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Multi-Sensor Snow Mapping and Global Trends

This workshop discusses legacy data sets, cross-calibration of sensors, and trend analysis for snow mapping using optical and passive microwave technologies.

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Multi-Sensor Snow Mapping and Global Trends

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  1. Multi-Sensor Snow Mapping and Global Trends R. L. Armstrong, M.J. Brodzik, M. Savoie and K. Knowles University of Colorado, Boulder, USA WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  2. Outline Legacy Data Sets for Hemispheric-Scale Snow Mapping Cross-calibration of SMMR and SSM/I Comparison of Optical and Microwave Snow Cover Time Series Snow Mapping using Blended Optical and Passive Microwave Trend Analysis WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  3. Source for visible data: NOAA Weekly/Daily Snow Charts since 1966 Longest time-series of any parameter derived from satellite remote sensing NOAA IMS – Interactive Multisensor Snow and Ice Mapping System – http://www.ssd.noaa.gov/PS/SNOW WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  4. Source for passive microwave data SMMR, SSM/I &AMSR-E Brightness Temperatures 1978-2006 EASE-Grid (Equal-Area Scalable Earth Grid) Full Global, North and South Hem. Projections NOAA/NASA Pathfinder Level 3 Gridded Data Available from NSIDC: http://nsidc.org WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  5. Northern Hemisphere Satellite-Derived Snow Extent1978 – 2006 Visible (NOAA) Passive Microwave (SMMR & SSM/I) microwave (green/blue) vs. visible (pink) sensors, Analysis of the microwave data is complicated by the change in sensors from SMMR to SSM/I WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  6. SMMR SSM/I Sensors are similar but exhibit important differences in spatial and temporal coverage that affect the sampling density within the long-term record. Daily passive microwave 37 GHz, horizontally polarized brightness temperatures, July 19, 1987, showing smaller coverage area of SMMR (left) vs. SSM/I (right). WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  7. SMMR-SSM/I Sensor Cross-Calibration Issues • The SMMR swath width was about half that of SSM/I, further reducing the probability of overlapping observations. • Sensor overlap occurred in July and August 1987 (Northern Hemisphere summer, essentially no seasonal snow cover). • The overlap is about 40 days, effectively 20 days of data, since SMMR was cycled on/off on a daily basis. • Low latitude sites may require up to 5 or 6 days for one SMMR overpass WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  8. Time series of Tibetan Plateau snow-covered area and average snow water equivalent (SWE) derived from passive microwave sensors, 1978-2004. Tibet Plateau snow extent snow water equivalent WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  9. Time series of 36/37 GHz, vertically-polarized “cold pass” brightness temperatures at Dome C (Antarctica) from SMMR (1978-1987, orange), various SSM/Is (1987-2006, pink/brown/green), and AMSR-E (2002-2006, blue). Note cross-sensor calibration issues, indicated by higher mean and compressed annual amplitudein SMMR data, compared to SSM/I and AMSR-E. Time Series of 36/37 GHz at Dome C Antarctica, SMMR-SSM/I-AMSR-E WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  10. Sensor Cross-Calibration: Locating Suitable Calibration Targets over Land Select land targets with surface characteristics that represent the cold through warm microwave emission range. The final targets are chosen for temporal and spatial stability. This is done by way of statistical analysis within a moving 3x3 array of pixels to determine the specific locations with minimal spatial and temporal variability. • A sub-array ‘footprint’ area was defined as a set of 3 x 3 grid cells (approximately 75 km x 75 km) for a given day, with indices j, centered at a particular grid point • The following statistics were considered: • Regions analyzed consisted of 100 x 100 arrays of 25-km EASE-Grid cells centered at 1.5°S, 21.6°E (Salonga, Zaire) and 75.0°S, 123.3°E (Dome C, Antarctica) • Over land, the spatial homogeneity and temporal stability of brightness temperatures (Tb) over selected regions such as tropical forest and ice sheet regions were examined Spatial mean and standard deviation of Tb within a footprint on day i: Temporal means (over one year) of miand i: Temporal standard deviation of mi over one year: WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  11. Tropical Forest - Salonga, Zaire Method to identify location of max. spatial and temporal stability -Tb statistics,central African tropical forest. (a) Upper left: (K). (b) Upper right: (K). (c) Lower left: (K). (d) Lower right: Surface topography (meters above sea level). Statistics for the year 2003 at Salonga, Zaire, for 10 GHz and 37 GHz vertical polarizations, descending passes: (a) Foot print mean, mi ; (b) Foot print standard deviation, i. Units are in Kelvins. Avg Daily Footprint Mean Stddev of Daily Footprint Mean (time) mi i Avg Daily Footprint Stddev (space) Avg Footprint Elevation WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  12. Scatter plots of SSM/I vs. SMMR brightness temperatures (19/18 GHz, left, and 37 GHz, right) at Earth targets selected