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Assessment of methods for mapping snow cover from MODIS
Institution:1. National Snow and Ice Data Center, University of Colorado, Boulder CO 80309-0449, USA;2. Earth Research Institute, University of California, Santa Barbara CA 93106-3060, USA;3. Bren School of Environmental Science & Management, University of California, Santa Barbara CA 93106-5131, USA;1. Finnish Environment Institute, Finland;2. Finnish Meteorological Institute, Finland;3. Gamma Remote Sensing AG, Switzerland;4. Norwegian Computing Center, Norway;5. Enveo IT GmbH, Austria;1. Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China;2. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China;3. University of Chinese Academy of Sciences, Beijing, China;4. Northwest Institute of Eco-Environment and Resources, Lanzhou, China;5. School of Geographic Sciences, Xinyang Normal University, Xinyang 464000, China;6. Department of Earth, Ocean, and Atmospheric Science, Florida State University, Tallahassee, FL, USA
Abstract:Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from ?0.16 to 0.04 while differences for MOD10A1 fractional range from ?0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly.
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