{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T09:13:00Z","timestamp":1783674780804,"version":"3.55.0"},"reference-count":89,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T00:00:00Z","timestamp":1567555200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000104","name":"National Aeronautics and Space Administration","doi-asserted-by":"publisher","award":["80NSSC17K0575"],"award-info":[{"award-number":["80NSSC17K0575"]}],"id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed data can be used to model the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil in natural and agricultural ecosystems. NPV and soil cover are difficult to estimate accurately since absorption by lignin, cellulose, and other organic molecules cannot be resolved by broadband multispectral data. A new generation of satellite hyperspectral imagers will provide contiguous narrowband coverage, enabling new, more accurate, and potentially global fractional cover products. We used six field spectroscopy datasets collected in prior experiments from sites with partial crop, grass, shrub, and low-stature resprouting tree cover to simulate satellite hyperspectral data, including sensor noise and atmospheric correction artifacts. The combined dataset was used to compare hyperspectral index-based and spectroscopic methods for estimating GV, NPV, and soil fractional cover. GV fractional cover was estimated most accurately. NPV and soil fractions were more difficult to estimate, with spectroscopic methods like partial least squares (PLS) regression, spectral feature analysis (SFA), and multiple endmember spectral mixture analysis (MESMA) typically outperforming hyperspectral indices. Using an independent validation dataset, the lowest root mean squared error (RMSE) values were 0.115 for GV using either normalized difference vegetation index (NDVI) or SFA, 0.164 for NPV using PLS, and 0.126 for soil using PLS. PLS also had the lowest RMSE averaged across all three cover types. This work highlights the need for more extensive and diverse fine spatial scale measurements of fractional cover, to improve methodologies for estimating cover in preparation for future hyperspectral global monitoring missions.<\/jats:p>","DOI":"10.3390\/rs11182072","type":"journal-article","created":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T03:22:36Z","timestamp":1567653756000},"page":"2072","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0241-1917","authenticated-orcid":false,"given":"Philip E.","family":"Dennison","sequence":"first","affiliation":[{"name":"Department of Geography, University of Utah, Salt Lake City, UT 84112, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Natural Resources, University of Nebraska Lincoln, Lincoln, NE 68583, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan K.","family":"Meerdink","sequence":"additional","affiliation":[{"name":"Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond F.","family":"Kokaly","sequence":"additional","affiliation":[{"name":"Geophysics and Geochemistry Science Center, US Geological Survey, Denver, CO 80225, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-7550","authenticated-orcid":false,"given":"David R.","family":"Thompson","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Craig S. T.","family":"Daughtry","sequence":"additional","affiliation":[{"name":"Hydrology and Remote Sensing Laboratory, US Department of Agriculture Agricultural Research Service, Beltsville, MD 20705, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5793-2835","authenticated-orcid":false,"given":"Miguel","family":"Quemada","sequence":"additional","affiliation":[{"name":"School of Agricultural Engineering, CEIGRAM, Universidad Politecnica de Madrid, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-4842","authenticated-orcid":false,"given":"Dar A.","family":"Roberts","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6276-9403","authenticated-orcid":false,"given":"Paul D.","family":"Gader","sequence":"additional","affiliation":[{"name":"Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3114-1642","authenticated-orcid":false,"given":"Erin B.","family":"Wetherley","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Izaya","family":"Numata","sequence":"additional","affiliation":[{"name":"Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keely L.","family":"Roth","sequence":"additional","affiliation":[{"name":"Science, The Climate Corporation, San Francisco, CA 94103, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/0034-4257(93)90020-X","article-title":"Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data","volume":"44","author":"Roberts","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1126\/science.1148913","article-title":"Hurricane Katrina\u2019s Carbon Footprint on U.S. Gulf Coast Forests","volume":"318","author":"Chambers","year":"2007","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1016\/j.rse.2009.01.006","article-title":"Estimating fractional cover of photosynthetic