Prototyping of MODIS LAI and FPAR algorithm with LASUR and LANDSAT data

This paper describes results from prototyping of the moderate resolution imaging spectroradiometer (MODIS) radiative transfer-based synergistic algorithm for the estimation of global leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation using land surface reflectances (LASUR) and Landsat data. The algorithm uses multispectral surface reflectances and a land cover classification map as input data to retrieve global LAI and FPAR fields. Our objectives are to evaluate its performance as a function of spatial resolution and uncertainties in surface reflectances and the land cover map. We analyzed reasons the algorithm can or cannot retrieve a value of LAI/FPAR from the reflectance data and justified the use of more complex algorithms, instead of NDVI-based methods. The algorithm was tested to investigate the effects of vegetation misclassification on LAI/FPAR retrievals. Misclassification between distinct biomes can fatally impact the quality of the retrieval, while the impact of misclassification between spectrally similar biomes is negligible. Comparisons of results from the coarse and fine resolution retrievals show that the algorithm is dependent on the spatial resolution of the data. By evaluating the data density distribution function, we can adjust the algorithm for data resolution and utilize the algorithm with data from other sensors.