Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK. / Ogutu, Booker; Dash, Jadunandan; Dawson, Terence P.
In: Canadian Journal of Remote Sensing, Vol. 37, No. 4, 2011, p. 333-347.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Evaluation of leaf area index estimated from medium spatial resolution remote sensing data in a broadleaf deciduous forest in southern England, UK
A1 - Ogutu,Booker
A1 - Dash,Jadunandan
A1 - Dawson,Terence P.
AU - Ogutu,Booker
AU - Dash,Jadunandan
AU - Dawson,Terence P.
PY - 2011
Y1 - 2011
N2 - <p>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.</p>
AB - <p>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.</p>
UR - http://pubs.casi.ca/doi/abs/10.5589/m11-043
M1 - Article
JO - Canadian Journal of Remote Sensing
JF - Canadian Journal of Remote Sensing
SN - 1712-7971
IS - 4
VL - 37
SP - 333
EP - 347
ER -