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Earth observations (EO) are nowadays a powerful tool to evaluate vegetation systems as crops to reach Sustainable Development Goals (SDGs) of the agenda 2030. Normalized Difference Vegetation Index (NDVI) is a popular and widespread index in remote sensing to evaluate vegetation dynamics. However, analytical advances of NDVI long term series analysis are towards understanding complex relations of atmosphere-plant-soil system through temporal and scaling behaviour. Hence, this research presents the generalized structure function (GSF) and Hurst exponent as innovative analytical methods to explore a satellite-based network of NDVI measurements and precipitation series in cereals in the semi-arid. Results suggest that weather support anti-persistence structure of NDVI time series sinceweather regime in semi-arid is essential in the understanding of complex processes of the crop growth. Mathematical description of NDVI series coupled with GSF and Hurst exponent can reinforce crop modelling future purposes.