vegetation, non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors","volume":"113","author":"Guerschman","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.rse.2015.02.013","article-title":"Evaluation of spectral unmixing techniques using MODIS in a structurally complex savanna environment for retrieval of green vegetation, nonphotosynthetic vegetation, and soil fractional cover","volume":"161","author":"Meyer","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2003.07.001","article-title":"The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14276","DOI":"10.3390\/rs71114276","article-title":"Monitoring the Impacts of Severe Drought on Southern California Chaparral Species using Hyperspectral and Thermal Infrared Imagery","volume":"7","author":"Coates","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.rse.2018.02.073","article-title":"A framework for detecting conifer mortality across an ecoregion using high spatial resolution spaceborne imaging spectroscopy","volume":"209","author":"Tane","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Roberts, D.A., Dennison, P.E., Peterson, S., Sweeney, S., and Rechel, J. (2006). Evaluation of Airborne Visible\/Infrared Imaging Spectrometer (AVIRIS) and Moderate Resolution Imaging Spectrometer (MODIS) measures of live fuel moisture and fuel condition in a shrubland ecosystem in southern California. J. Geophys. Res. Biogeosci.","DOI":"10.1029\/2005JG000113"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI160.1","article-title":"Satellite Monitoring of Vegetation Phenology and Fire Fuel Conditions in Hawaiian Drylands","volume":"9","author":"Elmore","year":"2005","journal-title":"Earth Interact."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2018.06.020","article-title":"Hyperspectral remote sensing of fire: State-of-the-art and future perspectives","volume":"216","author":"Veraverbeke","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/S0034-4257(01)00239-5","article-title":"Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains","volume":"79","author":"Chuvieco","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.still.2005.11.013","article-title":"Remote sensing of crop residue cover and soil tillage intensity","volume":"91","author":"Daughtry","year":"2006","journal-title":"Soil Tillage Res."},{"key":"ref_13","unstructured":"Liebig, M.A., Franzluebers, A., and Follet, R.F. (2012). Remote sensing of soil carbon and greenhouse gas dynamics across agricultural landscapes. Managing Agricultural Greenhouse Gases: Coordinated Agricultural Research through GRACEnet to Address our Changing Climate, Academic Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1016\/j.rse.2007.08.014","article-title":"Evaluation of hyperspectral data for pasture estimate in the Brazilian Amazon using field and imaging spectrometers","volume":"112","author":"Numata","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Davidson, E.A., Asner, G.P., Stone, T.A., Neill, C., and Figueiredo, R.O. (2008). Objective indicators of pasture degradation from spectral mixture analysis of Landsat imagery. J. Geophys. Res. Biogeosci., 113.","DOI":"10.1029\/2007JG000622"},{"key":"ref_16","first-page":"26","article-title":"Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data","volume":"58","author":"Pullanagari","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3939","DOI":"10.1080\/01431160110115960","article-title":"Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations","volume":"23","author":"Asner","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","unstructured":"Scarth, P., Roder, A., and Schmidt, M. (2010, January 13). Tracking Grazing Pressure and Climate Interaction\u2014The Role of Landsat Fractional Cover in Time Series Analysis. Proceedings of the 15th Australasian Remote Sensing and Photogrammetry Conference, Alice Springs, Australia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0034-4257(99)00082-6","article-title":"Plant Litter and Soil Reflectance","volume":"71","author":"Nagler","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_20","unstructured":"Guerschman, J.P., Oyarzabal, M., Malthus, T., McVicar, T.R., Byrne, G., Randall, L., and Stewart, J. (2012). Evaluation of the MODIS-Based Vegetation Fractional Cover Product, CSIRO."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Candela, L., Formaro, R., Guarini, R., Loizzo, R., Longo, F., and Varacalli, G. (2016, January 10\u201315). The PRISMA Mission. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729057"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Iwasaki, A., Ohgi, N., Tanii, J., Kawashima, T., and Inada, H. (2011, January 1\u20135). Hyperspectral Imager Suite (HISUI)\u2014Japanese hyper-multi spectral radiometer. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Sendai, Japan.","DOI":"10.1109\/IGARSS.2011.6049308"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8830","DOI":"10.3390\/rs70708830","article-title":"The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation","volume":"7","author":"Guanter","year":"2015","journal-title":"Remote Sens."