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The main objective of this research was to use time-series images with medium spatial resolution and values with maximum and minimum vegetation index relating to the dynamic aspects of the soybean crop in Brazil via Earth Engine and making the data query available on-line high performance. Three sensors (MODIS, OLI and MSI) and cloud-based JavaScript processing were used for time-series composition. The largest soybean areas were detected in the Center-West and Southern regions of Brazil. Automated monitoring via Earth Engine was satisfactory, showing the ability to identify in near real-time what areas were grown with soybeans after harvesting.

  • 1 Universidade do Estado de Mato Grosso - UNEMAT
  • 2 Universidade do Estado de Mato Grosso
  • 3 Universidade Estadual do Oeste do Paraná - Câmpus Cascavel
  • 4 Universidade Federal de Alagoas
  • 5 Universidade Federal de Mato Grosso do Sul
  • 6 Louisiana State University
  • 7 Institute of Space Technology / University of Peshawar
  • 8 Universidade Federal Fluminense
  • 9 Departamento de Agronomia / Universidade Estadual de Maringá
  • Time series analysis of remote sensing data
Earth Engine
automated mapping