## 6 Conclusions  
The state and evolution of plant canopies has been monitored from space for decades because the phenology of vegetation foreshadows the yield of crops and therefore serves as a useful indicator of economic output and impacts. The crucial role of vegetation in the carbon cycle has also stimulated a strong interest in the assessment of the net primary productivity of the biosphere. However, the bulk of published studies (1) are mostly concerned about agricultural productivity in monocultures over large areas in the Northern Hemisphere, where phenology follows a regular, highly predictable seasonal evolution, (2) are based on an analysis of the Normalized Difference Vegetation Index (NDVI), mostly using the logistic or hyperbolic tangent models, and (3) rely on globally available data products at a spatial resolution of 500 m or coarser. This paper explored the feasibility of modeling natural, unmanaged vegetation canopy in the protected Kruger National Park in South Africa, where the vegetation features significant biodiversity and exhibits much larger spatial and temporal fluctuations than crops (in particular, from year-to-year and within-seasons). This study relied on a state of the art FAPAR data product available since March 2000, derived from NASA's MISR instrument, using the MISR-HR processing system, and generated at a spatial resolution of 275 m.  
This paper compared the suitability of four different parametric double S-shaped models to simulate the phenology of plant canopies: the Gaussian (PDGF), the Hyperbolic Tangent (PDHyTgF), the logistic (PDLF), and the Sine (PDSF) models, and it showed that they are all capable of explaining the bulk of the variability present in the FAPAR record when constrained with ample empirical data, well distributed in time. These models do differ, though, in their efficiency and effectiveness with respect to the presence of gaps in the data, an issue that is intrinsic to the use of remote sensing observations in the solar spectral range. Specifically, PDHyTgF appears to be the least sensitive to the particular distribution of input data during the vegetative season, though it is somewhat more computationally expensive. The PDSF model fits the FAPAR data with the smallest \(\chi^{2}\) but is also the most sensitive to the presence of gaps in the input data. The PDLF model is functionally equivalent to PDHyTgF, in the sense that both models can be made to match each other perfectly, though they do differ somewhat in terms of their sensitivity and computational efficiency. It has also been shown that the presence of gaps in the empirical evidence over extended periods, especially during the growing phase (rainy season), is detrimental to the successful inversion of global models over the vegetative season. This suggests that further efforts to reduce the gaps in the input data will go a long way to improve the benefits to be derived from this modeling approach. FAPAR values simulated by those models were compared to Gross Primary Productivity (GPP) estimates derived from an analysis of the Eddy Covariance flux tower at Skukuza, as this environmental variable is useful to investigate biomass accumulation, agricultural yield, or elements of the carbon cycle over land, for instance. A general non-linear relation between FAPAR and GPP has been confirmed when all data sets are adequate for the purpose, and a useful relation exists between FAPAR and GPP when the latter varies in the range from 0 to 5 gC m\({}^{-2}\) d\({}^{-1}\). That relation saturates at larger values of GPP, so FAPAR may no be appropriate to monitor large values of GPP. However, long gaps were also occurring in this GPP data set, and the uneven distribution or even absence of FAPAR data during the vegetation growth phase in multiple seasons prevented meaningful comparisons, or resulted in unrealistic assessments in those seasons.