# Phenology and Seasonal Ecosystem Productivity in an Amazonian Floodplain Forest
## 5 Conclusions  
Cycles of ecosystem productivity and evapotranspiration are decoupled in this southern Amazon flooded forest due to the dormancy period during flooded months, which influences the amount of free water evaporation and the decrease of productivity. The remaining high values of productivity at the beginning of the flooded period and before the flooding ends, in May, are explained by the shorter duration of soil saturation in higher terrain portions, concentrated near the flux tower. Although topographic differences in this floodplain forest only vary by 2 meters in height, some terrain portions are not subject to the same flood pulse, given the mean flood height of 1.3 m. Flooding also determines the amount of soil moisture during the non-flooded months, which is positively associated with productivity (rho = 0.34). To address the remote sensing challenges of detecting canopy phenology and productivity seasonality in this flooded area, we associated these ground measurements and leaf litter mass data with the EVI from MODIS-MAIAC and assessed the density of canopy gaps using airborne LiDAR data. Our results showed that EVI is positively associated with leaf litter mass (r = 0.55) and with GPP (r = 0.5), suggesting a coupling between new leaf production and phenology of photosynthetic capacity, with a bimodal productivity pattern of assimilating maximum carbon after the flooded period ends and during the rainy non-flooded season, under non-limiting soil water conditions. We also found that this forest has a very dense canopy, which means that free water exposed through gaps during the flooded period does not strongly influence EVI observations. Additionally, EVI described inter-annual variations of vegetation response to environmental drivers and revealed that water availability is the main phenological driver at this site, which can change vegetation phenology during extreme years, consequently affecting ecosystem productivity. Our study showed the potential for accurate monitoring of phenology in floodplain forests using spaceborne remote sensing. The following are available online at [[http://www.mdpi.com/2072-4292/11/13/1530/s1](http://www.mdpi.com/2072-4292/11/13/1530/s1)]([http://www.mdpi.com/2072-4292/11/13/1530/s1](http://www.mdpi.com/2072-4292/11/13/1530/s1)): Figure S1: Scatterplot between flood height and the river level; Figure S2: Permanent water channels mapped through the Bing Virtual Earth high-resolution image provided on QGIS 2.18 software; Table S1: The percentage of water channels and forest cover extract from the Bing Virtual Earth high-resolution image provided on QGIS 2.18. Figure S3: Scatterplot of rainfall data from TRMM against flux tower data; Figure S4: Scatterplot of temperature data from ERA5 against flux tower data; Figure S5: Scatterplot of vapour pressure deficit (VPD) from the tower and ERA 5 data. Table S2: Forest inventory of two plots located 200 and 100 m from the Bananal tower. Figure S6:Monthly GPP and EVI from May, 2011 to September, 2013; Figure S7: A) Monthly rainfall estimated from the Tropical Rainfall Measuring Mission (TRMM) and flood height measured at the flux tower base. B) Cumulative Water Deficit from 2004 to 2017, calculated with TRMM. C) Average monthly soil moisture (volume of water per bulk volume of soil) for depths ranging from 0-150 cm (Clayey layers), and for the 200 cm (Clayey layer) and 290 cm (Sandy layer). Data availability: The flux tower dataset, TRMM and leaf litter mass dataset are summarized in Tables S3-S6. **Author Contributions:** All authors contributed to the research conceptualization. [PERSON] processed the MODIS MALAC time series and conducted the gap analysis on LiDAR data. [PERSON] processed and provided the GPP data. [PERSON] contributed to climate and remote-sensing analyses. [PERSON] and [PERSON] conducted the formal analysis. [PERSON] wrote the manuscript and all authors reviewed the manuscript and provided critical feedback on the paper's discussion and improvement. [PERSON] supervised the research.