Estimating natural grassland biomass by vegetation indices using Sentinel 2 remote sensing data

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.

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