{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:13:59Z","timestamp":1760238839336,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u00d7 3 grid (or 36 km \u00d7 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\u20131.34), 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.<\/jats:p>","DOI":"10.3390\/rs12172764","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T09:05:37Z","timestamp":1598432737000},"page":"2764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales"],"prefix":"10.3390","volume":"12","author":[{"given":"John S.","family":"Iiames","sequence":"first","affiliation":[{"name":"United States Environmental Protection Agency, Washington, DC 27711, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ellen","family":"Cooter","sequence":"additional","affiliation":[{"name":"United States Environmental Protection Agency, Washington, DC 27711, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8696-5107","authenticated-orcid":false,"given":"Andrew N.","family":"Pilant","sequence":"additional","affiliation":[{"name":"United States Environmental Protection Agency, Washington, DC 27711, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.scitotenv.2012.07.077","article-title":"The role of the atmosphere in the provision of air-ecosystem services","volume":"448","author":"Cooter","year":"2013","journal-title":"Sci. Total Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/0168-1923(92)90040-B","article-title":"Foliage area and architecture of plant canopies from sunfleck size distributions","volume":"60","author":"Chen","year":"1992","journal-title":"Agric. For. Meteorol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1002\/2015JD024406","article-title":"Improved meteorology from an updated WRF\/CMAQ modeling system with MODIS vegetation and albedo","volume":"121","author":"Ran","year":"2016","journal-title":"J. Geophys. Res."},{"key":"ref_4","unstructured":"Goto, Y. (2008). Improved Vegetation Characterization and Freeze Statistics in A Regional Spectral Model for the Florida Citrus Farming Region. [Ph.D. Thesis, The Florida State University]."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1029\/1999GL011234","article-title":"Increased carbon sequestration by a boreal deciduous forest in years with a warm spring","volume":"27","author":"Black","year":"2000","journal-title":"Geophys. Res. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1126\/science.1192534","article-title":"Efficient atmospheric cleansing of oxidized organic trace gases by vegetation","volume":"330","author":"Karl","year":"2010","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1016\/j.atmosenv.2011.01.028","article-title":"Estimation of ambient BVOC emissions using remote sensing techniques","volume":"45","author":"Nichol","year":"2011","journal-title":"Atmos. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1029\/1999GL900107","article-title":"Potential high-latitude vegetation feedbacks on CO2-induced climate change","volume":"26","author":"Levis","year":"1999","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"22645","DOI":"10.1029\/98JD01564","article-title":"Description and evaluation of a multilayer model for inferring dry deposition using standard meteorological measurements","volume":"103","author":"Meyers","year":"1998","journal-title":"J. Geophys. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"173","DOI":"10.5194\/acp-15-173-2015","article-title":"Air quality and atmospheric deposition in the eastern US: 20 years of change","volume":"5","author":"Sickles","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sutton, M.A., Mason, K.E., Sheppard, L.J., Sverdrup, H., Haeuber, R., and Hicks, K.W. (2014). Progress in monitoring and modelling estimates of nitrogen deposition at local, regional and global scales. Nitrogen Deposition, Springer. Critical Loads and Biodiversity.","DOI":"10.1007\/978-94-007-7939-6"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6695","DOI":"10.1029\/1999JD901080","article-title":"Sensitivity of the National Oceanic and Atmospheric Administration multilayer model to instrument error and parameterization uncertainty","volume":"105","author":"Cooter","year":"2000","journal-title":"J. Geophys. Res."},{"key":"ref_13","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X.-Y., Wang, W., and Powers, J.G. (2008). A description of the Advanced Research WRF Version 3, National Center for Atmospheric Research."