Improved LAI Algorithm Implementation to MODIS Data by Incorporating Background, Topography, and Foliage Clumping Information

Leaf area index (LAI) is one of the essential biogeophysical variables related to terrestrial carbon and biogeochemical cycles. The University of Toronto (UofT) LAI product is developed in order to support the European Space Agency GLOBCARBON project for global and climate change assessments. The climate and global change communities have recently requested for a daily 250-m LAI product in order to improve the spatial and temporal patterns of carbon pools and fluxes knowledge. In light of these considerations, we carry out further improvements on the UofT LAI algorithm, including enhanced spatial resolution (250 m) by considering an improved land cover map, local topography, clumping index, and background reflectance variations in order to produce canopy LAI time series. Here, we present the methodological framework and an evaluation of 250-m UofTv2 LAI estimates in forest stands of the Canadian Carbon Program fluxnet sites. The LAI distributions over Canada and the comparison with ground measurements show an improved LAI estimates from the UofT v2 LAI algorithm as compared with the UofT v1 LAI algorithm. One of the key differences between v1 and v2 UofT LAI product is that the former produces total LAI whereas the latter produces overstorey LAI in forest and total LAI in other vegetated land cover types. A daily LAI product can further be extracted from the 10-day UofT v2 LAI time series by fitting various curve fitting algorithms. Although, we have shown the LAI product only over Canada, the algorithm can also be extended for a global 250-m LAI product.