## 4 Discussion
[PERSON] (2019) found GEOV2 fAPAR showed a systematic overestimation of the fAPAR anomalies compared with the MODIS fAPAR and proposed a two-step harmonization procedure to remove this discrepancy [86]. However, the homogenization may alter the magnitude of the original fAPAR time series in an undesirable way. These results highlight the need to validate the temporal consistency between different satellite products and explore more solutions to deal with such inconsistencies. In addition, geolocation uncertainty due to the spatial mismatch could also have influenced validation reliability, As our study sites are not homogeneous and sampling area is consistently smaller than the pixel of remote sensing data, mean or median remotely-sensed LAI values of surrounding \(3\times 3\) array of pixels [15,87,88] cannot be used for validation. To solve this problem, future field measurements should be conducted at a larger spatial scale and across more homogenous habitats. Overall, the absolute and relative uncertainty of LAI products tends to be smaller for the fine resolution LAI products (Table 2). Collection 300 m GEOV3 has the smallest absolute uncertainty at around 0.22. Collection1 km GEOV2 has the largest absolute uncertainty at around 0.56. Uncertainty magnitude for Collection 6 500 m MODIS LAI is 0.37. This performance is consistent with the LAI relative uncertainty (Table 2). If compared with the absolute uncertainty requirements (\(\pm 0.5\)) and relative uncertainty requirements (20%) set by the GCOS [12], all three products (Collection 300 m GEOV3, 0.22, 5%; Collection 6 500 m MODIS LAI, 0.37, 10%; Collection 1 km GEOV2 0.56, 18%) appear satisfactory. This is consistent with two global studies comparing different EO LAI products (MODIS, CYCLOPES, and GLOCARBON) at two coarse spatial resolutions (5 km and 1 km). More pixels can meet the absolute uncertainty and relative uncertainty requirements at the spatial resolution of 1 km compared with the 5 km [19,34]. All of these results indicated that the LAI product uncertainty is scale-dependent. The algorithms will produce smaller uncertainty estimated for the LAI values at finer spatial scales. This could make the uncertainty estimates of the fine-resolution products become very conservative. Changes in LAI error with spatial resolution could be due to vegetation heterogeneity, especially for the forests and scale-dependent reflectance values. However, it is still unclear how product uncertainty changes over different spatial scales. For MODIS, the product uncertainty is the standard deviation over all acceptable solutions of a look-up table (LUT) retrieval method [17]; for Collection 300 m GEOV3 and 1 km GEOV2 LAI products, the quantitative uncertainties (LAI_ERR) are computed as the RMSE between the final dekadal value and the daily NNTs estimates in the compositing period [12; 62]. Standardizing different LAI products to the same spatial scale and averaging the LAI and uncertainty for different pixels during the upscaling and resampling process [19; 89] could all generate errors [90]. When dealing with different spatial scales, the land surface properties (e.g., land cover proportion, soil properties) could change significantly. Thus, LAI retrieval algorithms are based on inherent empirical assumptions on the distribution of their parameters, which can depart significantly from the actual canopy and soil characteristics [14]. The ratio (\(r\), in Equation (4)) of LAI product uncertainty to the bias of in situ measurements is dramatically lower than the reference value for all of these LAI products (Figure 4, Table 2). This result indicated that none of the three LAI uncertainty products tested here were able to provide a robust estimate for the in-situ measurements. The most promising product is the Collection 300 m GEOV3 LAI; however, while its accuracy is the best, its uncertainty is still too conservative to capture the offset from co-located field LAI measurements.