{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T00:40:04Z","timestamp":1768437604332,"version":"3.49.0"},"reference-count":67,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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>It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10\u201330 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.<\/jats:p>","DOI":"10.3390\/rs12010115","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T04:43:03Z","timestamp":1578026583000},"page":"115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval"],"prefix":"10.3390","volume":"12","author":[{"given":"Cuicui","family":"Ji","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6048-1577","authenticated-orcid":false,"given":"Xiaosong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huaidong","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Desertification and Aeolian Sand Disaster Combating, Gansu Desert Control Research Institute, Lanzhou 730070, China"},{"name":"School of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sike","family":"Li","sequence":"additional","affiliation":[{"name":"Earth Atmosphere and Environment, Science Faculty, Monash University, Clayton, VIC 3800, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,1]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","unstructured":"Li, X.S., Zheng, G.X., Wang, J.Y., Ji, C.C., Sun, B., and Gao, Z.H. (2016). Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data. Remote Sens., 8.","DOI":"10.3390\/rs8100800"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1126\/science.1131634","article-title":"Global desertification: Building a science for dryland development","volume":"316","author":"Reynolds","year":"2007","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1016\/j.rse.2005.05.023","article-title":"Spectral mixture analyses of hyperspectral data acquired using a tethered balloon","volume":"103","author":"Chen","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.compag.2005.03.003","article-title":"Identification of red and NIR spectral regions and vegetative indices for discrimination of cotton nitrogen stress and growth stage","volume":"48","author":"Zhao","year":"2005","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2006.09.018","article-title":"Relative spectral mixture analysis\u2014A multitemporal index of total vegetation cover","volume":"106","author":"Okin","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0034-4257(00)00126-7","article-title":"A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation","volume":"74","author":"Asner","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"165","DOI":"10.2134\/agronj1995.00021962008700020005x","article-title":"Potential for Discriminating Crop Residues from Soil by Reflectance and Fluorescence","volume":"87","author":"Daughtry","year":"1995","journal-title":"Agron. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/S0034-4257(98)00085-6","article-title":"Effects of Band Positioning and Bandwidth on NDVI Measurements of Tropical Savannas","volume":"67","author":"Filho","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2006.07.013","article-title":"Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data","volume":"105","author":"Garrigues","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_12","unstructured":"Lehnert, L., Meyer, H., Thies, B., Reudenbach, C., and Bendix, J. (May, January 27). Monitoring plant cover on the Tibetan Plateau: A multi-scale remote sensing based approach. Proceedings of the Egu General Assembly Conference, Vienna, Austria."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/S0034-4257(96)00248-9","article-title":"Effects of spectral, spatial; radiometric characteristics on remote sensing vegetation indices of forested regions","volume":"61","author":"Teillet","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"15361","DOI":"10.3390\/rs71115361","article-title":"Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale","volume":"7","author":"Clasen","year":"2015","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.rse.2011.12.004","article-title":"Landsat remote sensing approaches for monitoring long-term tree cover dynamics in semi-arid woodlands: Comparison of vegetation indices and spectral mixture analysis","volume":"119","author":"Yang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.rse.2012.11.021","article-title":"Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data","volume":"130","author":"Okin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"11","article-title":"Automated spectral unmixing of AVIRIS data using convex geometry concepts","volume":"1","author":"Boardman","year":"1993","journal-title":"Summ. Annu. JPL Airborne Geosci. Workshop"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/0034-4257(94)90107-4","article-title":"Nonlinear spectral mixing models for vegetative and soil surfaces","volume":"47","author":"Borel","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/0034-4257(95)00171-9","article-title":"Nonlinear spectral mixing in desert vegetation","volume":"55","author":"Ray","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2015.08.001","article-title":"Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation","volume":"108","author":"Marshall","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Wang, W., and Duan, Z. (2017). Gansu Yearbook, Chinese Literature Press."},{"key":"ref_26","unstructured":"Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P., and Stewart, J.B. (2011). Field Measurement of Fractional Ground Cover: A Technical Handbook Supporting Ground Cover Monitoring for Australia."},{"key":"ref_27","unstructured":"Pieters, C.M., and Englert, P.A.J. (1993). Imaging spectroscopy: Interpretation based on spectral mixture analysis. Remote Geochem. Anal. Top. Remote Sens, Cambridge University Press."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1080\/01431169308904402","article-title":"Linear Mixing and the Estimation of Ground Cover Proportions","volume":"14","author":"Drake","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8098","DOI":"10.1029\/JB091iB08p08098","article-title":"Spectral mixture modeling: A new analysis of rock and soil types at the Viking Lander I site","volume":"91","author":"Adams","year":"1986","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_30","unstructured":"Gillespie, A.R., Smith, M.O., Adams, J.B., Willis, S.C., Fischer, A.F., and Sabol, D.E. (1990, January 4\u20135). Interpretation of Residual Images: Spectral Mixture Analysis of AVIRIS Images. Proceedings of the 2nd AVIRIS Workshop, Owens Valley, Pasadena, CA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/01431160010019652","article-title":"An evaluation of spectral mixture modelling applied to a semi-arid environment","volume":"23","author":"Theseira","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.rse.2003.06.004","article-title":"Snow-cover mapping in forests by constrained linear spectral unmixing of MODIS data","volume":"88","author":"Vikhamar","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S0034-4257(02)00136-0","article-title":"Estimating impervious surface distribution by spectral mixture analysis","volume":"84","author":"Wu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4157","DOI":"10.1080\/01431160600993454","article-title":"Nonlinear estimation of subpixel proportion via kernel least square regression","volume":"28","author":"Zhang","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1109\/36.843007","article-title":"Constrained subpixel target detection for remotely sensed imagery","volume":"38","author":"Chang","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/36.911111","article-title":"Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery","volume":"39","author":"Heinz","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"Sel. Top. Appl. Earth Obs. Remote Sens. IEEE J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TSP.2012.2222390","article-title":"Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture\/Nonlinear-Fluctuation Model","volume":"61","author":"Chen","year":"2013","journal-title":"Signal Process. IEEE Trans."