TY  - JOUR
AU  - Kilibarda, Milan
AU  - Percec-Tadić, Melita
AU  - Hengl, Tomislav
AU  - Luković, Jelena
AU  - Bajat, Branislav
PY  - 2015
UR  - https://gery.gef.bg.ac.rs/handle/123456789/700
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 (c) usability i.e. fitness of use for spatio-temporal interpolation based on cross-validation of spatio-temporal regression-kriging models. The results indicate significant clustering of meteorological stations in the combined data set in both geographical and feature space. The majority of the distribution of stations (84%) can be explained by population density and accessibility maps. Consequently, higher elevations areas and inaccessible areas that are sparsely populated are significantly under-represented. Under-representation also reflects on the results of spatio-temporal analysis. Spatio-temporal regression-kriging model of mean daily temperature using 8-day MODIS LST images, as covariate, produces average global accuracy of 2-3 degrees C. Prediction of temperature for polar areas and mountains is 2 times lower than for areas densely covered with meteorological stations. Balanced spatio-temporal regression models that account for station clustering are suggested.
PB  - Elsevier Sci Ltd, Oxford
T2  - Spatial Statistics
T1  - Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
VL  - 14
SP  - 22
EP  - 38
DO  - 10.1016/j.spasta.2015.04.005
UR  - https://hdl.handle.net/21.15107/rcub_gery_700
ER  - 

@article{
author = "Kilibarda, Milan and Percec-Tadić, 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 (c) usability i.e. fitness of use for spatio-temporal interpolation based on cross-validation of spatio-temporal regression-kriging models. The results indicate significant clustering of meteorological stations in the combined data set in both geographical and feature space. The majority of the distribution of stations (84%) can be explained by population density and accessibility maps. Consequently, higher elevations areas and inaccessible areas that are sparsely populated are significantly under-represented. Under-representation also reflects on the results of spatio-temporal analysis. Spatio-temporal regression-kriging model of mean daily temperature using 8-day MODIS LST images, as covariate, produces average global accuracy of 2-3 degrees C. Prediction of temperature for polar areas and mountains is 2 times lower than for areas densely covered with meteorological stations. Balanced spatio-temporal regression models that account for station clustering are suggested.",
publisher = "Elsevier Sci Ltd, Oxford",
journal = "Spatial Statistics",
title = "Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation",
volume = "14",
pages = "22-38",
doi = "10.1016/j.spasta.2015.04.005",
url = "https://hdl.handle.net/21.15107/rcub_gery_700"
}

Kilibarda, M., Percec-Tadić, 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 Sci Ltd, Oxford., 14, 22-38.
https://doi.org/10.1016/j.spasta.2015.04.005
https://hdl.handle.net/21.15107/rcub_gery_700

Kilibarda M, Percec-Tadić 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
https://hdl.handle.net/21.15107/rcub_gery_700 .

Kilibarda, Milan, Percec-Tadić, 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 .,
https://hdl.handle.net/21.15107/rcub_gery_700 .