Retrieving leaf area index using ICESat/GLAS full-waveform data

Abstract

Leaf area index (LAI) is an important parameter controlling many biological and physical processes associated with vegetation on the Earth's surface. In this study, an algorithm for estimating LAI from the ICESat (Ice, Cloud and land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System) data was proposed and applied to a forest area in the Tibetan Plateau. First, Gaussian decomposition of the GLAS waveform was implemented to identify the ground peaks and calculate the ground and canopy return energy. Second, the ground-to-total energy ratio (E _r) was computed as the ratio of the ground return energy to the total waveform return energy for each GLAS footprint. Third, a regression model between the E _r and the field-measured LAI was established based on the Beer–Lambert law. The coefficient of determination (R ²) of the model was 0.81 and the root mean square error (RMSE) is 0.35 (n = 23, p < 0.001). Finally, the leave-one-out cross-validation procedure was used to assess the constructed regression model. The results indicate that the regression model is not overfitting the data and has a good generalization capability. We validated the accuracy of the GLAS-predicted LAIs using the other 15 field-measured LAIs (R ² = 0.84), and the result shows that the accuracy of the GLAS-predicted LAI is high (RMSE = 0.31).

Acknowledgements

This work was supported by the National Basic Research Program (973 Program) of China (2010CB951701), the Major International Cooperation and Exchange Project of National Natural Science Foundation of China (No. 41120114001), the National Natural Science Foundation of China (No. 41271428, No. 41171279) and the 100 Talents Program of the Chinese Academy of Sciences. We thank the National Snow and Ice Data Center for providing the GLAS data. We also thank three anonymous reviewers for many constructive comments on the manuscript.