Zobrazeno 1 - 10
of 28
pro vyhledávání: '"spatio-temporal geostatistics"'
Publikováno v:
Sensors, Vol 21, Iss 14, p 4717 (2021)
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urb
Externí odkaz:
https://doaj.org/article/65edd8bbc2eb4086b7db6328ece4c710
Autor:
Zhao-Cheng Zeng, Liping Lei, Kimberly Strong, Dylan B. A. Jones, Lijie Guo, Min Liu, Feng Deng, Nicholas M. Deutscher, Manvendra K. Dubey, David W. T. Griffith, Frank Hase, Bradley Henderson, Rigel Kivi, Rodica Lindenmaier, Isamu Morino, Justus Notholt, Hirofumi Ohyama, Christof Petri, Ralf Sussmann, Voltaire A. Velazco, Paul O. Wennberg, Hui Lin
Publikováno v:
International Journal of Digital Earth, Vol 10, Iss 4, Pp 426-456 (2017)
This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between obs
Externí odkaz:
https://doaj.org/article/ab9c35932419458d87efc856d069ac4c
Autor:
Mateusz Zaręba, Tomasz Danek
Publikováno v:
Sensors, Vol 21, Iss 5208, p 5208 (2021)
Sensors
Volume 21
Issue 15
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 15
Sensors (Basel, Switzerland)
In this paper, we present a detailed analysis of the public data provided by low-cost sensors (LCS), which were used for spatial and temporal studies of air quality in Krakow. A PM (particulate matter) dataset was obtained in spring in 2021, during w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5070278be87ebe868faa117187b32ac1
https://pubmed.ncbi.nlm.nih.gov/34372442
https://pubmed.ncbi.nlm.nih.gov/34372442
Publikováno v:
Sensors, Vol 21, Iss 4717, p 4717 (2021)
Sensors
Volume 21
Issue 14
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 14
Sensors (Basel, Switzerland)
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d555892fcff6c63637e232b9c7832763
https://www.mdpi.com/1424-8220/21/14/4717
https://www.mdpi.com/1424-8220/21/14/4717
Publikováno v:
Remote Sensing, Vol 8, Iss 5, p 361 (2016)
The aim of this study is to evaluate the Greenhouse gases Observation SATellite (GOSAT) column-averaged CH4 dry air mole fraction (XCH4) data by using geostatistical analysis and conducting comparisons with model simulations and surface emissions. Fi
Externí odkaz:
https://doaj.org/article/9ff96c1d1c8c413ba4dcc11874bb0853
Akademický článek
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Akademický článek
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Publikováno v:
Repositori Universitat Jaume I
Universitat Jaume I
Universitat Jaume I
Problem statement: Obtaining new and flexible classes of nonseparable spatio-temporal covariances have resulted in a key point of research in the last years within the context of spatiotemporal Geostatistics. Approach: In general, the literature has
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bab3a86de93ae79fbdfbb113999209f
https://doi.org/10.3844/jmssp.2011.37.44
https://doi.org/10.3844/jmssp.2011.37.44
Publikováno v:
Remote Sensing, Vol 8, Iss 5, p 361 (2016)
Remote Sensing
Volume 8
Issue 5
Pages: 361
Remote Sensing
Volume 8
Issue 5
Pages: 361
The aim of this study is to evaluate the Greenhouse gases Observation SATellite (GOSAT) column-averaged CH4 dry air mole fraction (XCH4) data by using geostatistical analysis and conducting comparisons with model simulations and surface emissions. Fi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c734740ebb5e52e19a062bf7f22a2dd3
http://www.mdpi.com/2072-4292/8/5/361
http://www.mdpi.com/2072-4292/8/5/361
Publikováno v:
Spatial Statistics
Spatial Statistics, Elsevier, 2016, 18, pp.125-146. ⟨10.1016/j.spasta.2016.02.004⟩
Spatial Statistics, Elsevier, 2016, 18, pp.125-146. ⟨10.1016/j.spasta.2016.02.004⟩
Multivariate space–time data are increasingly available in various scientific disciplines. When analyzing these data, one of the key issues is to describe the multivariate space–time dependences. Under the Gaussian framework, one needs to propose
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8f7285f81975c5e35ad2a679edda1f8
https://hal.archives-ouvertes.fr/hal-01509041
https://hal.archives-ouvertes.fr/hal-01509041
