Land Surface Parameterization (LSP) schemes have evolved from simple tipping-bucket models to fully interactive models, including parameterizations which account for exchanges of momentum, energy, mass, and biogeochemistry. As the demand for greater realism has increased, so has the complexity of LSPs which now includes some parameters that may not be universally relevant to all regions of the globe. The performance of LSP schemes depends on the magnitude of structural, data-related (input and output), and parameter uncertainties in the model. Parameter estimation uncertainty can be reduced by calibrating LSPs against measurements available at field sites. Given the multiple outputs of the models, multi-objective optimization approaches are performed. Some of the parameter values used in LSPs have originally obtained from laboratory studies which analyzed plant behavior under a range of conditions in enclosed chambers. The research described in this dissertation takes advantage of currently available data from several eddy covariance flux towers located mainly in the Brazilian Amazon basin to estimate parameter values of a widely-used LSP scheme, version 3 of the Simple Biosphere model (SiB3). Background climatological data was used to assess the representativeness of the data collection period that might have affected model calibration. Variance-based sensitivity analysis was then used to investigate potential structural deficiencies in SiB3 and to reduce the dimensionality of the subsequent optimization by identifying those model parameters that merit calibration. Finally, some structural and conceptual aspects of SiB3 were tested inside Biosphere 2 Tropical Rain Forest biome (B2-TRF) under meteorological conditions that resemble those predicted in future climate scenarios for the Amazon basin.