{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T22:26:05Z","timestamp":1769725565195,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2013,2,15]],"date-time":"2013-02-15T00:00:00Z","timestamp":1360886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding the impact of vegetation mixture and misclassification on leaf area index (LAI) estimation is crucial for algorithm development and the application community. Using the MODIS standard land cover and LAI products, global LAI climatologies and statistics were obtained for both pure and mixed pixels to evaluate the effects of biome mixture on LAI estimation. Misclassification between crops and shrubs does not generally translate into large LAI errors (&lt;0.37 or 27.0%), partly due to their relatively lower LAI values. Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are also found for savanna (0.51), followed by evergreen needleleaf forests (0.44) and broadleaf forests (~0.31). Comparison with MODIS uncertainty indicators show that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main uncertainties may be introduced by algorithm deficits, especially in summer. The LAI climatologies for pure pixels are recommended for land surface modeling studies. Future studies should focus on improving the biome classification for savanna systems and refinement of the retrieval algorithms for forest biomes.<\/jats:p>","DOI":"10.3390\/rs5020830","type":"journal-article","created":{"date-parts":[[2013,2,15]],"date-time":"2013-02-15T11:04:41Z","timestamp":1360926281000},"page":"830-844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["The Impact of Potential Land Cover Misclassification on MODIS Leaf Area Index (LAI) Estimation: A Statistical Perspective"],"prefix":"10.3390","volume":"5","author":[{"given":"Hongliang","family":"Fang","sequence":"first","affiliation":[{"name":"LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Wenjuan","family":"Li","sequence":"additional","affiliation":[{"name":"LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Ranga","family":"Myneni","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, Boston University, Boston, MA 02215, USA"}]}],"member":"1968","published-online":{"date-parts":[[2013,2,15]]},"reference":[{"key":"ref_1","unstructured":"Available online: http:\/\/www.wmo.int\/pages\/prog\/gcos\/Publications\/gcos-154.pdf (accessed on 13 November 2012)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.rse.2006.05.026","article-title":"Stochastic transport theory for investigating the three-dimensional canopy structure from space measurements","volume":"112","author":"Huang","year":"2008","journal-title":"Remote Sens. 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