Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales
Iiames, J., E. Cooter, D. Pilant, AND Y. Shao. Comparison of EPIC-simulated and MODIS-derived Leaf Area Index (LAI) across multiple spatial scales. Remote Sensing. MDPI AG, Basel, Switzerland, 12(17):2764, (2020). https://doi.org/10.3390/rs12172764
Incorporating modeled LAI from EPIC model with satellite-derived LAI creates the possibility of replacing current static LAI inputs with satellite-derived LAI into EPA atmospheric models such as Community Multiscale Air Quality Model (CMAQ) or the Multilayer Model (MLM). This work is supported by Air, Climate and Energy research program (ACE) 6.02 (AIMS-2.1): Integrated Multimedia, Multi-stressor Systems Model Development for Ecosystem Applications (2015 – 2019).
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.