Using crowdsourced and weather station data to fill cloud gaps in MODIS snow cover datasets |
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| Affiliation: | 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China;2. Hydrology and Remote Sensing Laboratory, USDA-ARS, Beltsville, MD, USA;3. Department of Civil and Environmental Engineering, MIT, Cambridge, MA 02139, USA;4. Institute of Surface-Earth System Science, Tianjin University, Tianjin, China;1. Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;2. Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA 02138, USA;3. John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;4. Climate Research Division, Environment Canada, Toronto, ON M3H 5T4, Canada;5. Department of Geography, University of Guelph, 50 Stone Road East, Guelph, Ontario N1G2W1, Canada;6. Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada;7. Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;8. Department of Geography, Colgate University, Hamilton, NY, USA;9. Finnish Meteorological Institute, Arctic Research, P.O. Box 503, FI-00101 Helsinki, Finland;10. Centre d''Applications et de Recherches en Télédétection, Université de Sherbrooke, Sherbrooke, Québec, Canada;11. Centre for Northern Studies, Québec, Canada;12. Joint Center for Global Change Studies, Beijing 100875, China;13. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;14. Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA |
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| Abstract: | We present the design, development, and testing of a new software package for generating snow cover maps. Using a custom inverse distance weighting method, we combine volunteer snow reports, cross-country ski track reports and station measurements to fill cloud gaps in the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover product. The method is demonstrated by producing a continuous daily time step snow probability map dataset for the Czech Republic region. For validation, we checked the ability of our method to reconstruct MODIS snow cover under cloud by simulating cloud cover datasets and comparing estimated snow cover to actual MODIS snow cover. The percent correctly classified indicator showed accuracy between 80 and 90% using this method. The software is available as an R package. The output data sets are published on the HydroShare website for download and through a web map service for re-use in third-party applications. |
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| Keywords: | Snow cover Crowdsourcing Interpolation Winter sports |
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