1_IGARSS11_2069_ONeill.pptx

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1_IGARSS11_2069_ONeill.pptx

  1. 1. Utilization of Ancillary Data Sets for SMAP AlgorithmDevelopment and Product Generation<br />Peggy E. O’Neill, NASA GSFC<br />Erika Podest, JPL<br />Eni G. Njoku, JPL <br />IGARSS’11, Vancouver, BC<br />July 27, 2011<br />http://smap.jpl.nasa.gov<br />National Aeronautics and Space Administration<br />
  2. 2. BACKGROUND<br /><ul><li>SMAP is a planned NASA Earth Science Decadal Survey Mission
  3. 3. Launch currently scheduled for October 2014 into a 6 am / 6 pm</li></ul> sun-synchronous orbit<br /><ul><li> Will use an L-band radar & radiometer to measure global soil</li></ul> moisture & freeze/thaw every 2-3 days<br /><ul><li> Baseline SMAP data products include:</li></ul>-- radar-derived F/T at 3 km resolution<br />-- radiometer-only SM at 40 km resolution<br />-- combined radar/radiometer SM at 9 km resolution<br />-- value-added products (root zone SM, carbon NEE) at 9 km<br /><ul><li> All SMAP products output on nested 1, 3, 9, 36 km EASE grids</li></li></ul><li>SMAP Data Products<br />
  4. 4. Algorithm Needs<br /><ul><li> All baseline SMAP products have associated algorithm(s) which</li></ul> require a variety of ancillary data to meet retrieval accuracies:<br />-- 0.04 cm3/cm3 for soil moisture within SMAP land mask<br /> -- 80% classification accuracy for binary F/T in boreal latitudes<br /><ul><li> Areas of snow/ice, frozen ground, mountainous topography, open </li></ul> water, urban areas, and dense vegetation (> 5 kg/m2) are excluded<br /> from SM accuracy statistics<br /><ul><li> Static ancillary data do not change during mission </li></ul> -- permanent masks (land/water/forest/urban/mountain), DEM, soils<br /><ul><li>Dynamic ancillary data require periodic updates ranging from daily</li></ul> to seasonally <br />-- soil T, precipitation, vegetation, surface roughness, land cover<br />
  5. 5. Ancillary Parameters<br />Table 1. Ancillary Parameters<br /><ul><li> 14 ancillary data parameters identified as </li></ul> needed by one or more SMAP algorithms<br /><ul><li> choice of source of each parameter driven by:</li></ul>-- availability<br />-- ease of use<br />-- inherent error<br />-- latency<br />-- temporal & spatial resolution<br />-- global coverage<br />-- positive impact on SMAP retrieval accuracies<br />-- compatibility with SMOS choices<br /><ul><li> choices documented in a SMAP Ancillary Data Report for each parameter
  6. 6. data from each primary source will be used now in pre-launch simulations
  7. 7. choices will be revisited as new information becomes available</li></li></ul><li>Soil Temperature<br />SMAP 6 am descending orbit <br /><ul><li> SMAP soil moisture products will be</li></ul> retrieved using data from the 6 am <br /> descending orbits<br /><ul><li> the 6 am 0-5 cm TS is the most dynamic</li></ul> ancillary parameter needed -- it is updated<br /> every orbit for each location<br /><ul><li> SMAP error budgets currently carry 2 K </li></ul> as the error in ancillary TS<br /><ul><li> data from the Oklahoma Mesonet indicates</li></ul> that at the 6 am overpass time, all NWP <br /> TS products have errors just below 2 K<br /><ul><li> initial global estimates of NWP TS error</li></ul> against in situ point measurements are less <br /> optimistic, more in the range of 2.5 – 3.0 K;<br /> analysis on global TS error is continuing<br />Accuracy of synchronized NWP forecast <br />surface soil temperature compared <br />against in situ temperatures for the <br />Oklahoma Mesonet for 2004 and 2009.<br />
  8. 8. Vegetation Water Content<br />snow<br />Annual climatology of NDVI for <br />Walnut Creek, IA<br /><ul><li>a new 10-year (2000-2010) MODIS NDVI climatology has been created at 1 km resolution</li></ul>globally<br /><ul><li> VWC calculated using NDVI-based water contributions from both foliage and stem components, adjusted for IGBP land cover classes</li></ul>VWC (kg/m2) over the continental U.S. for the month of July on a 1-km EASE grid<br /> asconstructed from a 10-year MODIS NDVI climatology and land cover products.<br />
