Abstract
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 (<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.
Highlights
Leaf area index (LAI) quantifies the amount of live green leaves in the canopy per unit of ground surface
Global LAI products have been operationally provided through several satellite remote sensing projects, such as MODIS [2,3], CYCLOPES [4] and GLOBCARBON [5,6]
Evergreen broadleaf forest can be confused with deciduous broadleaf forest, which causes an underestimation of LAI up to
Summary
Leaf area index (LAI) quantifies the amount of live green leaves in the canopy per unit of ground surface It is an important parameter in various vegetation ecosystem and land surface process models [1]. Previous studies of the effect of land cover misclassification on LAI estimation have adopted either a deterministic or a statistical approach. The deterministic approach simulates the physical radiative transfer processes within vegetation canopies that depend on land cover type. The statistical approach directly explores the relationship between the input biome classification map and the resulting LAI uncertainty, which avoids the usually complex radiative transfer simulation and parameter retrieval processes. Few studies have been conducted systematically using this approach on a global scale In this context, the focus of this paper is to explore the effect of biome misclassification on MODIS. Global LAI climatologies were compared for pure and mixed pixels to explore the impact of different biome misclassification on LAI errors
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