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Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing

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dc.contributor.authorDos Santos, L. M.
dc.contributor.authorFerraz, G.A.S.
dc.contributor.authorAlves, H.J.P.
dc.contributor.authorRodrigues, J.D.P.
dc.contributor.authorCamiciottoli, S.
dc.contributor.authorConti, L.
dc.contributor.authorRossi, G.
dc.date.accessioned2021-10-13T07:40:52Z
dc.date.available2021-10-13T07:40:52Z
dc.date.issued2021
dc.descriptionReceived: March 25th, 2021 ; Accepted: May 20th, 2021 ; Published: October 5th, 2021 ; Correspondence: luanna_mendess@yahoo.com.breng
dc.description.abstractVariations in climatic elements directly affect the productivity of agricultural activities. Temperature is one of the climatic elements that varies in space and time.Therefore, understanding spatial variations in temperature is essential for many activities. Given the above, the objective of this work was to compare the performance of the proposed spatiotemporal analysis model with that of purely spatial analysis using temperature data obtained by remote sensing. The experimental data were arranged in a grid with 403 spatial locations, with 22 samples collected in a 24-hour period. The statistical software R Core Team (2020) was used to perform the analysis. The packages used in the analyses were ‘geoR’, ‘CompRandFld’, ‘scatterplot3d’, and ‘fields’. For making the maps, the software ArcGIS was used. The behavioural analysis of spatiotemporal dependence indicated, through the covariogram graph of the data, that there is a strong spatial dependence. For the cases of purely spatial analysis of phenomena, a separate spatial model for each time is justified because this type of model presents a smaller prediction error and requires simpler processing than the space-time model. It was possible to compare the space-time analysis with the purely spatial analysis using temperature data obtained by remote sensing images. The data modelled with the purely spatial analysis had, on average, lower error than those with the space-time model.eng
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2021, vol. 19, no. 3, pp. 1423–1435eng
dc.identifier.urihttp://hdl.handle.net/10492/7015
dc.identifier.urihttps://doi.org/10.15159/ar.21.141
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ; openAccesseng
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectclimatic elementseng
dc.subjectgeostatisticseng
dc.subjectmathematical modellingeng
dc.subjectarticleseng
dc.titleComparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensingeng
dc.typeArticleeng

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2021, Vol. 19, No. 3