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The assessment of carbon dynamics in terrestrial ecosystems is of great importance in global climate change research. Caatinga is an exclusively Brazilian biome, and quite lacking in studies, particularly of carbon sequestered by biome. A major difficulty for studies is a function of no observation data in micrometeorological station. The hyperspectral and multispectral remote sensing become increasingly feasible to evaluate the dynamics of carbon in ecosystems, in particular in this Caatinga study area. The objective of this study was to apply correlation analysis and multiple regression to find a relationship between gross primary production (GPP) and vegetation cover index in the Caatinga biome, located in vicinities of Petrolina city, Pernambuco State, Brazil. It was used a HandHeldFieldSpec of ASD to obtain the reflectance data of plant canopy and the vegetation indices. The GPP data were obtained in micrometeorological tower installed into a preserved area of Caatinga biome. The results showed that those vegetation indexes which take water into consideration presented higher correlation with GPP, r2=0.95. We conclude that it is possible to obtain GPP using only data of vegetation indexes estimated with remote sensing.