# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## Materials and Methods
### Remote-Sensing Data and Products  
We acquired the MODIS-MAIAC product EVI for the study area from the time period of 2004 to 2017 [50]. In this product, prior to EVI calculation, the surface reflectance data were normalized to nadir target and 45-degree solar zenith angle through the Bidirectional Reflectance Distribution function, at a spatial resolution of 1 km and aggregated to biweekly (16-day) composites using the median values. The EVI was calculated using Equation 3[13]. Further information on image processing and correction are described in [PERSON] et al. [50,51]. The composites were retrieved considering only cloud-free and low atmospheric turbidity according to MAIAC quality flags. For the MODIS-MAIAC pixel containing the flux tower, the mean number of samples per 16-day composite was 4.6 samples for the flooding period (February-May) and 12 samples for the non-flooding period. \[EVI=2.5*\frac{\rho NIR-\rho Red}{\rho NIR+(6*\rho Red-7.5*\rho Blue)+1} \tag{3}\]  
where \(\rho\)NIR is infrared reflectance, \(\rho\)Red is red reflectance, and \(\rho\)Blue is blue reflectance. The constants (6, 7.5, 1, and 2.5) in the divisor represent the aerosol coefficient adjustment of the atmosphere for the red and blue band, the adjustment factor for the soil and the gain factor, respectively [52]. There is a high density of water channels in the study area. Therefore pixels composed of more than 10% of permanent water were excluded from the analysis, using the Global Forest Change product v1.4 mask [53]. This procedure also eliminated areas that were disturbed between 2000 to 2016. The mask was resampled from 30 m to 1 km according to MODIS pixels resolution. Only pixels around the tower footprint (2 km) meeting these criteria were selected for analysis. To further check the presence of water channels in these pixels, we used the high resolution Bing Virtual Earth image on QGIS 2.18 to visual interpret and manually delineate permanent water bodies, and we found more than 94% of forest cover (Figure S2 and Table S1). To explore rainfall variability over the time series, we obtained rainfall data from the Tropical Rainfall Measuring Mission (TRMM). This data showed a significant correlation with tower measurements from 2004 to 2014 (r = 0.62, \(p<0.001\)) (Figure S3). We did not use the field measured rainfall data directly due to gaps on records from 2014 to 2016. We also used 2 meter air temperature and vapour pressure deficit data from the ERA5 climate reanalysis [54] to build an inter-annual analysis, due to gaps in tower for these variables from 2010 to 2016. Both variables from the ERA5 reanalysis product had a very high correlation (Ta: r = 0.83, \(p<0.001\); VPD: r = 0.95, \(p<0.001\)) with tower data (Figures S4 and S5). The cumulative water deficit (CWD) was calculated to investigate negative monthly precipitation anomalies [55]. We calculated the monthly average ET from the tower, based on records with a good density of observations from 2004 to 2014 (Table S3), since there were gaps in measurements from 2010 to 2016. The CWD increases in magnitude when rainfall is lower than ET. The following rule was applied to the rainfall data (R) for each month (m) with evapotranspiration (E) set at 100 mm month (Equation (4)). \[ \begin{split}\text{CWD}_{\text{m}}=\text{CWD}_{\text{m}-1}+ \text{R}_{\text{m}}-\text{E}_{\text{m}}\\ \text{If }\text{CWD}_{\text{m}}>0\text{ then }\text{CWD}_{\text{m}}=0 \end{split}  \tag{4}\]  
We also performed a canopy gap analysis for the studied forests using airborne LiDAR data to assert the degree of expected interference of free standing water under the canopy during the flooded period on MODIS EVI values. The data was collected on 20 February 2016 along a 15 km transect with 500 m width, using a Riegl LMS-Q680i laser scanner at 500 m average flight altitude, 45\({}^{\circ}\) _i_LMS of view, and 300 kHz scanning frequency. The data had a high pulse density of greater than 5 points per m\({}^{2}\).