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

Vaata/ Ava
Aasta
2021Autor
Dos Santos, L. M.
Ferraz, G.A.S.
Alves, H.J.P.
Rodrigues, J.D.P.
Camiciottoli, S.
Conti, L.
Rossi, G.
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Variations 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.