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1555","DOI":"10.1007\/s11760-012-0392-3","article-title":"Blind nonlinear hyperspectral unmixing based on constrained kernel nonnegative matrix factorization","volume":"8","author":"Li","year":"2014","journal-title":"Signal Image Video Process."},{"key":"ref_40","first-page":"607","article-title":"Unsupervised nonlinear decomposing method of hyperspectral imagery","volume":"45","author":"Li","year":"2011","journal-title":"J. Zhejiang Univ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1098\/rsta.1909.0016","article-title":"Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations","volume":"209","author":"Mercer","year":"1909","journal-title":"Philos. Trans. R. Soc. Lond."},{"key":"ref_43","unstructured":"Zaanen, A.C. (1956). Linear Analysis, Bibliotheca Mathematica, North-Holland Publishing Co."},{"key":"ref_44","unstructured":"Bhatia, R. (2015). Positive Definite Matrices, Princeton University Press."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1109\/TIP.2016.2627815","article-title":"Nonlinear unmixing of hyperspectral data with vector-valued kernel functions","volume":"26","author":"Ammanouil","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1012450327387","article-title":"Choosing Multiple Parameters for Support Vector Machines","volume":"46","author":"Chapelle","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Broadwater, J., Chellappa, R., Banerjee, A., and Burlina, P. (2007, January 23\u201328). Kernel fully constrained least squares abundance estimates. Proceedings of the 2007 IEEE Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423736"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G., and Bruzzone, L. (2009). Kernel Methods for Remote Sensing Data Analysis, Wiley.","DOI":"10.1002\/9780470748992"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/TGRS.2014.2358620","article-title":"Linear Spectral Mixture Analysis via Multiple-Kernel Learning for Hyperspectral Image Classification","volume":"53","author":"Liu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2951","DOI":"10.1080\/01431160802558659","article-title":"Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data","volume":"30","author":"Fan","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Ji, C., Jia, Y., Gao, Z., Wei, H., and Li, X. (2017). Nonlinear spectral mixture effects for photosynthetic\/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0189292"},{"key":"ref_52","first-page":"3643","article-title":"Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 hyperion data","volume":"35","author":"Li","year":"2015","journal-title":"Acta Ecol. Sin."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4910","DOI":"10.1109\/TGRS.2015.2413409","article-title":"Assessment of Multiple Scattering in the Reflectance of Semiarid Shrublands","volume":"53","author":"Wang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1117\/12.555769","article-title":"Shadow fraction in spectral mixture analysis of a cotton canopy","volume":"5544","author":"Fitzgerald","year":"2004","journal-title":"Remote Sens. Model. Ecosyst. Sustain."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.rse.2005.05.020","article-title":"Multiple shadow fractions in spectral mixture analysis of a cotton canopy","volume":"97","author":"Fitzgerald","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/S0034-4257(03)00145-7","article-title":"Coupling spectral unmixing and trend analysis for monitoring of long-term vegetation dynamics in Mediterranean rangelands","volume":"87","author":"Hostert","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_57","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_58","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0034-4257(02)00079-2","article-title":"Towards an operational MODIS continuous field of percent tree cover algorithm: Examples using AVHRR and MODIS data","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/S0034-4257(02)00080-9","article-title":"Development of a MODIS tree cover validation data set for Western Province, Zambia","volume":"83","author":"Hansen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jaridenv.2006.09.020","article-title":"The effect of spatial resolution on measurement of vegetation cover in three Mojave Desert shrub communities","volume":"67","author":"Frank","year":"2006","journal-title":"J. Arid Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/0034-4257(95)00132-K","article-title":"Comparison of broad-band and narrow-band red and near-infrared vegetation indices","volume":"54","author":"Elvidge","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6893","DOI":"10.1080\/01431161.2018.1468105","article-title":"Non-photosynthetic vegetation biomass estimation in semiarid Canadian mixed grasslands using ground hyperspectral data, Landsat 8 OLI, and Sentinel-2 images","volume":"39","author":"Li","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.rse.2019.01.031","article-title":"Chlorophyll content estimation in an open-canopy conifer forest with Sentinel-2A and hyperspectral imagery in the context of forest decline","volume":"223","author":"Hornero","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/S0034-4257(99)00067-X","article-title":"Hyperspectral vegetation indices and their relationships with agricultural crop characteristics","volume":"71","author":"Thenkabail","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_65","first-page":"1063","article-title":"Spectral mixing mechanism analysis of photosynthetic\/non-photosynthetic vegetation and bared soil mixture in the Hunshandake (Otindag) sandy land","volume":"36","author":"Zheng","year":"2016","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.rse.2015.04.020","article-title":"Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach","volume":"164","author":"Lehnert","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2945","DOI":"10.1109\/TGRS.2011.2121073","article-title":"A Quantitative Analysis of Virtual Endmembers\u2019 Increased Impact on the Collinearity Effect in Spectral Unmixing","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:03:27Z","timestamp":1760364207000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/1\/115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["rs12010115"],"URL":"https:\/\/doi.org\/10.3390\/rs12010115","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}