{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T18:55:34Z","timestamp":1775760934451,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T00:00:00Z","timestamp":1532649600000},"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>Leaf area index (LAI) is a critical vegetation structural parameter in biogeochemical and biophysical ecosystems. High-resolution LAI products play an essential role in regional studies. Empirical methods, which normally use field measurements as their training samples and have been identified as the most commonly used approaches to retrieve structural parameters of vegetation from high-resolution remote-sensing data, are limited by the quality of training samples. Few efforts have been made to generate training samples from existing global LAI products. In this study, two methods (a homogeneous and pure pixel filter method (method A) and a pixel unmixing method (method B)) were developed to extract training samples from moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and LAI products, and a support vector regression (SVR) algorithm trained by the samples was used to retrieve the high-resolution LAI from Landsat data at Baoding, situated in the Hebei Province in China, and Des Moines, situated in Iowa, United States. For the homogeneous and pure pixel filter method, two different sets of training samples were designed. One was composed of upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products (dataset A1); the other was composed of MODIS reflectance and LAI products (dataset A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (dataset B) were extracted from unmixed MODIS surface reflectance and LAI products at 30-m resolution, and the third inversion model was obtained with them. LAI inversion results showed that good agreement with field measurements was achieved using these three inversion models. The R2 (coefficient of determination) value and the root mean square error (RMSE) value were computed to assess the results. For all tests, the R2 values are higher than 0.74 and RMSE values are less than 0.73. These tests showed that three models for the two methods combined with MODIS products can retrieve 30-m resolution LAI from Landsat data. The results of the pixel unmixing method was slightly better than that of the homogeneous and pure pixel filter method.<\/jats:p>","DOI":"10.3390\/rs10081187","type":"journal-article","created":{"date-parts":[[2018,7,27]],"date-time":"2018-07-27T12:20:03Z","timestamp":1532694003000},"page":"1187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products"],"prefix":"10.3390","volume":"10","author":[{"given":"Jianmin","family":"Zhou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Shan","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Hua","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Zhiqiang","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1865-2846","authenticated-orcid":false,"given":"Feng","family":"Gao","sequence":"additional","affiliation":[{"name":"USDA (United States Department of Agriculture), Agricultural Research Service, Hydrology and Remote Sensing Laboratory, 10300 Baltimore Avenue, Beltsville, MD 20705, USA"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0168-1923(91)90074-Z","article-title":"Measuring leaf area index of plant canopies with branch architecture","volume":"57","author":"Chen","year":"1991","journal-title":"Agric. For. Meteorol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.5194\/bg-8-1971-2011","article-title":"Assimilation of soil wetness index and leaf area index into the ISBA-A-gs land surface model: Grassland case study","volume":"8","author":"Barbu","year":"2011","journal-title":"Biogeosci. Discuss."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3637","DOI":"10.3390\/rs5083637","article-title":"Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. Part II: Earth system models","volume":"5","author":"Anav","year":"2013","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"927","DOI":"10.3390\/rs5020927","article-title":"Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011","volume":"5","author":"Zhu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Claverie, M., Matthews, J., Vermote, E., and Justice, C. (2016). A 30+ year AVHRR LAI and FPAR climate data record: Algorithm description and validation. Remote Sens., 8.","DOI":"10.3390\/rs8030263"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TGRS.2013.2237780","article-title":"Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance","volume":"52","author":"Xiao","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.rse.2007.02.018","article-title":"LAI, FPAR and fCover CYCLOPES global products derived from VEGETATION. Part 1: Principles of the algorithm","volume":"110","author":"Baret","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.rse.2012.12.027","article-title":"Geov1: LAI and FPAR essential climate variables and fCover global time series capitalizing over existing products. Part 1: Principles of development and production","volume":"137","author":"Baret","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.rse.2003.05.002","article-title":"Performance of the MISR LAI and FPAR algorithm: A case study in africa","volume":"88","author":"Hu","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"1","article-title":"Deforestation in Michoacan, Mexico, from CYCLOPES-LAI time series (2000\u20132006)","volume":"1\u20138","author":"Baret","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, J., Zhou, H., and Xiao, Z. (2017). Detecting forest disturbance in northeast China from GLASS LAI time series data using a dynamic model. Remote Sens., 9.","DOI":"10.3390\/rs9121293"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.rse.2005.04.003","article-title":"Evaluation of seasonal variation of MODIS derived leaf area index at two European deciduous broadleaf forest sites","volume":"96","author":"Wang","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.isprsjprs.2015.04.013","article-title":"Experimental sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods\u2014A comparison","volume":"108","author":"Verrelst","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"3417","article-title":"Retrieving LAI in the heihe and the hanjiang river basins using Landsat images for accuracy evaluation on MODIS LAI product","volume":"3417\u20133421","author":"Zhang","year":"2007","journal-title":"Int. Geosci. Remote Sens. Symp. (IGARSS)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/S0034-4257(01)00300-5","article-title":"Derivation and validation of Canada-wide coarse-resolution leaf area index maps using high-resolution satellite imagery and ground measurements","volume":"80","author":"Chen","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"12","article-title":"Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data","volume":"26","author":"Duan","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.rse.2006.09.031","article-title":"Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer","volume":"107","author":"Durbha","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_19","first-page":"240","article-title":"Comparison of winter wheat yield estimation by sequential assimilation of different spatio-temporal resolution remotely sensed LAI datasets","volume":"46","author":"Huang","year":"2015","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_20","first-page":"245","article-title":"Applicability of pywofost model based on ensemble kalman filter in simulating maize yield in northeast China","volume":"33","author":"Chen","year":"2012","journal-title":"Chin. J. Agrometeorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"063554","DOI":"10.1117\/1.JRS.6.063554","article-title":"Simple method for retrieving leaf area index from Landsat using MODIS leaf area index products as reference","volume":"6","author":"Gao","year":"2012","journal-title":"J. Appl. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3468","DOI":"10.1016\/j.rse.2011.08.010","article-title":"Comparison of different vegetation indices for the remote assessment of green leaf area index of crops","volume":"115","author":"Gitelson","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"48","article-title":"Inversion of masson pine forest LAI by multiple-perspective vegetation index","volume":"35","year":"2017","journal-title":"Plant Sci. J."},{"key":"ref_24","first-page":"756","article-title":"Estimating time series leaf area index based on recurrent neural networks","volume":"24","author":"Linna","year":"2009","journal-title":"Adv. Earth Sci."},{"key":"ref_25","first-page":"875","article-title":"Recent progress in land surface quantitative remote sensing","volume":"20","author":"Liang","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_26","unstructured":"Lv, J. (2012). Hyperspectral Remote Sensing Inversion Models of Crop Chlorophy || Content Based on Machine Learning and Radiative Transfer Models, China University of Geosciences."},{"key":"ref_27","first-page":"203","article-title":"Support vector regression","volume":"11","author":"Awad","year":"2007","journal-title":"Neural Inf. Process. Lett. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1080\/02757259609532303","article-title":"A review of mixture modeling techniques for sub-pixel land cover estimation","volume":"13","author":"Ichoku","year":"1996","journal-title":"Remote Sens. Rev."},{"key":"ref_29","first-page":"55","article-title":"A review of pixel unmixing models","volume":"55\u201358","year":"2003","journal-title":"Remote Sens. Inf."},{"key":"ref_30","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":"Settle","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liao, L., Song, J., Wang, J., Xiao, Z., and Wang, J. (2016). Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI. Remote Sens., 8.","DOI":"10.3390\/rs8060452"},{"key":"ref_33","first-page":"281","article-title":"Support vector method for function approximation, regression estimation, and signal processing","volume":"9","author":"Vapnik","year":"1997","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","first-page":"27","article-title":"Libsvm: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"China: Open access to earth land-cover map","volume":"514","author":"Chen","year":"2014","journal-title":"Nature"},{"key":"ref_37","unstructured":"Chen, J., Liao, A., Chen, J., Peng, S., Chen, L., and Zhang, H. (2017). 30-Meter Global Land Cover Data Product-Globe Land30, Geomatics World."},{"key":"ref_38","unstructured":"Knyazikhin, Y., Glassy, J., Privette, J.L., Tian, Y., Lotsch, A., Zhang, Y., Wang, Y., Morisette, J.T., Votava, P., and Myneni, R.B. (2018, May 11). MODIS Leaf Area Index (LAI) and Fractionof Photosynthetically Active Radiation Absorbed by Vegetation (FPAR) Product (MOD15) Algorithm Theoretical Basis Document, Available online: http:\/\/eospso.gsfc.nasa.gov\/atbd\/modistables.html."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1109\/TGRS.2006.871214","article-title":"Analysis of leaf area index and fraction of par absorbed by vegetation products from the terra modis sensor 2000\u20132005","volume":"44","author":"Yang","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.rse.2004.03.019","article-title":"Upscaling ground observations of vegetation water content, canopy height, and leaf area index during SMEX02 using aircraft and Landsat imagery","volume":"92","author":"Anderson","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_41","first-page":"335","article-title":"Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat","volume":"44","author":"Liang","year":"2015","journal-title":"Infrared Laser Eng."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:14:52Z","timestamp":1760195692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,27]]},"references-count":41,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081187"],"URL":"https:\/\/doi.org\/10.3390\/rs10081187","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,27]]}}}