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Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK

Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK

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Original languageEnglish
Pages333-347
Number of pages15
JournalCanadian Journal of Remote Sensing
Journal publication date2011
Journal number4
Volume37
StatePublished

Abstract

Leaf area index (LAI) is a key biophysical variable influencing land surface fluxes. Different algorithms have been developed to estimate LAI from remote sensing data. This prompts the need for an evaluation of their comparability and performance. We present an evaluation of the comparability of four products (i.e., MODIS (MOD15A2), NNMERIS, CYCLOPES, and GLOBCARBON) and their performance against in situ LAI for an entire growing season in a broadleaf deciduous forest. All the LAI products detected the phenological trend of this biome reasonably accurately, albeit with differences in absolute values. The MODIS LAI was higher than the in situ LAI throughout the growing season whereas the GLOBCARBON LAI was higher in the summer months. The NN-MERIS was closest to the in situ measurements whereas the CYCLOPES product was lower than the in situ measurements. The NN-MERIS and CYCLOPES LAI were closely matched (RMSE = 0.45), whereas MODIS and CYCLOPES LAI were the most divergent (RMSE = 1.57). All the algorithms were significantly different (p < 0.05) indicating a need for more efforts to harmonize these algorithms. Finally, the spatial consistency between the NN-MERIS LAI and in situ LAI revealed a season dependency trend. Better spatial agreement was observed during the summer season as opposed to early spring and autumn seasons.

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