean, maximum, and minimum daily temperatures is root-mean-square error (RMSE) =2 degrees C for areas densely covered with stations and between 2 degrees C and 4 degrees C for areas with lower station density. The lowest prediction accuracy was observed at high altitudes (>1000m) and in Antarctica with an RMSE around 6 degrees C. The model and predictions were built for the year 2011 only, but the same methodology could be extended for the whole range of the MODIS land surface temperature images (2001 to today), i.e., to produce global archives of daily temperatures (a next-generation repository) and to feed various global environmental models. Key Points  Global spatio-temporal regression-kriging daily temperature interpolation   Fitting of global spatio-temporal models for the mean, maximum, and minimum temperatures   Time series of MODIS 8 day images as explanatory variables in regression part",
publisher = "Wiley-Blackwell",
journal = "Journal of Geophysical Research-Atmospheres",
title = "Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution",
pages = "2313-2294",
number = "5",
volume = "119",
doi = "10.1002/2013JD020803"
}

Kilibarda, M., Hengl, T., Heuvelink, G. B. M., Graeler, B., Pebesma, E., Tadić-Percec, M.,& Bajat, B.. (2014). Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. in Journal of Geophysical Research-Atmospheres
Wiley-Blackwell., 119(5), 2294-2313.
https://doi.org/10.1002/2013JD020803

Kilibarda M, Hengl T, Heuvelink GBM, Graeler B, Pebesma E, Tadić-Percec M, Bajat B. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. in Journal of Geophysical Research-Atmospheres. 2014;119(5):2294-2313.
doi:10.1002/2013JD020803 .

Kilibarda, Milan, Hengl, Tomislav, Heuvelink, Gerard B. M., Graeler, Benedikt, Pebesma, Edzer, Tadić-Percec, Melita, Bajat, Branislav, "Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution" in Journal of Geophysical Research-Atmospheres, 119, no. 5 (2014):2294-2313,
https://doi.org/10.1002/2013JD020803 . .