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
Keywords:
GSOD / MaxEnt / MODIS LST / Spatio-temporal analysis / Daily temperature interpolation / Global space-time kriging model
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
UR - conv_1779
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",
url = "conv_1779"
}
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
conv_1779
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
conv_1779 .
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 .,
conv_1779 .
