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@Article{GueriniFoKuplQuad:2020:EsNaGr,
               author = "Guerini Filho, Marildo and Kuplich, Tatiana Mora and Quadros, 
                         Fernando L. F. de",
          affiliation = "{Universidade Federal do Rio Grande do Sul (UFRGS)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Santa Maria (UFSM)}",
                title = "Estimating natural grassland biomass by vegetation indices using 
                         Sentinel 2 remote sensing data",
              journal = "International Journal of Remote Sensing",
                 year = "2020",
               volume = "41",
               number = "8",
                pages = "2861--2876",
                month = "Apr.",
             abstract = "Estimation of natural grassland biomass was carried out in a 
                         region located in the Brazilian Pampa, using field and remote 
                         sensing data and statistical models. The study was conducted in a 
                         grassland with a rotational grazing system, with grazing rest 
                         interval based on accumulated thermal sums 375 and 750 Degrees Day 
                         (DD). One image of the MSI (MultiSpectral Instrument) sensor 
                         aboard the Sentinel-2 satellite was evaluated and calibrated by 57 
                         sampled biomass units collected in the field. Initially, the image 
                         was preprocessed, with extraction of the reflectance values of the 
                         Sentinel-2 bands, re-sampling of the pixels to 20 metres and 
                         calculation of vegetation indices. Data statistical analyses 
                         indicated significant correlations between field and remote 
                         sensing data. Multiple linear regression analyses were applied at 
                         each grazing rest interval using the remote sensing variables as 
                         predictors (independent) of the biomass (dependent). Among the 
                         variables, it is important to highlight the significant 
                         correlation of the red-edge bands with the biomass. The equations 
                         for estimating green biomass-presented coefficients of 
                         determination (R2 ) of R2 = 0.51 for the rest interval 375 DD and 
                         R2 = 0.65 for the rest interval 750 DD, while the senescent and 
                         total biomass generated adjustments with R2 \≤ 0.50 for the 
                         two rest intervals. Biomass estimates results were satisfactory, 
                         regardless of the interval evaluated. Sampling schemes at 
                         different seasons of the year and further spectral and field 
                         variables (spectral and biomass) are suggested to improve even 
                         more the accuracy of the estimates.",
                  doi = "10.1080/01431161.2019.1697004",
                  url = "http://dx.doi.org/10.1080/01431161.2019.1697004",
                 issn = "0143-1161",
             language = "en",
           targetfile = "guerini_estimating.pdf",
        urlaccessdate = "13 abr. 2021"
}


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