Boston University

Land cover, vegetation green leaf area index (LAI) and the fraction of incident photosynthetically active radiation absorbed by vegetation (FPAR) govern the exchange of energy, momentum and mass (water and carbon dioxide, for example) between the Earth’s surface and the atmosphere. These variables are operationally derived from measurements of the Multiangle Imaging SpectroRadiometer (MISR) aboard the NASA’s Terra spacecraft, which was launched in December of 1999. The primary objectives of this research are to evaluate and refine the MISR LAI and FPAR algorithm in terms of retrieval quality, accuracy, spatial and temporal coverage. Further understanding and modeling of environmental processes requires land cover and land use maps at broad spatial scales. Therefore, a secondary objective is to develop a spatial aggregation algorithm that represents fine resolution land cover information from satellite data at coarser resolutions with minimal information loss for use in large scale modeling studies. A principal objective of the MISR LAI and FPAR retrieval algorithm is to retrieve LAI and FPAR without requiring a static, pre-specified global biome map. The spatial and temporal coverage of the MISR LAI and FPAR product are mainly limited by cloud vi contamination. The algorithm provides retrievals in 50-80 percent of pixels with suitable input. Earlier versions of the algorithm overestimated LAI in grasses and broadleaf crops. Retrievals from the recalibrated algorithm show structural and phenological variability in agreement with field data. The product is accurate to within 0.5 units in herbaceous vegetation and savannas and overestimates LAI by about 1 unit in broadleaf forests. The land cover aggregation algorithm uses patterns in fine resolution images to preferentially aggregate blocks that show homogeneity, majority and adjacency. Disappearance of classes is avoided by predefining the number of pixels from each class that should be present in the coarser resolution data set. This technique conserves the complex patterns in the original image. Land cover maps generated using this aggregation method are found to be more similar to the original image than those created by random and majority aggregation and thus can better represent fine resolution land cover information for use in large scale modeling studies.