for spatial stability (Dome C (Antarctic ice sheet), Salonga (African tropical forest), Canada (plains), Summit (Greenland ice sheet)).The large plus sign in each plot represents the typical range (+/- 1 standard deviation) of wintertime brightness temperatures in seasonally snow-covered regions. Scatter plots of SSM/I vs. SMMR brightness temperature at stable targets WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  13. Comparison of NASA Aqua AMSR-E (36 GHz) and DMSP SSM/I (37 GHz) Dome-C Antarctica, June 2002 to December 2005. SSM/I vs. AMSR-E WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  14. (50% or more of the weeks in the particular month over the period of record classified as snow covered). Visible vs. Microwave Sensors Monthly snow extent climatology for NOAA and passive microwave data WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  15. Blend of microwave and visible data - Mean monthly snow extent and SWE for 1978-2005 from a blend of passive microwave (SMMR & SSM/I) and visible (NOAA) data WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  16. NSIDC Global Monthly EASE-Grid Snow Water Equivalent Climatology WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  17. Blended snow prototypes, SWE (from SSM/I (left side) and AMSR-E (right side)) with snow cover from MODIS, Fall & Spring 2003. Top panels in each set of four represent SWE (mm), with additional area indicated as snow by MODIS in red. Lower panels represent passive microwave snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. SSM/I + MODIS AMSR-E + MODIS Fall 2003 P.M. (grey) + MODIS % Figure 3 Winter 2003 Figure 3b. Same as 4a, Spring, 2003. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  18. Improved Shallow Snow Detection through Application of 89 GHz Example of improvement in shallow snow SWE estimates by inclusion of 89 GHz data in combination with MODIS snow-covered area, October 24-31, 2003. Top row includes standard SWE algorithm results, with zoomed area in Northern Europe and Western Russia. Middle row includes additional snow-covered area (red), determined fromat least 25% of component MODIS CMG pixels indicating snow cover. Bottom row includes shallow SWE derived from additional information available from the 89 GHz channel. (select day w/ max diff 37 – 85 GHz) AMSR-E Only AMSR-E + MODIS AMSR-E + MODIS + 89 GHz (Nagler) WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  19. Trends • analysis includes: • autoregression, to avoid conclusions with false confidence in significance, and • number of years required to detect significant trends (Weatherhead, et al. 1998) • Changes in snow cover over the last several decades are being detected, although the message is mixed: no clear hemispheric trends, some regional trends, dependency on time period involved, consistent decreasing trends in spring/summer months. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  20. There is no evidence of a significant trend in either series from 1978-2005, > 17 more years required. Northern Hemisphere Visible-Derived Snow Extent Standardized Anomalies There is evidence of a significant decreasing trend in the full series (1966-2005) of visible-derived snow cover, -1.7 %/decade WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  21. For Western 11 U. S. States, there is evidence to suggest significant decreasing trends in both series considered from 1978-2005, - 9 %/decade. Western US Visible-Derived Snow Extent Standardized Anomalies but no evidence of a significant trend in the full series (1966-2005) of visible-derived snow cover. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  22. Continuing Work, Multi-Sensor • Refine cross-calibration of SMMR and SSM/I brightness temperatures. • Cross-calibrate SSM/I and AMSR-E and merge the historical snow cover times series with NASA EOS data and products. • Continue work on optimal blending MODIS and AMSR-E data including use of 89 GHz. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  23. Continuing Work, Trends • Run snow extent trend analysis for specific regions of interest, Arctic Basin, Tibet Plateau, sub-regions within USA etc. • Produce trend surface maps for snow cover duration and snow water equivalent to identify regions of greatest change. • Compare results with surface temperature trends. • Latest results to be presented at Fall AGU 2006 (U09) WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  24. Comparison of Monthly SWE Derived from SSM/I and the CCIN Gridded North American Data Set, August 1987 – June 1997 (Brown, Brasnett and Robinson, 2003) November WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  25. Comparison of Monthly SWE Derived from SSM/I and the CCIN Gridded North American Data Set (Brown, Brasnett and Robinson, 2003) January WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  26. Comparison of Monthly SWE Derived from SSM/I and the CCIN Gridded North American Data Set (Brown, Brasnett and Robinson, 2003) March WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  27. Bare Dry Soil WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  28. Medium Depth Snow (10-25 cm) WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  29. Wet Snow WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  30. Antarctica - Dome C AMSR-E 10 GHz vertical polarization Tb statistics for East Antarctica, descending