},{"key":"ref_24","unstructured":"National Research Council (2007). Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond, National Academies Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.rse.2015.06.012","article-title":"An introduction to the NASA Hyperspectral InfraRed Imager (HyspIRI) mission and preparatory activities","volume":"167","author":"Lee","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_26","unstructured":"National Academies of Sciences, Engineering, and Medicine (2018). Thriving on Our Changing Planet: A Decadal Strategy for Earth Observation from Space, National Academies Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nieke, J., and Rast, M. (2019, April 22). Towards the Copernicus Hyperspectral Imaging Mission for The Environment (CHIME)\u2014IEEE Conference Publication. Available online: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8518384.","DOI":"10.1109\/IGARSS.2019.8899807"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111214","DOI":"10.1016\/j.rse.2019.111214","article-title":"User needs for future Landsat missions","volume":"231","author":"Wu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","unstructured":"Dennison, P.E., Roberts, D.A., Chambers, J.Q., Daughtry, C.S.T., Guerschman, J.P., Kokaly, R.F., Okin, G.S., Scarth, P.F., Nagler, P.L., and Jarchow, C.J. (2019, August 30). Global Measurement of Non-Photosynthetic Vegetation, Available online: https:\/\/hyspiri.jpl.nasa.gov\/downloads\/RFI2_HyspIRI_related_160517\/RFI2_final_DennisonPhilipE.pdf."},{"key":"ref_30","unstructured":"ASD Inc. (1999). Analytical Spectral Devices, Inc. (ASD) Technical Guide 4th Edition, ASD Inc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., and Driscoll, R.L. (2017). USGS Spectral Library Version 7, Data Series.","DOI":"10.3133\/ds1035"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1016\/j.rse.2007.08.006","article-title":"Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover","volume":"112","author":"Daughtry","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Quemada, M., and Daughtry, C.S.T. (2016). Spectral Indices to Improve Crop Residue Cover Estimation under Varying Moisture Conditions. Remote Sens., 8.","DOI":"10.3390\/rs8080660"},{"key":"ref_34","unstructured":"Roth, K. (2014). Discriminating among Plant Species and Functional Types Using Spectroscopy Data: Evaluating Capabilities Within and Across Ecosystems, across Spatial Scales and through Seasons, University of California."},{"key":"ref_35","unstructured":"Roberts, D., Brown, K., Green, R., Ustin, S., and Hinckley, T. (1998, January 12\u201316). Investigating the Relationship Between Liquid Water and Leaf Area in Clonal Populus. Proceedings of the Summaries of the Seventh JPL Airborne Earth Science Workshop, Pasadena, CA, USA."},{"key":"ref_36","first-page":"3","article-title":"Hyperspectral Vegetation Indices","volume":"Volume 2","author":"Roberts","year":"2019","journal-title":"Hyperspectral Remote Sensing of Vegetation"},{"key":"ref_37","unstructured":"Berk, A., Anderson, G.P., Acharya, P.K., Bernstein, L.S., Muratov, L., Lee, J., Fox, M., Adler-Golden, S.M., Chetwynd, J.H., and Hoke, M.L. (March, January 1). MODTRAN 5: A reformulated atmospheric band model with auxiliary species and practical multiple scattering options: Update. International Society for Optics and Photonics. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, Orlando, FL, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.rse.2013.08.001","article-title":"High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: Radiative transfer modeling and power plant plume detection","volume":"139","author":"Dennison","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2015.02.010","article-title":"Atmospheric correction for global mapping spectroscopy: ATREM advances for the HyspIRI preparatory campaign","volume":"167","author":"Thompson","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_40","unstructured":"Dennison, P.E., Daughtry, C.S.T., Quemada, M., Roth, K.L., Numata, I., Meerdink, S.L., Wetherley, E.B., Gader, P.D., and Roberts, D.A. (2019). Fractional Cover Simulated VSWIR Dataset Version 2, Original 10nm spectra, ECOSIS Spectral Information System."},{"key":"ref_41","unstructured":"Dennison, P.E., Daughtry, C.S.T., Quemada, M., Roth, K.L., Numata, I., Meerdink, S.L., Wetherley, E.B., Gader, P.D., and Roberts, D.A. (2019). Fractional Cover Simulated VSWIR Dataset Version 2, Noise & Atmos. Correction Artifacts Included, ECOSIS Spectral Information System."