@InProceedings{SchultzFerMarFraGue:2019:MoMiSa,
               author = "Schultz, Bruno and Ferreira, Renato Martins Passos and Marcari 
                         J{\'u}nior, Etore and Franchito, Cecare{\c{c}}{\c{c}}o. Izabel 
                         and Guerra, J{\'u}lio Bandeira",
          affiliation = "{Geoambiente Sensoriamento Remoto} and {Geoambiente Sensoriamento 
                         Remoto} and {Geoambiente Sensoriamento Remoto} and {Geoambiente 
                         Sensoriamento Remoto} and {Geoambiente Sensoriamento Remoto}",
                title = "DataSafra: monitoramento de milho safrinha no Mato Grosso por 
                         sensoriamento remoto e Google Earth Engine",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "2933--2936",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Landsat-like, s{\'e}ries temporais, multisensor, safras, 
                         Landsat-like, temporal series, multisensor, crop cycles.",
             abstract = "Atualmente, no Brasil, n{\~a}o dispomos de 
                         informa{\c{c}}{\~o}es obtidas de forma r{\'a}pida sobre o 
                         monitoramento das safras de milho safrinha. Para isto, a 
                         Geoambiente vem desenvolvendo o projeto chamado DataSafra, que 
                         visa atender esse nicho espec{\'{\i}}fico do mercado 
                         agr{\'{\i}}cola brasileiro. Os levantamentos iniciais do 
                         DataSafra foram realizados sobre o estado do Mato Grosso, e para 
                         isso, foram mapeados talh{\~o}es de milho safrinha de oito safras 
                         (2010 a 2017) e estimadas as datas de plantio destes talh{\~o}es. 
                         Os dados sobre data de plantio foram levantados a partir das 
                         s{\'e}ries temporais filtradas multi-sensor, em Google Earth 
                         Engine. Os resultados sobre data de plantio foram correlacionados 
                         com dados de precipita{\c{c}}{\~a}o do CHIRPS. De acordo com os 
                         resultados obtidos, o tamanho m{\'e}dio dos talh{\~o}es de milho 
                         safrinha foi de 120,23 Ha. O plantio de milho safrinha n{\~a}o 
                         {\'e} ditado por um {\'u}nico padr{\~a}o temporal e existe 
                         correla{\c{c}}{\~a}o entre taxa de precipita{\c{c}}{\~a}o 
                         acumulada e data de plantio, por dec{\^e}ndio do ano safra. 
                         ABSTRACT: Currently, in Brazil, we do not have informationon the 
                         maize monitoring obtained in a fast and timely way. For this, 
                         Geoambiente has been developing the Project called DataSafra that 
                         aims meetting this specific niche of agricultural Brazilian 
                         market. DataSafra's initial surveys were carried out on the state 
                         of Mato Grosso, and for this purpose, safrinha maize from nine 
                         cycle crops (2011 to 2017) were mapped and the planting dates of 
                         these fields were estimated. Planting date data were collected 
                         from the multi-sensor filtered time series in Google Earth Engine. 
                         Results on planting date were correlated with precipitation data 
                         from CHIRPS. According to the results obtained, the average size 
                         of the stands of safrinha maize was 120.23 Ha, the planting of 
                         safrinha maize is not dictatedby a single temporal pattern and 
                         there is a correlation between the accumulated rainfall rate and 
                         the planting date per decay of the year.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3UA44KL",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3UA44KL",
           targetfile = "97862.pdf",
                 type = "Produ{\c{c}}{\~a}o e previs{\~a}o agr{\'{\i}}cola",
        urlaccessdate = "13 maio 2024"
}