Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales

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.

Use of PhenoCam Measurements and Image Analysis to Inform the ALMANAC Process-based Simulation Model

“…The Environmental Policy Integrated Climate Model (EPIC) has already been supported with remote sensing imagery in a 2020 study from [35]. They used data acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) to assess if modelled LAI to support air quality models for retroactive and future meteorological assessments.…”

“…They used data acquired from Moderate Resolution Imaging Spectroradiometer (MODIS) to assess if modelled LAI to support air quality models for retroactive and future meteorological assessments. They found that EPIC LAI simulations and MODIS LAI collections "compared favorably", although some data collections were more successful than others [35]. This shows that data from simulations, particularly LAI, can be used to supplement other remote sensing projects.…”

Climate change and topographic differences influence grassland vegetation greening across environmental gradients

“…It has been effectively used in vegetation dynamics monitoring (such as coverage, productivity, and phenology) and the study of vegetation responses to climatic changes at different scales during the past few years (Mao et al, 2012;Piao et al, 2014). Additionally, this index is the primary input for generating other geophysical products, such as the leaf area index, evapotranspiration, and land cover classification (Cihlar et al, 1991;Iiames et al, 2020).…”

Spatiotemporal evolution of vegetation in the karst region in southwest China based on the Google Earth Engine platform

“…Thus, this technology is broadly applied to monitor dynamic changes in vegetation [3]. Among the remote sensing products available today, commonly used vegetation indices include the leaf area index (LAI) [4], fraction of vegetation cover (FVC) [5], enhanced vegetation index (EVI) [6], and normalized difference vegetation index (NDVI) [7]. These indices are capable of representing the ecological status of surface vegetation.…”