  9. 9. Soil Texture<br />Global sand fraction at 0.01 degree resolution based on a composite of FAO, HWSD, STATSGO, NSDC, and ASRIS datasets using best available source for a given region.<br /><ul><li> soil sand & clay fraction needed by dielectric models used in SM retrieval
  10. 10. best available source used for any given region
  11. 11. resulting global map a combination of different data sets
  12. 12. potential for discontinuities at data set boundaries (e.g., US / Canada)</li></li></ul><li>Urban Areas<br />Global Rural-Urban Mapping Project<br /><ul><li> GRUMP urban data (Columbia U.) gridded to SMAP 9 km EASE grid
  13. 13. better delineation between urban & rural areas
  14. 14. urban fraction > 0.5 shown
  15. 15. however, urban flag likely to be set much lower since TB cannot be</li></ul> corrected for presence of urban areas<br />
  16. 16. Open Water Fraction<br />Open water (both <br />permanent & transient)<br />in a SMAP footprint <br />is a potential large <br />error source for SMAP <br />retrieval algorithms if <br />its presence is not <br />detected & corrected for <br />Partial UAVSAR ratio image of <br />Mono Lake. ~7% detection error<br /><ul><li> use SMAP HiRes radar to determine open water fraction
  17. 17. a 3 dB threshold is applied to HH to VV ratio to distinguish water from land
  18. 18. this SMAP parameter can be supplemented by static permanent water body </li></ul> data sets like MODIS MOD44W and JERS-1/PALSAR (for boreal latitudes)<br /><ul><li> the water fraction is then used to correct TB for a mix of land & water in the</li></ul> grid cell <br />
  19. 19. Input Data Set:<br />US SRTM<br />SRTM<br />GTOPO<br />Alaska DEM<br />Canada DEM<br />Coverage:<br />United States<br />56 °S to 60 °N<br />Global<br />Alaska<br />Canada<br />Source:<br />NASA-JPL<br />NASA-JPL<br />USGS<br />USGS<br />GeoBase<br />Resolution:<br />1 arc-second<br />3 arc-seconds<br />30 arc-seconds<br />2 arc-seconds<br />3x6 arc-seconds<br />Horz. Datum:<br />WGS84<br />WGS84<br />WGS84<br />NAD27<br />NAD83<br />Vertical Datum:<br />EGM96<br />EGM96<br />EGM96<br />NAVD29<br />CVGD28<br />Projection:<br />Geographic<br />Geographic<br />Geographic<br />Geographic<br />Geographic<br />Acquisition Date:<br />February 2000<br />February 2000<br />Late 1996<br />1925 - 1999<br />--<br />Topography / DEM<br />JPL Global DEM<br />-- compiled from different sources<br />-- 1 arc-second<br />resolution <br />-- GMTED2010 will eventually replace GTOPO30<br />-- above will be useful in assessing any discontinuities between existing data sets<br />-- elevation and slope variance (TBC) could be used to set topography flag<br />
  20. 20. Error Analysis<br /><ul><li> Errors in ancillary data are factored into the SMAP soil moisture retrieval</li></ul> algorithm error budget<br />L2_SM_P Error Analysis<br />Simulated error performance of candidate retrieval algorithms for the radiometer-derived <br />soil moisture product using one year of simulated SMAP H- and V-pol TB with indicated <br />errors in model and ancillary parameters.<br />
  21. 21. Ancillary Parameter Choices<br /> Anticipated Primary Sources of Ancillary Parameters<br />
  22. 22. Summary<br /><ul><li>All ancillary data will be resampled to the SMAP EASE grids at 1, 3, 9, 36 km
  23. 23. Preliminary choices have been made for primary source of each</li></ul> ancillary parameter<br />-- these choices will be used pre-launch for SMAP simulations and<br /> algorithm development<br />-- choices will be re-examined as new information becomes available<br />-- will leverage SMOS data and experience<br /> -- SMOS / SMAP consistency desirable<br /><ul><li>Choices documented in SMAP Ancillary Data Reports
  24. 24. Wise choices in ancillary data will help SMAP to provide accurate </li></ul> global measurements of SM & F/T<br />

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