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Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology

Evaluation of Landsat and MODIS data fusion products for analysis of dryland forest phenology Current satellite sensors provide data of insufficient spatial and temporal resolutions to fully characterize the patchy phenology patterns of dryland forests. The spatial and temporal adaptive reflectance fusion model (STARFM) is an algorithm that fuses Landsat 30 m data with MODIS 500 m data to produce synthetic imagery at Landsat spatial resolution and MODIS time steps. In this study, we evaluated the feasibility of using STARFM to produce synthetic imagery over a dryland vegetation study site for the purpose of tracking phenological changes. We assembled subsets of six Landsat-5 TM scenes and temporally-coincident MODIS datasets spanning the 2006 April–October growing season in central-northern Arizona, which is characterized by large tracts of dryland forest. To investigate the effects of temporal compositing, BRDF-adjustment, and base pair timing on the accuracy of the resulting synthetic imagery, we employed a range of MODIS 500 m surface reflectance datasets (daily, 8-day composite, and 16-day Nadir BRDF-Adjusted Reflectance (NBAR)) as well as initial Landsat/MODIS imagery pairs from opposite ends of the growing season. The STARFM algorithm was applied to each MODIS data series to produce up to twelve synthetic images corresponding to each Landsat image. We evaluated the accuracy of the synthetic images by comparing the reflectance values of a random sample of the vegetation pixels with the corresponding pixel values of the reference Landsat image on a band-by-band basis. Our results indicate that the NBAR imagery is the optimal dataset for use with Landsat-5 TM data in this area. The NBAR dataset consistently returned the lowest absolute difference values and the highest correlations. A comparison of landscape-scale maps of the timing and value of the peak NDVI derived from STARFM, Landsat, and MODIS (NBAR) time series across the full 2006 growing season shows the effect of the heightened spatial and temporal resolution offered by a STARFM-based dataset. This work demonstrates the feasibility of using the STARFM algorithm to assemble an imagery time series at Landsat spatial resolution and MODIS temporal resolution in vegetated dryland ecosystems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier
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