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19-year remotely sensed data in the forecast of spectral models of the environment

International Journal of Digital Earth 2021年第8期14卷 1040-1066页

作者： Fernando Saragosa Rossi Carlos Antonio da Silva Junior JoséFrancisco de Oliveira-Júnior Paulo Eduardo Teodoro Luciano Shozo Shiratsuchi mendelson lima Larissa Pereira Ribeiro Teodoro Auana Vicente Tiago Guilherme Fernando Capristo-Silva State University of Mato Grosso(UNEMAT) Alta FlorestaMato GrossoBrazil Department of Geography State University of Mato Grosso(UNEMAT)SinopMato GrossoBrazil cFederal University of Alagoas(UFAL) MaceióAlagoasBrazil dFederal University of Mato Grosso do Sul(UFMS) Chapadão do SulMato Grosso do SulBrazil eAgCenter School of PlantEnvironmentaland Soil SciencesLouisiana State University(LSU)Baton RougeLouisianaUSA fPostgraduate Program in Agronomy Federal University of Mato Grosso(UFMT)SinopMato GrossoBrazil

The aims of this study were:i)to compare no-till areas in two municipalities located in different regions of Brazil,along with the influence on CO_(2)Flux and GPP,and ii)to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios(ARIMA time-series model number).The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS,both in Brazil.A time series of 19 years was performed with data acquired by remote sensing from the following satellites:i)Landsat-8(OLI and TIRS),and ii)TERRA/AQUA(MODIS).The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time,with a satisfactory correlation in future predictions of the variables for the next ten years,in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality,and CO_(2)Flux tends to decrease in its seasonality,indicating a higher CO_(2) absorption.

关键词： ARIMA numerical modeling remote sensing orbital products tillage

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