## 3 Vegetation Phenology
### Phenological Patterns Derived from Remote Sensing
#### 3.2.2 Monitoring Forest Phenology  
The first global dataset with high temporal resolution was acquired by the Advanced Very High Resolution Radiometer (AVHRR) sensor, onboard the TIROS-N satellite (launched in 1978) and National Oceanic and Atmospheric Administration (NOAA) series 6 through 14 (the latter launched in 1994) meteorological satellites. A number of vegetation indices were developed based on AVHRR images, including: the Normalized Difference Vegetation Index (NDVI) [115, 116, 117, 118], the Vegetation Conditioning Index (VCI) [119, 118], the Temperature Conditioning Index (TCI) [119, 120], the Vegetation Health Index (VHI) [121], the NDVI-Land Surface Temperature (NDVI-LST) [122], the Drought Severity Index (DSI) and the Palmer Drought Severity Index (PDSI) [123, 124, 125]. Then, a more specific water sensitive index was developed, the normalized difference water index (NDWI) [126]. This index showed adequate potential for canopy-level water content estimation ([127, 128, 129] and others). One of the first studies for the monitoring of Amazonian forests was carried out by [130]. Using daily NDVI images derived from NOAA AVHRR from 1982 to 1988, [130] found little or no cyclical pattern over the Amazon forest. They also reported a decline in the response of the vegetation index over the dense forested area over time, demonstrating the effects of the sensor degradation and the lack of sensor calibration. [PERSON] et [PERSON]. [131] studied drought patterns in South America from 1985 to 1992 using AVHRR data. They found that NDVI and VCI agreed well with rainfall anomalies for the entire country. However, the relationship between NDVI and monthly rainfall in the tropical forest was weak and the drought dynamic patterns for both indices in this region were not strongly correlated with the meteorological precipitation anomaly data. [PERSON] _et al._[132] carried out a correlation analysis between NOAA AVHRR NDVI and field meteorological stations in the Amazon evergreen forest, finding no relationships. However, during strong climatic events, such as El Nino and La Nina, changes in the amplitude of AVHRR NDVI over Amazonia suggest that the forest phenology is responsive to rainfall variation over seasonal and inter-annual time scales [106]. It has also been suggested that temporal variations in southern and eastern forests of Amazonia in the NOAA AVHRR NDVI data could be primarily associated with atmospheric variations such as clouds and aerosols [98]. The MODIS sensor, which has improved radiometric and atmospheric corrections in relation to the NDVI generated by the AVHRR sensor, has greatly contributed to the advances in the knowledge of the Amazon forest phenology. Studies evaluating the EVI for the Amazon evergreen forest captured for the first time a remarkable large-scale seasonal pattern. [PERSON] _et al._[74] observed an increase of 25% in EVI (enhanced leaves activity) in the sunnier dry season across Amazon forests, suggesting that sunlight may exert more influence than rainfall in the phenology of this forest. Results from the study performed by [133] using MODIS EVI also reinforce this result. Quantitative analysis of the MODIS LAI product for the Amazon forests demonstrated that changes in LAI are positively correlated with changes in solar radiation and negatively correlated with changes in precipitation, although the correlations between leaf area and radiation changes are larger and more numerous [66]. However, [PERSON] _et al._[68] showed that the MODIS LAI seasonal differences are particularly sensitive to additional filtering for indirect aerosol and cloud effects, while the seasonal differences in EVI were less sensitive to the filtering methods evaluated. The large scale patterns observed on the new leaf production [66, 74, 133] encompasses many different forest types and these results do not agree with the observation of field studies, where peak in leaf flush has been observed during the dry, wet or dry and wet seasons in different forests in Amazonia.