Evaluation of the performance of the MODIS LAI and FPAR algorithm with multiresolution satellite data

Green leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by vegetation (FPAR) are two key variables of vegetated surfaces because of the important role they play in biosphere-atmosphere interactions. Accurate global estimates of these parameters are essential for understanding and predicting the future state of the climate and terrestrial ecosystems. The objective of this research is to evaluate the performance of a LAI/FPAR algorithm designed for the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA TERRA spacecraft, with special emphasis on the effects of scale, or spatial resolution. Results from prototyping exercises prior to the launch of MODIS demonstrated the feasibility of physically valid retrievals with the algorithm. It was found that land cover misclassifications between distinct biomes could fatally impact the retrievals. A comparison of coarse (16 km) and fine (30 m) resolution retrievals highlighted the scale dependence of the algorithm. Investigation of the effect of land cover mixtures within coarse resolution pixels shows that LAI retrieval errors are inversely related to the proportion of the dominant land cover in a pixel. Errors are particularly large when forests are minority biomes in non-forest pixels. A physically based theory for scaling with an explicit scale dependent radiative transfer formulation was developed and successfully applied to scale the algorithm to various resolutions of satellite data. Consistency between LAI retrievals from 30 m Landsat Enhanced Thematic Mapper Plus (ETM+) data and field measurements from Maun (Botswana) indicates good performance of the algorithm. LAI values for coarse resolution data are underestimated if the resolution of the data is not considered in the retrieval technique. Hierarchical variance analysis of data from Maun, Harvard Forest (USA) and Ruokulahti Forest (Finland) indicates that LAI estimates derived from ETM+ data exhibit multiple characteristic scales of spatial variation. Isolating the effects associated with different scales through variograms aids the development of a new sampling strategy for validation of MODIS products.