Mapping leaf area index using spatial, spectral, and temporal information from multiple sensors

Leaf area index (LAI) is one of the most important biophysical parameters for modeling ecosystem processes such as carbon and water fluxes. Remote sensing provides the only feasible option for mapping LAI continuously over landscapes, but existing methodologies have significant limitations. There is a tradeoff between spatial and temporal resolutions inherent in remotely sensed images, i.e. high spatial resolution images may only be collected infrequently, whereas imagery with fine temporal resolution has necessarily coarser spatial resolution. LAI products created using a single sensor inherit the spatial and temporal characteristics of that sensor. Moreover, the majority of developed algorithms in the literature use spectral information alone, which suffers from the serious limitation of signal saturation at moderately high LAI. We developed a novel approach for mapping effective LAI (Lₑ) using spectral information from Landsat, spatial information from IKONOS, and temporal information from MODIS, which overcomes these limitations. The approach is based on an empirical model developed between Lₑ measured on the ground and spectral and spatial information from remotely sensed images to map annual maximum and minimum Lₑ. A phenological model was fit to a time series of MODIS vegetation indices which was used to model the trajectory between annual minimum and maximum Lₑ. This approach was able to generate maps of Lₑ at Landsat spatial resolution with daily temporal resolution. We tested the approach in the North Carolina Piedmont and generated daily maps of Lₑ for a 100km² area. Modeled Lₑ compared well with time series of LAI estimates from two AmeriFlux sites within the study area. A comparison of the MODIS LAI product with spatially averaged Lₑ estimates from our model showed general agreement in forested areas, but large differences in developed areas. This model takes advantage of multidimensional information available from multiple remote sensors and offers significant improvements for mapping leaf area index, particularly for forested areas where spectral indices tend to saturate.