@Article{MacielSAVBSSSDFCS:2020:LeWaPo,
               author = "Maciel, Daniel Andrade and Silva, V{\^a}nia Aparecida and Alves, 
                         Helena Maria Ramos and Volpato, Margarete Marin Lordelo and 
                         Barbosa, Jo{\~a}o Paulo Rodrigues Alves de and Souza, Vanessa 
                         Cristina Oliveira de and Santos, Meline Oliveira and Silveira, 
                         Helbert Rezende de Oliveira and Dantas, Mayara Fontes and Freitas, 
                         Ana Fl{\'a}via de and Carvalho, Gladyston Rodrigues and Santos, 
                         Jacqueline Oliveira dos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Empresa de 
                         Pesquisa Agropecu{\'a}ria de Minas Gerais (EPAMIG)} and {Empresa 
                         Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Empresa 
                         de Pesquisa Agropecu{\'a}ria de Minas Gerais (EPAMIG)} and 
                         {Universidade Federal de Lavras (UFLA)} and {Universidade federal 
                         de Itajub{\'a} (UNIFEI)} and {Empresa de Pesquisa 
                         Agropecu{\'a}ria de Minas Gerais (EPAMIG)} and {Empresa de 
                         Pesquisa Agropecu{\'a}ria de Minas Gerais (EPAMIG)} and {Empresa 
                         de Pesquisa Agropecu{\'a}ria de Minas Gerais (EPAMIG)} and 
                         {Empresa de Pesquisa Agropecu{\'a}ria de Minas Gerais (EPAMIG)} 
                         and {Empresa de Pesquisa Agropecu{\'a}ria de Minas Gerais 
                         (EPAMIG)} and {Empresa de Pesquisa Agropecu{\'a}ria de Minas 
                         Gerais (EPAMIG)}",
                title = "Leaf water potential of coffee estimated by Landsat-8 images",
              journal = "PLoS One",
                 year = "2020",
               volume = "15",
               number = "3",
                pages = "e0230013",
             abstract = "Traditionally, water conditions of coffee areas are monitored by 
                         measuring the leaf water potential (\ΨW) throughout a 
                         pressure pump. However, there is a demand for the development of 
                         technologies that can estimate large areas or regions. In this 
                         context, the objective of this study was to estimate the \ΨW 
                         by surface reflectance values and vegetation indices obtained from 
                         the Landsat-8/OLI sensor in Minas GeraisBrazil Several algorithms 
                         using OLI bands and vegetation indexes were evaluated and from the 
                         correlation analysis, a quadratic algorithm that uses the 
                         Normalized Difference Vegetation Index (NDVI) performed better, 
                         with a correlation coefficient (R2) of 0.82. Leave-One-Out 
                         Cross-Validation (LOOCV) was performed to validate the models and 
                         the best results were for NDVI quadratic algorithm, presenting a 
                         Mean Absolute Percentage Error (MAPE) of 27.09% and an R2 of 0.85. 
                         Subsequently, the NDVI quadratic algorithm was applied to 
                         Landsat-8 images, aiming to spatialize the \ΨW estimated in 
                         a representative area of regional coffee planting between 
                         September 2014 to July 2015. From the proposed algorithm, it was 
                         possible to estimate \ΨW from Landsat-8/OLI imagery, 
                         contributing to drought monitoring in the coffee area leading to 
                         cost reduction to the producers.",
                  doi = "10.1371/journal.pone.0230013",
                  url = "http://dx.doi.org/10.1371/journal.pone.0230013",
                 issn = "1932-6203",
             language = "en",
           targetfile = "maciel_leaf.pdf",
        urlaccessdate = "27 abr. 2024"
}