Comparison of spatial-temporal analysis modelling with purely spatial analysis modelling using temperature data obtained by remote sensing
¹Federal University of Lavras, Department of Agricultural Engineering, University Campus, BR37.200-000 Lavras-MG, Brazil
²Institute of Applied Economic Research- IPEA, Rio de Janeiro, BR 20071-900
Rio de Janeiro, Brazil
³Geoprocessing Analyst, Bracell, Lençois Paulistas, BR17120-000 São Paulo, Brazil
⁴University of Firenze, Department of Agriculture, Food, Environment and Forestry, Via San Bonaventura, 13, Firenze, Italy
*Correspondence: luanna_mendess@yahoo.com.br
Abstract:
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.
Key words:
climatic elements, geostatistics, mathematical modelling