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An integrated approach to estimating LAI using multitemporal and multidirectional remote sensing measurements

Leaf area index (LAI) is one of the most important physical parameters in controlling hydrological processes across the geosphere-biosphere-atmosphere boundaries. Estimation of this parameter using remote sensing techniques has been associated with computation of vegetation indices (VIs). The inversion of bidirectional reflectance distribution function modeling approaches appear to be promising, but require simultaneous bidirectional observations. Attempts have been made to substitute multitemporal observations for simultaneous multidirectional measurements. This approach works well if the surface of interest consists of slow-growing vegetation. When applied to fast-growing vegetative surfaces, multitemporal observations cannot be used in BRDF model inversion because of changes in vegetation amount. In this study, a growth compensation model is proposed with the use of VIs, which is applied to multitemporal observations over a fast-growing alfalfa canopy. The compensated data are then used in the inversion of BRDF models to estimate LAI. The results showed that this approach is promising and there exists a potential for operational applications.