The Earth Engine scripts classify Eucalyptus plantations and surrounding native vegetation in the Brazilian state of Minas Gerais, based on Landsat surface reflectance images ingested into the Earth Engine platform and manually delineated training polygons, and segment these vegetation types into distinct stands and patches.
NDVI is an indicator of greenness based on land surface reflectance of visible (red) and near-infrared parts of spectrum (Weier and Herring 2011).
NDVI ranges from –1 to 1 (with higher numbers indicating more greenness) based on land surface reflectance of visible (red) and near infrared parts of spectrum (Weier and Herring 2011).
A near-infrared (NIR) reflectance sensor designed for moisture measurement at the soil surface is based on the reflectance of two light-emitting diodes (LEDs) of different wavelengths, in which one has a wavelength of 1940 nm and a strong water absorption band, whereas the other has a wavelength of 1800 nm and a weak water absorption band, as related to soil moisture reflectance.
Since the hyperspectral imaging technique was originated from geological and remote sensing applications, the established calibration protocols are based on spectral reflectance measurements on a flat surface, without considering three-dimensional tissue morphology, diffuse scattering, background pigmentation, and other biological effects.
Average cone contrast (Michelson contrast based on maximum cone reflectance and minimum surround reflectance) is 0.20 ± 0.09 for the retina portion common in all frames.
We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data.
The predictions based on Landsat reflectance were robust, with R-squared values of 0.86 for pH and 0.79 for soil organic carbon (SOC), and were used to create predicted surfaces (maps) for these soil properties.
