Abstract
Modeled leaf area index (LAI) in conjunction with satellite-derived LAI data streams may be used to support various regional and local scale air quality models for retrospective and future meteorological assessments. The Environmental Policy Integrated Climate (EPIC) model holds promise for providing LAI within a dynamic range for input into climate and air quality models, improving on current LAI distribution assumptions typical within atmospheric modeling. To assess the potential use of EPIC LAI, we first evaluated the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product collections 5 and 6 (i.e., Mc5, Mc6) with in situ LAI estimates upscaled at four 1.0 km resolution research sites distributed over the Albemarle-Pamlico Basin in North Carolina and Virginia, USA. We then compared the EPIC modeled 12.0 km resolution LAI to aggregated MODIS LAI (Mc5, Mc6) over a 3 × 3 grid (or 36 km × 36 km) centered over the same four research sites. Upscaled in situ LAI comparison with MODIS LAI showed improvement with the newer collection where the Mc5 overestimate of +2.22 LAI was reduced to +0.97 LAI with the Mc6. On three of the four sites, the EPIC/MODIS LAI comparison at 12.0 km resolution grid showed similar weighted mean LAI differences (LAI 1.29-1.34), with both Mc5 and Mc6 exceeding EPIC LAI across most dates. For all four research sites, both MODIS collections showed a positive bias when compared to EPIC LAI, with Mc6 (LAI = 0.40) aligning closer to EPIC than the Mc5 (LAI = 0.61) counterpart. Despite modest differences between both MODIS collections and EPIC LAI, the overestimation trend suggests the potential for EPIC to be used for future meteorological alternative management applications on a regional or national scale.
Highlights
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
Satellite-derived LAI products can be limited by obstructed atmospheric conditions yielding sub-optimal values, or complete non-returns
Summary
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-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], 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 the Moderate 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
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