@Article{OréAGOYTCBCLMGH:2020:CrGrMo,
               author = "Or{\'e}, Gian and Alc{\^a}ntara, Marlon S. and G{\'o}es, 
                         Juliana A. and Oliveira, Luciano P. and Yepes, Jhonnatan and 
                         Teruel, B{\'a}rbara and Castro, Valqu{\'{\i}}ria and Bins, 
                         Leonardo Sant'Anna and Castro, Felicio and Luebeck, Dieter and 
                         Moreira, Laila F. and Gabrielli, Lucas H. and Hernandez-Figueroa, 
                         Hugo E.",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Universidade 
                         Estadual de Campinas (UNICAMP)} and {Universidade Estadual de 
                         Campinas (UNICAMP)} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Universidade Estadual de Campinas (UNICAMP)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Universidade 
                         Estadual de Campinas (UNICAMP)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Radaz Ind{\'u}stria e Com{\'e}rcio de Produtos 
                         Eletr{\^o}nicos Ltda} and {Radaz Ind{\'u}stria e Com{\'e}rcio 
                         de Produtos Eletr{\^o}nicos Ltda} and {} and {Universidade 
                         Estadual de Campinas (UNICAMP)}",
                title = "Crop growth monitoring with drone-borne DInSAR",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "4",
                pages = "e615",
                month = "Feb.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 2: Fome zero e Agricultura 
                         sustent{\'a}vel}",
             keywords = "differential interferometry, DInSAR, precision agriculture, 
                         drone-borne radar, crop growth deficit map.",
             abstract = "Accurate, high-resolution maps of for crop growth monitoring are 
                         strongly needed by precision agriculture. The information source 
                         for such maps has been supplied by satellite-borne radars and 
                         optical sensors, and airborne and drone-borne optical sensors. 
                         This article presents a novel methodology for obtaining growth 
                         deficit maps with an accuracy down to 5 cm and a spatial 
                         resolution of 1 m, using differential synthetic aperture radar 
                         interferometry (DInSAR). Results are presented with measurements 
                         of a drone-borne DInSAR operating in three bandsP, L and C. The 
                         decorrelation time of L-band for coffee, sugar cane and corn, and 
                         the feasibility for growth deficit maps generation are discussed. 
                         A model is presented for evaluating the growth deficit of a corn 
                         crop in L-band, starting with 50 cm height. This work shows that 
                         the drone-borne DInSAR has potential as a complementary tool for 
                         precision agriculture.",
                  doi = "10.3390/rs12040615",
                  url = "http://dx.doi.org/10.3390/rs12040615",
                 issn = "2072-4292",
             language = "en",
           targetfile = "ore_crop.pdf",
        urlaccessdate = "28 abr. 2024"
}