# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 1 Introduction  
The magnitude and seasonal variation in leaf area play a significant role in determining atmospheric deposition of airborne pollutants and, as such, is a key variable within atmospheric deposition modeling [1]. Satellite remote sensing of leaf area index (LAI), with LAI defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies [2], allows for forest canopy characterization over large areas at broad spatial scales. However, satellite-derived LAI products can be limited by obstructed atmospheric conditions yielding sub-optimal values, or complete non-returns. Ecological research has utilized satellite-derived LAI estimates for retrospective analysis, e.g., [3, 4], however these data will never be available for prospective meteorological or alternative management applications. This study is a two-tiered effort to compare regional LAI estimates from the most recent algorithm change in theModerate Resolution Imaging Spectroradiometer (MODIS) LAI product and to compare these satellite estimates to LAI generated from an agricultural model developed by the United States Department of Agriculture (USDA). Modeled LAI inputs hold promise in support of various regional and local scale air quality models for retrospective and future meteorological assessments. LAI can be a critical determinant of regional air and water quality [1]. Leaf area impacts the carbon sequestration process from carbon emissions [5], the depositional removal of atmospheric pollutants [6], the creation of tropospheric ozone from biogenic emissions of volatile organic compounds [7], and the regulation of the change of global meteorological patterns over time [8]. Several process-based atmospheric models are highly sensitive to small deviations of LAI. One model that ingests LAI is the Multilayer Model (MLM), a local scale atmospheric deposition model that estimates ozone (O\({}_{3}\)), water vapor, nitric acid (HNO\({}_{3}\)), and sulfur dioxide (SO\({}_{2}\)) fluxes across the Clean Air Status and Trends Network (CASTNET) [9; 10; 11]. The MLM inputs a generalized annual LAI time-series profile developed from periodic leaf-on and leaf-off field sampled optical point measurements of each plant species. This model was shown to be highly sensitive to LAI profile parameters extracted from these point measurements generating estimates greater than 60% for ozone and \(\pm\)25% for sulfur dioxide and nitric acid [12]. The regional Community Multiscale Air Quality Model (CMAQ), developed in part by the United States Environmental Protection Agency (EPA) [8], is partially reliant on Pleim-Xiu LAI (PX LAI), an LAI model producing estimates based on: (1) deep soil temperature and (2) an LAI response function [13; 14]. This LAI response function relies on land-cover (LC)-specific minimum/maximum LAI and soil temperature, resulting in known overestimation biases [15]. CMAQ, MLM, and other atmospheric and air-quality models are likely to improve with more detailed temporal and spatial LAI inputs. LAI has been estimated both at regional and global scales using satellite remote sensing data from a variety of sensors including the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua satellite platforms. MODIS LAI data originally processed in 2000 (collection 3), has been reprocessed three additional times with each iteration (collections 4-6) improving LAI returns through improved inputs and refinements in the main algorithm [16; 17]. The most recent collection 6 (release date: 2015) improved upon the previous collection 5 (release date: 2007) through an improved surface reflectance product and the replacement of a static LC product with new multi-year LC product, both inputs benefiting from improved spatial resolution (1.0 to 0.5 km) [16].