},{"key":"ref_42","unstructured":"(2019, August 30). Ecological Spectral Information System (ECOSIS). Available online: https:\/\/ecosis.org\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.rse.2005.07.011","article-title":"A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA","volume":"98","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_45","unstructured":"Rouse, J., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/0034-4257(89)90046-1","article-title":"Detection of changes in leaf water content using Near- and Middle-Infrared reflectances","volume":"30","author":"Hunt","year":"1989","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0034-4257(88)90106-X","article-title":"A soil-adjusted vegetation index (SAVI)","volume":"25","author":"Huete","year":"1988","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"28","DOI":"10.2307\/1942049","article-title":"Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types","volume":"5","author":"Gamon","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.rse.2004.10.006","article-title":"On the relationship of NDVI with leaf area index in a deciduous forest site","volume":"94","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"125","DOI":"10.2134\/agronj2001.931125x","article-title":"Discriminating Crop Residues from Soil by Shortwave Infrared Reflectance","volume":"93","author":"Daughtry","year":"2001","journal-title":"Agron. J."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1016\/j.rse.2003.06.001","article-title":"Cellulose absorption index (CAI) to quantify mixed soil-plant litter scenes","volume":"87","author":"Nagler","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.rse.2008.09.004","article-title":"Effects of soil composition and mineralogy on remote sensing of crop residue cover","volume":"113","author":"Serbin","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"864","DOI":"10.2134\/agronj2003.0291","article-title":"Remote sensing the spatial distribution of crop residues","volume":"97","author":"Daughtry","year":"2005","journal-title":"Agron. J."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"971","DOI":"10.3390\/rs1040971","article-title":"An improved ASTER index for remote sensing of crop residue","volume":"1","author":"Serbin","year":"2009","journal-title":"Remote Sens."},{"key":"ref_56","first-page":"55","article-title":"Plant phenolics and absorption features in vegetation reflectance spectra near 1.66 \u03bcm","volume":"43","author":"Kokaly","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00037-6","article-title":"Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models","volume":"65","author":"Roberts","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v018.i02","article-title":"The pls package: Principal component and partial least squares regression in R","volume":"18","author":"Mevik","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_59","first-page":"3","article-title":"Spectroscopy of rocks and minerals, and principles of spectroscopy","volume":"Volume 3","author":"Clark","year":"1999","journal-title":"Manual of Remote Sensing"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00084-4","article-title":"Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression","volume":"67","author":"Kokaly","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"S78","DOI":"10.1016\/j.rse.2008.10.018","article-title":"Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies","volume":"113","author":"Kokaly","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1029\/JB089iB07p06329","article-title":"Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications","volume":"89","author":"Clark","year":"1984","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F. (2011). PRISM: Processing Routines in IDL for Spectroscopic Measurements (Installation Manual and User\u2019s Guide, Version 1.0).","DOI":"10.3133\/ofr20111155"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.rse.2015.01.026","article-title":"Relationships between dominant plant species, fractional cover and land surface temperature in a Mediterranean ecosystem","volume":"167","author":"Roberts","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/S0034-4257(03)00135-4","article-title":"Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE","volume":"87","author":"Dennison","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.rse.2004.07.013","article-title":"A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper","volume":"93","author":"Dennison","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_67","unstructured":"Roberts, D.A., Halligan, K., Dennison, P., Dudley, K., Somers, B., and Crabbe, A. (2019). VIPER Tools User Manual, Version 2.1, University of California Santa Barbara."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"324","DOI":"10.2747\/1548-1603.48.3.324","article-title":"Mapping Plant Functional Types at Multiple Spatial Resolutions Using Imaging Spectrometer Data","volume":"48","author":"Schaaf","year":"2011","journal-title":"Gisci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2012.08.030","article-title":"Comparing endmember selection techniques for accurate mapping of plant species and land cover using imaging spectrometer data","volume":"127","author":"Roth","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1139\/x26-068","article-title":"Determination of carbon fraction and nitrogen concentration in tree foliage by near infrared reflectances: A comparison of statistical methods","volume":"26","author":"Bolster","year":"1996","journal-title":"Can. J. Res."