# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## Methods
### LAI Data (In Situ LAI and Upscaling, MODIS LAI, EPIC LAI)
#### 2.2.2 MODIS Lai  
Both the 1.0 km MOD15A2 (Mc5) and the 0.5 km MOD15A2H (Mc6) LAI products are composited over an 8-day period and both are derived from a three-dimensional radiative transfer model driven by an atmosphere corrected surface reflectance product, a LC product, and ancillary information on surface characteristics. The half-kilometer resolution of the Mc6 collection reduces the uncertainty associated with heterogeneous landscapes, where one LC (i.e., biome type) is assigned to each pixel [16]. MODIS daily surface reflectances, previously produced at 1.0 km, were replaced by a 0.5 km product, which improved product output through better aerosol retrieval and algorithm corrections. In addition, the LAI product was improved through replacement of the static LC input with new multiyear LC maps. Both Mc5 and Mc6 MODIS LAI output is determined via two separate pathways: the main and the back-up algorithm. The main and back-up algorithms ingest an eight biome-specific classification map at 1.0 km (Mc5) and 0.5 km (Mc6), an atmospherically corrected MODIS spectral reflectance product (1.0 km--Mc5; 0.5 km--Mc6), and ancillary data containing radiative transfer coefficient lookup tables, backup algorithm lookup tables and output variable properties. The eight biome types used to generate Mc5 and Mc6 LAI data include: (1) grasses and cereal crops; (2) shrubs; (3) broadleaf crops; (4) savannas; (5) evergreen broadleaf forests; (6) deciduous broadleaf forests; (7) evergreen needle leaf forests; and (8) deciduous needle leaf forests. Biome-specific lookup tables containing the most probable LAI values are developed from iterative runs of a three-dimensional canopy radiative transfer model. In the case of the main algorithm, possible solutions (i.e., retrievals) are cases where the differences between the radiative transfer modeled and MODIS observed reflectances are within the uncertainty of the observed reflectances [40]. Dimensionless uncertainty assigned to the red and near infrared MODIS bands are estimated at 20% and 5% (non-forests) and 30% and 15% (forests), respectively [41; 42]. Thus, a 1.0 km MODIS cell is assigned the mean of the retrieved LAI distribution. The MODIS LAI (MOD15A2H), collection (6) Level 4 data was downloaded from the NASA EarthData website ([[https://earthdata.nasa.gov](https://earthdata.nasa.gov)]([https://earthdata.nasa.gov](https://earthdata.nasa.gov))) for the 2002 year. The native projection for this data is the Sinusoidal Projection. MOD15A2H data, an 8-day composite data set with 0.5 km pixel size, is delivered in \(1200\times 1200\) km\({}^{2}\) tiles in the Hierarchical Data Format. The algorithm chooses the \"best\" pixel available from all the acquisitions of the MODIS Terra sensor from within the 8-day period. MODIS LAI is processed over eight biome types identified by the MODIS LC product (MCD12Q1), where each 0.5 km pixel is classified according to its status as a land or non-land pixel. Non-land pixels include: perennial salt or fresh water, barren or sparse vegetation, perennial snow or ice, permanent  
Figure 4: Scale comparisons for MODIS LAI (“Mc5” 1.0 km, “Mc6” 0.5 km), in situ LAI (“LAI-RM” 1.0 km), and EPIC LAI (“E”) (12.0 km). Nine 12.0 km EPIC LAI values within a \(3\times 3\) grid were compared to MODIS aggregated LAI (“Mc5”: 144 1.0 km and “Mc6”: 576 0.5 km per one EPIC “E” 12.0 km cell). wetlands/inundated marshlands, or urban/built-up. The entire APB was contained within tile: H-11, V-5. All 46 MODIS LAI scenes for 2002 were stacked then reprojected into the Universal Transverse Mercator projection zones 17 and 18 (WGS 84 datum). This process was repeated for 1.0 km MODIS LAI (MOD15A2) collection (5) data downloaded from the USGS Earth Resources Observation and Science Center. MODIS LAI (Mc5 and Mc6) in its raw form is not functional for analysis unless the data are smoothed to compensate for missing data and bad retrievals. Due to snow, ice, or cloud issues, LAI products often have pixels with anomalous high (spikes) and low (drops) values.