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1023\/A:1013123725860","article-title":"A coupled land-surface and dry deposition model and comparison to field measurements of surface heat, moisture and ozone fluxes","volume":"1","author":"Pleim","year":"2001","journal-title":"Water Air Soil Pollut. Focus"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8491","DOI":"10.1002\/2015JD023424","article-title":"Sensitivity of the WRF\/CMAQ modeling system to MODIS LAI, FPAR, and albedo","volume":"120","author":"Ran","year":"2015","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R.R., Knyazikhin, Y., and Myneni, R.B. (2016). Evaluation of MODIS LAI\/FPAR product collection 6. Part 2: Validation and Intercomparison. Remote Sens., 8.","DOI":"10.3390\/rs8060460"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1885","DOI":"10.1109\/TGRS.2006.871215","article-title":"MODIS leaf area index products: From validation to algorithm improvement","volume":"44","author":"Yang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","unstructured":"Texas A&M AgriLife Research (2020, August 24). Environmental Policy Integrated Climate (EPIC) Model, Texas A&M AgriLife Research, Available online: https:\/\/data.nal.usda.gov\/dataset\/environmental-policy-integrated-climate-epic-model."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"GB1015","DOI":"10.1029\/2003GB002199","article-title":"A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system","volume":"19","author":"Krinner","year":"2005","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/S0168-1923(98)00091-4","article-title":"An interactive vegetation SVAT model tested against data from six contrasting sites","volume":"92","author":"Calvet","year":"1998","journal-title":"Agr. For. Meteorol."},{"key":"ref_21","first-page":"D18102","article-title":"Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products","volume":"111","author":"Gibelin","year":"2006","journal-title":"J. Geophys. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"931","DOI":"10.5194\/gmd-7-931-2014","article-title":"Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts","volume":"7","author":"Szczypta","year":"2014","journal-title":"Geosci. Model Dev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jhydrol.2016.10.038","article-title":"Multi-objective assessment of three remote sensing vegetation products for streamflow prediction in a conceptual ecohydrological model","volume":"543","author":"Naseema","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2006.872529","article-title":"Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup","volume":"44","author":"Morisette","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1080\/16742834.2014.11447154","article-title":"Evaluation of the WRF model with different land surface schemes: A drought event simulation in Southwest China during 2009\u20132010","volume":"7","author":"Hu","year":"2014","journal-title":"Atmos. Oceanic Sci. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4133","DOI":"10.1111\/gcb.13787","article-title":"Inconsistencies of interannual variability and trends in long-term satellite leaf area index products","volume":"23","author":"Jiang","year":"2017","journal-title":"Glob. Chang. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/2150704X.2013.862600","article-title":"Comparison of Different MODIS Data Product Collections over an Agricultural Area","volume":"5","author":"Stern","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"129","DOI":"10.13031\/2013.32748","article-title":"A modeling approach to determining the relationship between erosion and soil productivity","volume":"27","author":"Williams","year":"1984","journal-title":"Trans. ASAE"},{"key":"ref_29","unstructured":"Sharpley, A.N., and Williams, J.R. (1990). EPIC-Erosion\/Productivity Impact Calculator: 1. Model Documentation, USSDA Tech. Bull. 1768."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1061\/(ASCE)0733-9429(1985)111:6(970)","article-title":"SWRRB, a simulator for water resources in rural basins","volume":"111","author":"Williams","year":"1985","journal-title":"ASCE Hydraul. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Iiames, J.S., Cooter, E., Schwede, D., and Williams, J. (2018). A comparison of simulated and field-derived leaf area index (LAI) and canopy height values from four forest complexes in the southeastern USA. Forests, 9.","DOI":"10.3390\/f9010026"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2107","DOI":"10.1016\/j.atmosenv.2010.02.044","article-title":"Estimation of NH3 bi-directional flux over managed agricultural soils","volume":"44","author":"Cooter","year":"2010","journal-title":"Atmos. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.13031\/2013.23637","article-title":"The soil and water assessment tool: Historical development, applications, and future research directions","volume":"504","author":"Gassman","year":"2007","journal-title":"Trans. ASABE"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"S87","DOI":"10.1139\/s03-032","article-title":"Modelling the effects of boreal forest landscape management upon streamflow and water quality: Basic concepts and considerations","volume":"2","author":"Putz","year":"2003","journal-title":"J. Environ. Eng. