Leaf Area Index (LAI) indicates the amount of live green leaf above ground surface. Many agro-meteorology, atmospheric general circulation, and biogeochemical models rely on LAI to parameterize the vegetation interactions with the atmosphere. A series of LAI products have been generated with different spatial and temporal resolutions from optical satellite sensors. However, the uncertainties of current LAI products are still unclear for most cases, which greatly hampers the proper application of these products in ecosystem and landsurface models.

Land cover information is commonly used to parameterize canopy radiative transfer models or models that require land cover stratification, such as for the MODIS LAI product. Errors in classifying land cover type may thus propagate into LAI uncertainties during the parameter retrieval process. The accuracy of current global land cover product is only around 60%–80%. Understanding the impact of land cover misclassification on LAI estimation is thus critical to improving LAI retrieval from remote sensing imagery.

Prof. FANG Hongliang, Institute of Geographic Sciences and Natural Resources Research (IGSNRR) and his colleagues developed a novel statistical approach to quantify the global MODIS LAI uncertainties introduced by potential land cover misclassification.

Their study shows that biome misclassification does not necessary translate into strong disagreement in LAI retrievals. Misclassification between herbaceous types has minimal impact on LAI retrievals (<0.37 or 27.0%). Biome misclassification generally leads to an LAI overestimation for savanna, but an underestimation for forests. The largest errors caused by misclassification are for savanna (0.51), followed by evergreen needleleaf forest (0.44) and broadleaf forests (~0.31).

Researchers concluded that biome misclassification is a major factor contributing to LAI uncertainties for savanna, while for forests, the main source of uncertainties may be due to algorithm deficits, especially in summer. Therefore, “further efforts should be focused on improving the biome classification for the structurally complex savanna systems and refinement of the retrieval algorithms for forest biomes” said FANG Hongliang.

Their work was supported by the Chinese Academy of Sciences and the National Natural Science Foundation of China (41171333). The complete paper is available on the MDPIRemote Sensingopen access web site http://www.mdpi.com/2072-4292/5/2/830.