# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 4 Discussion
### Mc5, Mc6, and EPIC Comparisons (12.0 km)  
At the 12.0 km resolution grid cell scale Mc5 showed larger LAI values than Mc6 across 8 of the 13 dates for the four research sites, with values differing by 13.5 percent over all research sites and dates. We reason these differences at the grid cell level were related to input changes within the new Mc6 collection. One significant change between both collections was an improved biome map with Mc6 LAI reducing errors in biome classification. However, these differences may not have been recognized at the 12.0 km resolution grid cell level based on averaging of any propagated errors. Across all four research sites and all 13 dates, EPIC and MODIS LAI (Mc5 and Mc6) showed similar values when evaluating directional bias, with MODIS exceeding EPIC LAI by 0.61 and 0.40 LAI, respectively. However, there were wide variations between both MODIS LAI collections and EPIC LAI on a site and date basis. This would preclude interchanging EPIC for MODIS on a site-specific (i.e., \(36\times 36\) km\({}^{2}\)) basis based on the lack of a directional bias. However, on a larger regional level, EPIC and MODIS might be interchangeable, appropriate for regional and national modeling of air quality. In a recent study, MODIS satellite products that included both LAI and the fraction of photosynthetically active radiation (FPAR) were integrated into the WRF-CMAQ modeling system and showed greater error and bias with temperature but reduced error and bias with the LAI and FPAR inputs [15]. The primary areas on uncertainty within the EPIC model include characterization of the vegetation component and the ingestion of the chosen climatological time series. The coarse distribution of FIA sampling points within each \(3\times 3\) grid (or nine 12.0 km cells) affected EPIC LAI outputs. This was seen by running multiple iterations using differing tree species associations when presented with an FIA tree not previously calibrated in the field. These FIA limited points (usually 2-3 per 36 km\({}^{2}\)) were assumed to completely describe all tree species associations throughout the entire \(3\times 3\) grid. Forest species groups may be predicted with greater accuracy using remotely sensed imagery in combination with other spatially continuous geospatial data. The USFS is developing a dataset that combines in situ FIA data with STATSGO (soil bulk density, depth to bedrock, soil permeability, soil porosity, available water capacity, soil plasticity, rock volume, soil types, soil texture, and soil pH), MODIS vegetation indices (Enhanced Vegetation Index [EVI], NDVI), prism temperature and precipitation data, and various other datasets to arrive at a distribution of 28 forest type groups across the continental United States. ([[https://databasin.org/datasets/7](https://databasin.org/datasets/7) fa4 ddf2 alc447469 dbe7e9f8f1 de814]([https://databasin.org/datasets/7](https://databasin.org/datasets/7) fa4 ddf2 alc447469 dbe7e9f8f1 de814)). This 250 m product may provide a useful input to parameterize EPIC LAI simulations at regional or national scales. Another source of variability in the EPIC simulations that was highlighted during the generation of the stand simulations is the importance of the climatological time series employed. Daily weather conditions were \"generated\" using statistics derived from time series (e.g., 50 years or more) of observed weather conditions. Such long time-series are used to ensure that extreme events across a range of return periods e.g., multi-year droughts, heat waves, and floods are represented. Such extreme events can damage or destroy forest stands. EPIC simulations, however, assume these events occur everywhere within the study area and so occasionally a simulated stand will fail to survive, and so no viable stand will be simulated for the entire study area, a situation which is unrealistic.