# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 1 Introduction
LAI has also been simulated by a number of land-surface models including the United States Department of Agriculture (USDA) Environmental Policy Integrated Climate (EPIC) model [18], the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) model [19] and the Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) model [20; 21]. Comparing growing season hindcast LAI simulations and phenological patterns observed from satellite-derived LAI is useful in detecting inconsistencies in both satellite and modeled products, thus leading to improvement with both data streams. For example, length of growing season and leaf onset were compared between modeled and satellite-derived LAI in the Euro-Mediterranean region [22]. Gross primary productivity (GPP), LAI, and vertical optical depth (VOD) were simulated by a conceptual ecohydrologic model (EcoHydr) then assessed in terms of its ability to reproduce the observed satellite derived products of GPP, LAI, and VOD from MODIS LAI products [23]. Whether LAI is simulated or generated from satellite observations, in situ and algorithm version comparisons are invaluable in the assessment of these products. MODIS LAI has been validated with in situ measurements at regional and global scales since its inception in 2001 [24]. At a regional level, the evaluation of LAI generated from two land surface schemes in southwest China found one model overestimating LAI by a factor of two with another model displaying close agreement with in situ LAI [25]. Comparisons across multiple iterations of LAI algorithm improvements allow the modeling community to understand the effect of LAI perturbations on backcasting and forecasting outcomes. Two globally based studies compared collections 5 and 6 to track overall global mean LAI trends [26] and to validate improvements in LAI estimation across each of the MODIS LC types [16]. On a regional level, MODIS collection comparisons of collections 3-5 were made to track LAI differences in corn and soybean crop types in Illinois [27]. Comparison of EPIC LAI to MODIS-derived LAI and in situ LAI will allow for its assessment as a potential variable input into air quality models (i.e., CMAQ). The effect of erosion on agricultural productivity initially drove the development of the USDA EPIC model in the early 1980s [28]. The model allows simulation of many processes important in agricultural management, but more recent studies have expanded to consider both natural and managed shrubland and forests [29; 30; 31]. Simulation using a daily time-step can be performed over 100 years or more to represent relatively long-lived natural or managed forest stands [32]. Examples of EPIC use within forested systems include: (1) the assessment of light interception in forecasting water usage in Kenyan semi-arid hedge intercrop, monocrop, and hedge monoculture (Senna spectabilis cv. Embu) systems [33]; (2) the examination of forest management practices on the loading of nutrients and sediments and streamflow in nine small Texas watersheds [34] ([[http://blackland.tamu.edu/models/apex](http://blackland.tamu.edu/models/apex)]([http://blackland.tamu.edu/models/apex](http://blackland.tamu.edu/models/apex))); (3) the estimation of nitrogen, phosphorous, and herbicide losses across three undisturbed control watersheds, three conventional clearcut watersheds, and three intensive clearcut watersheds [35], and most recently; (4) our study that investigates EPIC model performance for predicting forest stand canopy height and LAI across four forest complexes in Virginia and North Carolina [31]. The objective of this study is to compare year 2002 MODIS LAI (collections 5 and 6--i.e., Mc5, Mc6) with (1) in situ LAI estimates upscaled to four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin (APB) in North Carolina and Virginia, USA; and (2) EPIC modeled LAI over a \(3\times 3\) grid of nine 12.0 km grid cells centered over each of these four research sites.