passes, in 2003. (a) Upper left: (K). (b) Upper right: (K). (c) Lower left: (K). (d) Lower right: Surface topography (meters above sea level). Statistics for the year 2003 at Dome ‘C’, Antarctica, for 10 GHz and 37 GHz vertical polarizations: (a) Footprint mean, mi; (b) Footprint standard deviation, i. Units are in Kelvins. Avg Daily Footprint Mean Stddev of Daily Footprint Mean (time) mi i Avg Daily Footprint Stddev(space) Avg Footprint Elevation WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  31. Statistics Definitions for Stable Targets Study • Footprint mean = the mean of the brightness temperatures of each grid cell in a footprint. • Footprint standard deviation =spatial variance at an instant in time or how the grid cells change in brightness temperature over the footprint. Needs to be small to have a homogeneous target. • Average daily footprint mean = time average of the footprint mean, e.g. over one year • Standard deviation of the daily footprint mean = standard deviation of the footprint mean over time. Sites with a low SDDFM when the time period is long are sites that generally have low seasonal brightness temperature variation. • Average daily footprint standard deviation = the average of each footprint standard deviation over some time series. Sites that are spatially homogeneous or have low brightness temperature gradients across a footprint will have low values of ADFSD. An area with a large ADFSD would not be a suitable calibration target, such as locations near land/water boundaries. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  32. Blended snow product prototypes, SWE (mm) from AMSR-E with additional snow extent from MODIS in red (October 24-31, 2003, max). Lower image represents AMSR-E snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR-E @ 25 km, MODIS CMG @ 5 km) Fall Season Example WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  33. Blended snow product prototypes, SWE (mm) from AMSR-E with additional snow extent from MODIS in red (Feb 26 – Mar 5, 2003, max). Lower image represents AMSR-E snow extent in grey, with percent area of additional pixels that MODIS classifies as snow in blues. (AMSR-E @ 25 km, MODIS CMG @ 5 km) Winter Season Example WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  34. October 24-31, 2003 Fall Season Example AMSR-E Only AMSR-E + MODIS Mask area where MODIS detects snow and select day w/ max diff 37 – 89 GHz to minimize atmospheric influence AMSR-E +MODIS+ Nagler WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  35. Passive Microwave Remote Sensing of Snow • Radiation emitted from the soil is scattered by the snow cover • Scattering increases in proportion to amount (mass) of snow • Brightness temperature decrease, negative spectral gradient, • polarization difference • Tb = ε * Tp (brightness temperature equals emissivity x physical temperature) WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  36. Northern Hemisphere monthly average snow water equivalent derived from SSM/I (left) and AMSR-E (right), November 2003 – February 2004. SSM/I AMSR-E WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  37. Snow Cover Mapping on the Tibetan Plateau Using Optical and Passive Microwave Satellite Data R. L. Armstrong, M.J. Brodzik, M. Savoie, K. Knowles University of Colorado and World Data Center for Glaciology, Boulder -- USA Asian Conference on Permafrost, Lanzhou, China, August 7-9, 2006 Acknowledgements – Research Support: NASA WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  38. 01 Nov 2001 06 Dec 2001 Visible Microwave WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  39. Tibetan Plateau, Winter vs. Summer November July \ Climatology differences, November and July, showing the anomalous snow cover to be a cold-season phenomenon and not due to land cover type. WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  40. Tibetan Plateau, Air Temperature and Frozen Soil Clockwise from upper left: SWE derived from SSM/I, ECMWF average daily surface temperatures, MODIS snow cover with frozen soil derived from SSM/I, and NOAA IMS product. (November 30, 2001) WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  41. Possible Solution: Atmospheric Correction Atmosphere should be considered when validating satellite retrieval algorithms based on surface and low-alt. aircraft Measurements (Wang& Manning, 2003) Conversely, atmosphere needs to be “removed” when applying algorithms empirically adjusted to perform through a full atmosphere at very high elevations. Atmospheric correction to microwave spectral gradient, modelled at altitudes of 1 km and 4 km. James Wang, GSFC WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  42. AMSR-E SWE, Before & After Atmospheric Correction Result of atmospheric correction, November 29, 2003. SWE derived from uncorrected AMSR-E (left) and corrected AMSR-E (right). WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  43. Tibet Plateau Region Snow CoverJan 9-16, 2003 SSM/I AMSR-E MODIS NOAA IMS WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  44. Snow Cover Derived from SSM/I January 9-16 2003 WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

  45. Snow Cover Derived from AMSR-EJanuary 9-16 2003 WDC for Glaciology, Boulder – 30th Anniversary Workshop Boulder, Colorado 25 October 2006

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