@InProceedings{HansenPSZAPLA:2019:ToGlMo,
               author = "Hansen, Matthew and Potapov, Peter and Song, Xiao Peng and Zalles, 
                         Viviana and Adusei, Bernard and Pickering, Jeffery and Lima, 
                         Andr{\'e} and Adami, Marcos",
          affiliation = "{University of Maryland} and {University of Maryland} and 
                         {Univeristy of Maryland} and {University of Maryland} and 
                         {Univeristy of Maryland} and {Univeristy of Maryland} and 
                         {University of Maryland} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Towards global monitoring of key commodity crops using 
                         multi-source data",
                 year = "2019",
         organization = "AGU Fall Meeting",
             abstract = "Improved time-series data sets and synergistic mapping and 
                         sampling methods enable large-scale monitoring and area estimation 
                         of key commodity crops such as soybean, maize and wheat. In this 
                         presentation, a generic method is presented that employs turn-key 
                         algorithms to target crop types for probability-based allocation 
                         of samples of reference data which are used to generate within 
                         season area estimates. The method includes field data and freely 
                         available Landsat and Sentinel 2 time-series imagery. PlanetScope 
                         data are presented for a highly heterogeneous landscape of 
                         intensive smallholder production, highlighting both the spatial 
                         and temporal detail of Planet imagery. Results illustrate the 
                         utility of remotely sensed data to facilitate unbiased crop type 
                         area estimates with low uncertainties. The ability to employ the 
                         same method across all major growing regions promises a more 
                         consistent global reporting capability. Importantly, yield data 
                         may be collected in a similar manner, allowing for production 
                         estimates from an internally consistent, large-scale methodology. 
                         Results will be shown for soybean and corn in the United States, 
                         soybean in South America, corn in China, and wheat in Pakistan.",
  conference-location = "San Francisco, CA",
      conference-year = "09-13 dec.",
             language = "en",
           targetfile = "hansen_towards.pdf",
        urlaccessdate = "30 abr. 2024"
}