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1002\/hyp.5611","article-title":"SWAT2000: Current capabilities and research opportunities in applied watershed modelling","volume":"19","author":"Arnold","year":"2005","journal-title":"Hydrol. Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1093\/sjaf\/32.3.101","article-title":"Validation of an integrated estimation of Loblolly pine (Pinus taeda l.) leaf area index (LAI) utilizing two indirect optical methods in the southeastern United States","volume":"32","author":"Iiames","year":"2008","journal-title":"South. J. Appl. For."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Leblanc, S.G., Chen, J.M., and Kwong, M. (2002). Tracing radiation and architecture of canopies. TRAC Manual, Ver. 2.1.3, Natural Resources Canada; Canada Centre for Remote Sensing.","DOI":"10.4095\/219952"},{"key":"ref_38","unstructured":"Burnaby, B.C. (1999). Gap Light Analyser (GLA). Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs. User\u2019s Manual and Program Documentation, Simon Fraser University; the Institute of Ecosystem Studies."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1139\/x06-030","article-title":"Using multispectral satellite imagery to estimate leaf area and response to silvicultural treatments in loblolly pine stands","volume":"36","author":"Flores","year":"2006","journal-title":"Can. J. For. Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4381","DOI":"10.1080\/01431160500113393","article-title":"Time-series validation of MODIS land biophysical products in a Kalahari woodland, Africa","volume":"26","author":"Huemmrich","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/S0034-4257(02)00074-3","article-title":"Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data","volume":"83","author":"Myneni","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_42","unstructured":"Myneni, R., and Park, Y.K.T. (2020, May 20). MODIS Collection 6 (C6) LAI\/FPAR Product User\u2019s Guide, Available online: https:\/\/lpdaac.usgs.gov\/sites\/default\/files\/public\/product_documentation\/mod15_user_guide.pdf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2015.12.023","article-title":"An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data","volume":"174","author":"Shao","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Eklundh, L., and J\u00f6nsson, P. (2011). Timesat 3.1 Software Manual, Lund University."},{"key":"ref_45","unstructured":"United States Department of Agriculture Forest Service (1990). Silvics of North America: 1. Conifers; 2. Hardwoods, Agriculture Handbook 654."},{"key":"ref_46","unstructured":"Chambers, J.M., Cleveland, W.S., Kleiner, B., and Tukey, P.A. (1983). Comparing Data Distributions. Graphical Methods for Data Analysis, 62, Wadsworth International Group."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2014.02.003","article-title":"Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data","volume":"145","author":"Weng","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ping, B., Meng, Y.S., and Su, F.Z. (2018). An enhanced linear spatio-temporal fusion method for blending Landsat and MODIS data to synthesize Landsat-like imagery. Remote Sens., 10.","DOI":"10.3390\/rs10060881"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8238","DOI":"10.3390\/rs6098238","article-title":"Noise reduction and gap filling of fAPAR time series using an adapted local regression filter","volume":"6","author":"Moreno","year":"2014","journal-title":"Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1397","DOI":"10.3390\/rs70201397","article-title":"Uncertainty analysis in the creation of a fine-resolution leaf area index (LAI) reference map for validation of moderate resolution LAI products","volume":"7","author":"Iiames","year":"2015","journal-title":"Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.14358\/PERS.74.11.1389","article-title":"Leaf Area Index (LAI) change detection analysis on Loblolly Pine (Pinus taeda) following complete understory removal","volume":"74","author":"Iiames","year":"2008","journal-title":"Photogram. Eng. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:53Z","timestamp":1760177213000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/17\/2764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,26]]},"references-count":51,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12172764"],"URL":"https:\/\/doi.org\/10.3390\/rs12172764","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,8,26]]}}}