This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R² = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R² = 0.59; R² = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

Surface carbon fluxes associated with terrestrial vegetation play a key role in the global carbon cycle. Remote sensing (RS) and land surface models (LSM) have demonstrated to be valuable tools in assessing the gross primary production (GPP). Yet, the seasonal variability of this flux, and timing of the seasonal cycle remain challenging to observe and simulate accurately. Here, the ability of four RS products and two LSM to simulate GPP and its variability was assessed. It was found that the mean seasonal GPP was simulated accurately with RS-based models, but that it failed to capture the seasonal variability and timing of the seasonal cycle. In contrast, the LSMs demonstrated their ability to simulate seasonal anomalies in GPP, but had trouble to simulate the associated phenology.

Tropical forests such as Amazon is repository of ecological services. Understanding how tropical forest responds to the climate helps to improve ecosystem modeling and declining the uncertainty in calculation of carbon balance. Nowadays, the availability of very high resolution satellite imagery such as Sentinel-2 are powerful tools for analyzing the canopy structural and functional shifts over time, especially for tropical forest. In this study, we examined the effect of the nutrient availability (nitrogen (N) and phosphorus (P)) on canopy and structural properties in tropical forest of French Guiana. In situ observations of canopy structure and functioning (i.e. photosynthesis, leaf N, chlorophyll content) were collected at two experimental sites (Paracou and Nouragues). Three topographical positions in each site were considered (top of the hills, middle and bottom end of the slope) and four plots were manipulated with different level of fertilization (Control, N, P, NP) in September 2016. Statistical analysis were conducted to analyze how the fertilization affect the forest canopy seasonality and if differences between sites and across positions existed. Furthermore, we tested whether Sentinel-2 data could help or not to describe the canopy changes observed in the field. Therefore, all Sentinel-2 images available before the start of the experiment, which date represent the natural situation, and two years after the intensive and repeated fertilization were collected. Greenness, chlorophyll and N, P related indicators were calculated from Sentinel-2 images. Key words: Sentinel-2, Tropical forest, soil fertilization, topographical position.