## 4 Discussion
This weakness reflects the complexity of this biome type and highlights that the algorithm used to generate the uncertainty information for these LAI products needs to be improved. For areas of high uncertainties, data producers may need to refine the algorithms and verify the information using additional data sources, particularly in situ data [34]. However, with respect to field data collection, more accurate measurements are also needed to test the results, such as destructive methods or using the litterfall traps. Direct measurement methods obtaining 'true' LAI values could also act as a reference to correct the bias and errors in indirect measurement techniques [31]. The accuracy of LAI data retrieved from remote sensing products is very important for a range of earth system models, for which LAI is a key internal variable. LAI uncertainty is particularly critical for model-data fusion in terrestrial carbon cycle studies [91]. The uncertainty produced for the existing remote sensing LAI products in the analysis would further lead to biases in vegetation productivity estimates [92; 93; 94]. For instance, the saturation of satellite-derived VIs generally results in an underestimation of GPP or NPP in areas with dense vegetation. Using finer resolution remote sensing data in carbon modeling studies could be an effective way to reduce the uncertainty in the estimates of C fluxes and/or stocks, but more robust errors are critical [95]. Uncertainty estimates of the observations are as important as the observations themselves because together they co-determine the outcome of the assimilation systems [96]. Bayesian approaches are commonly used data assimilation systems and the influence of observations are weighted by observational uncertainties. Uncertainties in data are critical as they often determine the outcome of analyses and forecasts [97]. The assimilation process requires clear error quantification for LAI to resolve model results and limit biases [98]. Besides, the presence of bias in a data stream will limit the utility of using multiple observation types in an assimilation framework. Therefore, it is imperative to characterize the error in the observations and understand better the error associated with the direct measurements of LAI to landscape pixel [99]. Our results indicated that none of these LAI uncertainty products are robust and therefore users should consider inflating the existing LAI product uncertainty when using these data sources in data assimilation frameworks [98; 100; 101] or in other studies for which the uncertainty of the product plays an important role. One way of solving this spatial mismatch problem is to upscale the field measurements based on its relationship with the plant index retrieved from high-resolution images (e.g., 10-30 m resolution) [102], or using airborne Light Detection and Ranging (LiDAR) [103; 104]. However, we failed to find a good relationship between the Landsat based EVI or NDVI and the field measurements (Figure S5). LAI-VI relationships are limited by a number of factors, including vegetation type, sun-surface-sensorgeometry, leaf chlorophyll content, background reflectance, and atmospheric quality [31]. Geolocation uncertainties due to spatial mismatch and insufficient overlap data between in situ measurements and good quality Landsat scenes partly lead to this result. Saturation effects occur for both field measurement and spectral plant indices for very dense canopies, which also increases the challenge of upscaling field LAI to generate high-resolution LAI reference maps. In addition, the temporal mismatch between field and Landsat data also contributes to the poor relationship between field and Landsat data. This result may be also associated with the complex forest environments (such as terrain, tree densities, canopy structures, and species diversities) in southern China (Figure S4, Table S1). Thus, their LAI cannot be accurately modeled based on simple spectral indices, even with relatively high resolution (30 m) satellite data.