**3. Results**

## *3.1. BRDF Parameters Relationship*

Figure 2 shows the relationship obtained between VB1 and VB2 with the NDVI and between V and R of bands 1 and 2. The r2, Root Mean Square Error (RMSE) and linear fit (red line) regression values are shown in the top left of each subplot. The results show that there is a general dependence of the V parameter with the NDVI. This result was expected considering that the V parameter models the volumetric component of the vegetation. A high V value means a denser vegetation, a higher biomass, and effectively a higher NDVI value. However, the high RMSE values both for band 1 and band 2 (0.42,0.45), sugges<sup>t</sup> that this is not a very precise approximation. In the case of the inter-band relationships, the results show that there is a high correlation (r2 > 0.8) between the parameters derived from bands 1 and 2. The small RMSE values (0.24,0.04) indicate that this approximation is reliable and could provide a smaller error than that derived by the atmospheric effects propagation from AVHRR or the spectral adjustment and calibration errors from MODIS. The cluster of points that show a 1:1 relation belongs to points with a small NDVI (NDVI < 0.2). Figure 3 shows the relationship between the Band 1 and Band 2 parameters for low NDVI values. It has been shown in previous studies that for low vegetation amount, the RB1 and RB2 values are almost identical [25,29]. We also noticed the same behavior for the V parameters, when the NDVI < 0.1. In this study, we also computed the relationship of R with the NDVI, but there was little or no correlation. This is expected considering that the R parameter is associated with the geometric component, and higher NDVI values such as for forests show a similar value to bare ground.

**Figure 2.** Relationships between VB1, VB2, RB1 and RB2 with each other and the NDVI, derived from the Moderate Resolution Imaging Spectrometer (MODIS).

**Figure 3.** Regression between VB1, VB2, RB1 and RB2 for low NDVI values. The red line represents the linear regression fit. The blue line shows the 1:1 line.
