Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation
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
Keywords:
GSOD / MaxEnt / MODIS LST / Spatio-temporal analysis / Daily temperature interpolation / Global space-time kriging modelSource:
Spatial Statistics, 2015, 14, 22-38Publisher:
- Elsevier
Funding / projects:
- Studying climate change and its influence on environment: impacts, adaptation and mitigation (RS-43007)
- The role and implementation of the national spatial plan and regional development documents in renewal of strategic research, thinking and governance in Serbia (RS-47014)
- Croatian Science Foundation 2831
- Spatial, environmental, energy and social aspects of developing settlements and climate change - mutual impacts (RS-36035)
DOI: 10.1016/j.spasta.2015.04.005
ISSN: 2211-6753
WoS: 000368912700003
Scopus: 2-s2.0-84947862977
Institution/Community
GraFarTY - 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 . .