# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 4 Discussion  
### Mc5, Mc6, and LAI-RM Comparisons (1.0 km)  
The improvement of MODIS LAI primary algorithm outputs did align with [16] findings where total uncertainty fell within 1.0 LAI units in comparison of LAI-RM to MODIS LAI. The Mc6 improvements reduced the Mc5 overestimate (+2.22 LAI) to less than 1.0 LAI units (+0.97 LAI). This was also seen when evaluating median differences where only a little more than half of the dates showed no significant differences across all four research sites. An interesting outlier is seen comparing the Mc6 difference with the LAI-RM on the Appomattox pine-dominated site (67.6% pine). This site showed no LAI difference between both the Mc6 and LAI-RM estimates for March 5, while the late spring (23 May) LAI estimate was 2.35 larger for MODIS than for LAI-RM (Table 6). This exceedance is difficult to understand in relation to the in situ measurements of the differing LC types within the 1.0 km reference area. The pine in situ LAI more than doubled between the March and May dates due to added needle flushes and understory hardwood leaf-out. But even this increase in conjunction with the hardwood LC component, which represented 21% of the LC on the 1.0 km site yielding a LAI of 2.87, could not account for the Mc6 recorded 4.13 LAI.  
Beyond error associated within the primary algorithm, two potential entry sources for error within the post-processing of MODIS LAI include the geometric reprojection from the native MODIS sinusoidal projection to the Landsat Universal Transverse Mercator projection and the temporal smoothing of the raw MODIS data. However, error introduced with the reprojection of the MODIS 1.0 and 0.5 km LAI data to the native Landsat projection has been reported as negligible [47, 48]. Additionally, the ASG  
Figure 9: Weighted mean LAI differences of MODIS (Mc5 and Mc6) to EPIC modeled LAI across Appomattox (**A**), Umstead (**A**), Hertford (**B**), and Fairystone (**C**) across 13 dates.  
smoothing filter does add some error but is rated as one of the better linear interpolation methods in removing frequency noise [49].  
Error in the creation of the LAI-RM may be introduced in the sampling strategy employed, in the optical measurements used within the modified Beer-Lambert light extinction function, in the analyst-effect in LC delineation [50], and in the correspondence of vegetation indices to ground measured LAI. In a prior study, we investigated variables ingested into the modified Beer-Lambert light extinction function. The largest sources of error identified were plant area index (PAI) and the woody-to-total ratio (W:T), variables subject to the thresholding method delineating sky from non-sky (PAI), and green vegetation from woody vegetation (W:T) [36]. It was understandable that we observed weak relationships between in situ LAI and vegetation indices derived from the red and near infrared bands of L5 and L7 imagery. The literature documents the NDVI:LAI correlation as suspect in forest stands exceeding a 3.0 LAI. Beyond LAI of 3.0, NDVI saturates i.e., an asymptotic increase with increasing LAI [51]. Saturation is a function of chemical and structural differences at the leaf and canopy levels (i.e., leaf orientation, pigmentation, woody-to-total ratio, canopy structure [tree height heterogeneity, crown classes present]) [48]. Because we did not have access to a good correlation in any of the hardwood stands, we also assumed our surrogate of the in situ LAI measured in a few areas was representative of the entire forest stand--an assumption that does not account for spatial heterogeneity of the forest canopy.