## 1 Introduction  
The Leaf Area Index (LAI), the total leaf area per unit ground area, is a key biophysical variable playing an important role in global carbon, water, and energy cycles [1, 2]. As such, it acts as an important parameter for several applications, such as land surface models [3], ecological models [4], and yield prediction models [5]. The amount of leaf area has a first-order control on photosynthesis, transpiration, and absorption of radiation, varying in both space and time. LAI seasonal dynamics provide information about phenological processes of canopy development, senescence, and plant traits [6]. Therefore, retrieving LAI over large areas and having a good knowledge of their yearly variations, errors, and bias is extremely important. Such information is central to accurately estimating primary productivity, understanding land surface-atmosphere exchanges, and detecting the response of terrestrial vegetation to climate change [7]. It is also beneficial for a large remote sensing community because it provides insights for the interpretation and correct usage of the LAI maps. Satellite-based Earth Observation (EO) offers the opportunity to retrieve information on LAI which is global in coverage and at increasing temporal and spatial resolution [8; 9; 10; 11; 12]. Robust estimates of uncertainties associated with EO LAI estimates are needed to ensure their appropriate integration within further analyses such as data assimilation [6; 13]. However, such robust evaluations are rarely possible due to the scarcity of in situ data at appropriate temporal and spatial resolutions [14; 15]. LAI product uncertainty information represents the performance of the products' algorithm and reflects the uncertainties in the input data, model imperfections, and the inversion process [16; 17; 18], which could be called the theoretical uncertainties [19]. Evaluation studies typically assess the theoretical uncertainty of LAI based on the standard from Global Climate Observing System (GCOS) [20], which sets a target for absolute LAI uncertainty range to be within 0.5 m\({}^{2}\) m\({}^{-2}\) and a relative uncertainty of less than 20%. However, few studies validate the uncertainty estimates of EO LAI using in situ data [14] at appropriate scales. A key challenge for the validation process is the mismatch in the scale of satellite products (typically \(>\) 300 m) compared to that of the in situ data (\(<\)10 m). Heterogeneity of LAI at scales finer than the satellite resolution [21] means that simple comparison between measurements at different scales can be problematic [22]. The validation of LAI at temporal scales includes a more important component, the LAI temporal dynamics, besides the traditional absolute value checking [23]. The temporal dynamics of LAI time series contain useful ecological information (e.g., phenological infomation), which help study the relationship between plant phenology and climate [24] or assist in land cover classification [25]. However, the temporal dimension has been neglected for most of the LAI validation work [23]. To validate the seasonal variations of LAI products, higher field sampling rates are need, especially for the Evergreen Broadleaf Forests (EBF) across several regions (Africa, Eurasia, South America, Australia and Asia) [14]. Despite the important role played by tropical forests in regulating the global carbon [26; 27] and energy balance [28], tropical phenology is highly uncertain. Large-scale monitoring of tropical forest LAI with satellites is hindered by several challenges, including signal saturation [23], poor observation conditions (cloud-aerosol contamination, etc.) [29], coarse spatial and temporal resolutions [30], and scarcity of both ground data and detailed validation tests [31; 32; 33]. Uncertainties are therefore particularly high in these regions [34].