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.rse.2014.05.004","article-title":"Spectroscopic analysis of seasonal changes in live fuel moisture content and leaf dry mass","volume":"150","author":"Qi","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1651","DOI":"10.1890\/13-2110.1","article-title":"Spectroscopic determination of leaf morphological and biochemical traits for northern temperate and boreal tree species","volume":"24","author":"Serbin","year":"2014","journal-title":"Ecol. Appl."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"2180","DOI":"10.1890\/14-2098.1","article-title":"Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties","volume":"25","author":"Singh","year":"2015","journal-title":"Ecol. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S., Lyon, J.G., and Huete, A.R. (2018). Hyperspectral remote sensing tools for quantifying plant litter and invasive species in arid ecosystems. Hyperspectral Remote Sensing of Vegetation, CRC Press.","DOI":"10.1201\/9781315164151-1"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"E12","DOI":"10.1029\/2002JE001847","article-title":"Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems","volume":"108","author":"Clark","year":"2003","journal-title":"J. Geophys. Res. E: Planets"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Tane, Z., Roberts, D., Veraverbeke, S., Casas, \u00c1., Ramirez, C., and Ustin, S. (2018). Evaluating Endmember and Band Selection Techniques for Multiple Endmember Spectral Mixture Analysis using Post-Fire Imaging Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10030389"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5549","DOI":"10.1080\/01431160903311305","article-title":"An automated waveband selection technique for optimized hyperspectral mixture analysis","volume":"31","author":"Somers","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2013.04.006","article-title":"Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests","volume":"136","author":"Somers","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2012.10.026","article-title":"Estimating the fractional cover of growth forms and bare surface in savannas. A multi-resolution approach based on regression tree ensembles","volume":"129","author":"Gessner","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"4787","DOI":"10.1109\/TGRS.2015.2409563","article-title":"Global Land Surface Fractional Vegetation Cover Estimation Using General Regression Neural Networks From MODIS Surface Reflectance","volume":"53","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3427","DOI":"10.3390\/rs6043427","article-title":"Estimating fractional shrub cover using simulated EnMAP data: A comparison of three machine learning regression techniques","volume":"6","author":"Schwieder","year":"2014","journal-title":"Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.rse.2015.01.021","article-title":"Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data","volume":"161","author":"Guerschman","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., King, T.V.V., and Hoefen, T.M. (2013). Surface Mineral Maps of Afghanistan Derived from HyMap Imaging Spectrometer Data, Version 2.","DOI":"10.3133\/ds787"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"489","DOI":"10.5382\/econgeo.2018.4559","article-title":"Application of Imaging Spectroscopy for Mineral Exploration in Alaska: A Study over Porphyry Cu Deposits in the Eastern Alaska Range","volume":"113","author":"Graham","year":"2018","journal-title":"Econ. Geol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.rse.2017.12.012","article-title":"Improved crop residue cover estimates obtained by coupling spectral indices for residue and moisture","volume":"206","author":"Quemada","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.rse.2003.10.016","article-title":"Estimating fractional snow cover from MODIS using the normalized difference snow index","volume":"89","author":"Salomonson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.advwatres.2012.03.002","article-title":"Assessment of methods for mapping snow cover from MODIS","volume":"51","author":"Rittger","year":"2013","journal-title":"Adv. Water Resour."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2006.09.005","article-title":"Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil","volume":"106","author":"Powell","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1080\/01431169508954549","article-title":"Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: Comparative anatomy for cities","volume":"16","author":"Ridd","year":"1995","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2072\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:36Z","timestamp":1760188596000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,4]]},"references-count":89,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182072"],"URL":"https:\/\/doi.org\/10.3390\/rs11182072","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,4]]}}}