## 3 Vegetation Phenology
### Phenological Patterns Derived from Remote Sensing  
#### 3.2.1 Background  
Remote sensing technology offers a possible solution to the problem of collecting leaf data from the largest area of continuous forest on Earth. However, a potential obstacle to the acquisition of a multitemporal dataset for detecting vegetation phenology in the Amazon by satellite-derived vegetation indices is the influence of aerosols from biomass-burning [97, 98] and clouds and cloud's shades [99]. These problems are slowly becoming resolved through the use of a new generation of moderate-resolution sensors with more frequent coverage (e.g., daily) of Amazonia. These sensors represent a new opportunity for frequent and efficient assessment of natural changes in forest canopies. For instance, the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra (morning) and Aqua (afternoon) platforms provide consistent daily coverage of the entire globe at 250 m to 1 km resolution with 36 bands of spectral information since 2000 and 2001, respectively [100]. The MODIS instruments acquire images up to 4 times a day, with a swath width of approximately 2300 km. The data are geometrically and radiometrically rectified, and land-cover products are automatically generated for the monitoring of the earth surface, such as the daily and 8-day composite Surface Reflectance product (MOD09), 16-day composite Surface Reflectance and Vegetation Index product (MOD13), 8-day composite Leaf Area Index (MOD15), Vegetation Cover Conversion product (MOD44A), and the Vegetation Continuous Fields product (MOD4B).  
Vegetation index images are the most widely used satellite data to monitor natural vegetation dynamics as they measure canopy greenness, a composite property of canopy structure, leaf area and canopy chlorophyll content [101]. Over the last several decades, vegetation monitoring at regional and global scales has been done with the Advanced Very High Resolution Radiometer (AVHRR) sensor data converted to Normalized Difference Vegetation Index (NDVI) images [102, 103, 104, 105, 106, 107, 108]. NDVI is defined as:  
\[\centering NDVI=\frac{\left(\rho_{\text{\tiny{NIR}}}-\rho_{\text{\tiny{Red}}} \right)}{\left(\rho_{\text{\tiny{NIR}}}+\rho_{\text{\tiny{Red}}}\right)}\@add@centering \tag{1}\]  
where \(\rho_{\text{\tiny{NIR}}}\) and \(\rho_{\text{\tiny{Red}}}\) are the reflectance of NIR and Red spectral bands.  
In addition to the NDVI, the MODIS vegetation indices product also has the Enhanced Vegetation Index (EVI). NDVI has been intensively studied, and limitations including saturation in closed canopy and sensitivity to atmospheric aerosols and soil background have been reported [109, 110]. In this sense, EVI was developed to minimize these effects, including the blue band for atmospheric correction, computed as (Equation (2)):  
\[\centering EVI=2.5\times\frac{\rho_{\text{\tiny{NIR}}}-\rho_{\text{\tiny{Red }}}}{\rho_{\text{\tiny{NIR}}}+\left(6\times\rho_{\text{\tiny{Red}}}-7.5\times \rho_{\text{\tiny{Blue}}}\right)+1}\@add@centering \tag{2}\]  
where \(\rho_{\text{\tiny{NIR}}}\), \(\rho_{\text{\tiny{Red}}}\) and \(\rho_{\text{\tiny{Blue}}}\) are the reflectance in the NIR, Red and Blue channels respectively; 2.5 is a gain factor, 6 and 7.5 are coefficients designed to correct for aerosol scattering and absorption and 1 is a canopy background adjustment [111, 112]. While NDVI is more sensitive to the absorbing chlorophyll bands (red) and to atmospheric properties such as clouds, water vapor solar illumination and satellite viewing geometry [113, 114], EVI is more sensitive to canopy structural variations, including leaf area index [111].