## 1 Introduction  
Vegetation indices (VI), defined as the arithmetic combination of two or more bands related to the spectral characteristics of vegetation [1, 2], have been used in a variety of fields including phenology, classification of vegetation, photosynthetic activity, aboveground net primary productivity and land surface temperature [3, 4]. VI, particularly the Normalized Difference Vegetation Index (NDVI), are essential components of any study aiming to investigate ecosystem services especially those where vegetation, water, and biodiversity are involved [5, 6]. However, VI sensitivity is affected by the changing radiance that accompanies changes in orientation of the vegetation surface being sensed [7]. The radiance changes at different times in the year and between years, due to different solar incidences over the surface, the so-called sun-sensor geometry [8]. Radiance is further changed in rough terrain, where a combination of the orientation of the terrain and the position of the satellite will determine high or low illumination conditions (IC) [7, 8, 9, 10]. The ratio properties of NDVI enable a large proportion of noise caused by changing sun angles, topography, clouds or shadow to be cancelled, making this index less susceptible to IC. However, this index is still affected by atmospheric, soil and background vegetation cover feedback conditions. Furthermore, in tropical forests with high levels of biomass or high Leaf Area Index (LAI) values, NDVI also tends to saturate [7, 11]. The Enhanced Vegetation Index (EVI), in turn, is more sensitive than NDVI to biophysical attributes such as LAI [11; 12] and much more affected by IC than NDVI, because is not a ratio-based VI and cannot compensate for variations in IC. In addition, EVI was proven to be five times more sensitive than NDVI to changes in near-infrared reflectance (NIR) [11; 12; 13; 14]. Due to the effect of IC, the use of EVI as an indicator of vegetation functioning or forest productivity has been put under debate in recent years. Some authors claimed that the unusual greening effect observed in the dry season in the Amazon forest using the EVI Moderate-resolution Imaging Spectroradiometer (MODIS) imagery [15] was induced by changes in sun-sensor geometry and not as a result of canopy structure, phenological patterns, or vegetation functioning [16]. This effect has been confirmed in similar ecosystems at different times of the same season, with different IC [17; 14]. However, in similar tropical forests, after the removal of the IC effects, seasonal patterns were still present and seemed to be correlated with gross primary production (GPP), although authors recommended being cautious with this correlation [13]. Effects related to sun-sensor geometry and topography were also found at different times of the year using EVI and NDVI in subtropical deciduous forests under different IC. Sunlit and shadowed surfaces showed, respectively, different intensities of decrease and increase in reflectance even after topographic correction [12]. Most of the studies referenced above used coarse spatial resolutions (MODIS) and/or limited dates to account for seasonal or intra-annual variability using VI [7; 12; 14; 16]. When tried to correct IC in Landsat sensors, for either global coverages or long time-series only some of them covering limited time spans were included in the analysis [18; 19; 20]. In general, IC effects were calculated using the information on solar zenith and azimuth angles for MODIS available imagery or using this sensor but with information taken from Landsat [18; 21; 22]. Transforming image angle information into a model of IC using data on digital elevation models (DEM) provides higher detail at the pixel level, especially in irregular terrain [10; 23] and is important to compare different spatial resolutions [24; 25]. The monitoring of IC and VI spatiotemporal patterns can be improved using higher spatial resolutions, longer time series, and the best-calibrated data to minimize errors in the analysis.