@Article{MaireMarNouStaPon:2012:ExMORe,
               author = "le Maire, Guerric and Marsden, Claire and Nouvellon, Yann and 
                         Stape, Jos{\'e} Luiz and Ponzoni, Fl{\'a}vio Jorge",
          affiliation = "CIRAD, UMR and SupAgro, UMR and CIRAD, UMR / Departamento de 
                         Ci{\^e}ncias Atmosf{\'e}ricas, IAG, Universidade de S{\~a}o 
                         Paulo and Department of Forestry and Environmental Sciences, North 
                         Carolina State University and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Calibration of a species-specific spectral vegetation index for 
                         Leaf Area Index (LAI) monitoring: Example with MODIS reflectance 
                         time-series on Eucalyptus plantations",
              journal = "Remote Sensing",
                 year = "2012",
               volume = "4",
                pages = "3766--3780",
                 note = "Setores de Atividade: Agricultura, Pecu{\'a}ria e Servi{\c{c}}os 
                         Relacionados.",
             keywords = "remote sensing, eucalypt, EucVI, MOD13Q1, radiative transfer 
                         model, PROSAIL.",
             abstract = "The leaf area index (LAI) is a key characteristic of forest 
                         ecosystems. Estimations of LAI from satellite images generally 
                         rely on spectral vegetation indices (SVIs) or radiative transfer 
                         model (RTM) inversions. We have developed a new and precise method 
                         suitable for practical application, consisting of building a 
                         species-specific SVI that is best-suited to both sensor and 
                         vegetation characteristics. Such an SVI requires calibration on a 
                         large number of representative vegetation conditions. We developed 
                         a two-step approach: (1) estimation of LAI on a subset of 
                         satellite data through RTM inversion; and (2) the calibration of a 
                         vegetation index on these estimated LAI. We applied this 
                         methodology to Eucalyptus plantations which have highly variable 
                         LAI in time and space. Previous results showed that an RTM 
                         inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) 
                         near-infrared and red reflectance allowed good retrieval 
                         performance (R2 = 0.80, RMSE = 0.41), but was computationally 
                         difficult. Here, the RTM results were used to calibrate a 
                         dedicated vegetation index (called EucVI) which gave similar LAI 
                         retrieval results but in a simpler way. The R2 of the regression 
                         between measured and EucVI-simulated LAI values on a validation 
                         dataset was 0.68, and the RMSE was 0.49. The additional use of 
                         stand age and day of year in the SVI equation slightly increased 
                         the performance of the index (R2 = 0.77 and RMSE = 0.41). This 
                         simple index opens the way to an easily applicable retrieval of 
                         Eucalyptus LAI from MODIS data, which could be used in an 
                         operational way.",
                  doi = "10.3390/rs4123766",
                  url = "http://dx.doi.org/10.3390/rs4123766",
                 issn = "2072-4292",
                label = "lattes: 7476929614934397 5 LeMaireMarNouStaPon:2012:ExMORe",
             language = "en",
        urlaccessdate = "28 abr. 2024"
}