## 1 Introduction  
Leaf Area Index (LAI) is the number of leaf layers per unit area in an ecosystem. It is widely used in the coupling of land surface and atmospheric processes, such as radiation, precipitation interception [1] and gas exchange [2]. There are several methods to estimate LAI [3], including direct observation and the use of modern radiometers. However, at global scale satellite products are arguably the most important. LAI is a key variable of energy and water balance calculations in vegetation models [4]. It influences numerous model outputs such as net primary productivity (NPP), evapotranspiration (ET), the fraction of the light being absorbed by plants (FAPAR) and nutrient dynamics [5]. Land Surface Models (LSMs) have different approaches for calculating LAI, and while the use of plant functional types (PFTs) is widespread [6], there are important differences in the number of simulated PFTs, their spatial distribution and the representation of vegetation dynamics [7]. LSMs differ in the number of PFTs they include [8], and typically divide vegetation into between 4 and 16 PFTs. The number of PFTs and their parameterization leads to important discrepancies in the distribution of the vegetation types [9]. In addition, models vary in their representation of functional trade-offs and plant responses to the environment [10]. The former creates a trade-off between the number of modeled PFTs and their correct representation: using many PFTs leads to an increased uncertainty due to their parameterizations, while an insufficient number results in a misrepresentation of vegetation dynamics. One example of this is the ratio of evergreen to deciduous boreal forest in the Northern Hemisphere, or the ratio of evergreen forests to grasslands over the tropics; the distribution of these have important implications for future climate prediction, as shown by [PERSON] _et al._[7, 11]. There are several studies that have compared model results with satellite data [12, 11, 13]. [PERSON] _et al._[12] compared the NCAR-CC3 model with satellite data and found that the model partitioning of latent and sensible heat fluxes create discrepancies in the CO\({}_{2}\) fluxes, which lead to an overestimation of the modeled growing season length (GSL). In another example, [PERSON] _et al._[14] compared phenology measurements of ten forests sites in USA with fourteen vegetation models; they found that the models overestimated the length of the growing season, while correctly reproducing the CO\({}_{2}\) fluxes due to an underestimation of the LAI peak. Finally, [PERSON] _et al._[15] found that models underestimate the carbon uptake during the growing season in boreal forest ecosystems due to tardiness in the LAI peak. One of the main reasons for the lack of comparison between model outputs and satellite observations is data limitation. While satellites have been recording vegetation growth since the 1980s, the data were difficult to use due to frequent missing values. The first complete satellite global timeseries did not appear until 1991 [16, 17]. These products were initially used to validate simple climatic models of vegetation distribution [12], but their usage has increased steadily in a range of applications. For example, they are used to estimate the biomass of grasslands [18], boreal forests [19] and mangroves [20]. During this time, LSMs continued to develop in sophistication and diversity [21]. While the core processes represented in these models remain similar, they vary greatly in their parameterization. This is particularly true in the responses to temperature and drought. Moreover, refined observational forcing data have become widely available. This allows LSMs to be run offline using observed climatology, as in this paper, or offline with self-generated climatology as part of an Earth-System-Model (ESM) (as in Part II of this study, [PERSON] _et al._[22]).