Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resoluti...Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy （CHRIS/PROBA） images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that perfor- mance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hope- fully improve the performance of VIs in LAI estimation, which will require further investigation.展开更多
文摘Sensitivity analyses were conducted for the retrieval of vegetation leaf area index （LAI） from multi- angular imageries in this study. Five spectral vegetation indices （VIs） were derived from Compact High Resolution Imaging Spectrometer onboard the Project for On Board Autonomy （CHRIS/PROBA） images, and were related to LAI, acquired from in situ measurement in Jiangxi Province, China, for five vegetation communities. The sensitivity of LAI retrieval to the variation of VIs from different observation angles was evaluated using the ratio of the slope of the best-fit linear VI-LAI model to its root mean squared error. Results show that both the sensitivity and reliability of VI-LAI models are influenced by the heterogeneity of vegetation communities, and that perfor- mance of vegetation indices in LAI estimation varies along observation angles. The VI-LAI models are more reliable for tall trees than for low growing shrub-grasses and also for forests with broad leaf trees than for coniferous forest. The greater the tree height and leaf size, the higher the sensitivity. Forests with broad-leaf trees have higher sensitivities, especially at oblique angles, while relatively simple-structured coniferous forests, shrubs, and grasses show similar sensitivities at all angles. The multi-angular soil and/or atmospheric parameter adjustments will hope- fully improve the performance of VIs in LAI estimation, which will require further investigation.
