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.

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Abstract. The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. It is modulated by hydrometeorological drivers (i.e., shortwave radiation, air temperature, vapor pressure deficit and soil moisture) and the vegetation state (i.e., canopy greenness, leaf area index) at instantaneous to interannual timescales. In this study, we set out to evaluate the ability of GPP-models to capture this variability. 11 models were considered, which rely purely on remote sensing data (RS-driven), meteorological data (meteo-driven, e.g., dynamic global vegetation models; DGVMs) or a combination of both (hybrid, e.g., light-use efficiency models; LUE). They were evaluated using in situ observations at 61 eddy covariance sites, covering a broad range of herbaceous and forest biomes. The results illustrated how the determinant of temporal variability shifts from meteorological variables at sub-seasonal timescales to biophysical variables at seasonal and interannual scale. RS-driven models lacked the sensitivity to the dominant drivers at short timescales (i.e., shortwave radiation and vapor pressure deficit), and failed to capture the decoupling of photosynthesis and canopy greenness (e.g., in evergreen forests). Conversely, meteo-driven models accurately captured the variability accross timescales, despite the challenges in the prognostic simulation of the vegetation state. Largest errors were found in water-limited sites, where the accuracy of the soil moisture dynamics determines the quality of the GPP estimates. In arid herbaceous sites, canopy greenness and photosynthesis were more tightly coupled, resulting in improved results with RS-driven models. Hybrid models capitalized on the combination of RS observations and meteorological information. LUE models were among the most accurate models to monitor GPP across all biomes, despite their simple architecture. Overall, we conclude that the combination of meteorological drivers and remote sensing observations is required to yield an accurate reproduction of the spatio-temporal variability of GPP. To further advance the performance of DGVMs, improvements in the soil moisture dynamics and vegetation evolution are needed.

<p>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.</p> <p>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.</p> <p>Key words: Sentinel-2, Tropical forest, soil fertilization, topographical position.</p>