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National Aeronautics and Space Administration

Utilization of Ancillary Data Sets for SMAP Algorithm Development and Product Generation. Peggy E. O’Neill, NASA GSFC Erika Podest, JPL Eni G. Njoku, JPL . IGARSS’11, Vancouver, BC July 27, 2011. http ://smap.jpl.nasa.gov. National Aeronautics and Space Administration. BACKGROUND.

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National Aeronautics and Space Administration

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  1. Utilization of Ancillary Data Sets for SMAP AlgorithmDevelopment and Product Generation Peggy E. O’Neill, NASA GSFC Erika Podest, JPL Eni G. Njoku, JPL IGARSS’11, Vancouver, BC July 27, 2011 http://smap.jpl.nasa.gov National Aeronautics and Space Administration

  2. BACKGROUND • SMAP is a planned NASA Earth Science Decadal Survey Mission • Launch currently scheduled for October 2014 into a 6 am / 6 pm • sun-synchronous orbit • Will use an L-band radar & radiometer to measure global soil • moisture & freeze/thaw every 2-3 days • Baseline SMAP data products include: • -- radar-derived F/T at 3 km resolution • -- radiometer-only SM at 40 km resolution • -- combined radar/radiometer SM at 9 km resolution • -- value-added products (root zone SM, carbon NEE) at 9 km • All SMAP products output on nested 1, 3, 9, 36 km EASE grids

  3. SMAP Data Products

  4. Algorithm Needs • All baseline SMAP products have associated algorithm(s) which • require a variety of ancillary data to meet retrieval accuracies: • -- 0.04 cm3/cm3 for soil moisture within SMAP land mask • -- 80% classification accuracy for binary F/T in boreal latitudes • Areas of snow/ice, frozen ground, mountainous topography, open • water, urban areas, and dense vegetation (> 5 kg/m2) are excluded • from SM accuracy statistics • Static ancillary data do not change during mission • -- permanent masks (land/water/forest/urban/mountain), DEM, soils • Dynamic ancillary data require periodic updates ranging from daily • to seasonally • -- soil T, precipitation, vegetation, surface roughness, land cover

  5. Ancillary Parameters • Table 1. Ancillary Parameters • 14 ancillary data parameters identified as • needed by one or more SMAP algorithms • choice of source of each parameter driven by: • -- availability • -- ease of use • -- inherent error • -- latency • -- temporal & spatial resolution • -- global coverage • -- positive impact on SMAP retrieval accuracies • -- compatibility with SMOS choices • choices documented in a SMAP Ancillary Data Report for each parameter • data from each primary source will be used now in pre-launch simulations • choices will be revisited as new information becomes available

  6. Soil Temperature SMAP 6 am descending orbit • SMAP soil moisture products will be • retrieved using data from the 6 am • descending orbits • the 6 am 0-5 cm TS is the most dynamic • ancillary parameter needed -- it is updated • every orbit for each location • SMAP error budgets currently carry 2 K • as the error in ancillary TS • data from the Oklahoma Mesonet indicates • that at the 6 am overpass time, all NWP • TS products have errors just below 2 K • initial global estimates of NWP TS error • against in situ point measurements are less • optimistic, more in the range of 2.5 – 3.0 K; • analysis on global TS error is continuing Accuracy of synchronized NWP forecast surface soil temperature compared against in situ temperatures for the Oklahoma Mesonet for 2004 and 2009.

  7. Vegetation Water Content snow Annual climatology of NDVI for Walnut Creek, IA • a new 10-year (2000-2010) MODIS NDVI climatology has been created at 1 km resolution • globally • VWC calculated using NDVI-based water contributions from both foliage and stem components, adjusted for IGBP land cover classes VWC (kg/m2) over the continental U.S. for the month of July on a 1-km EASE grid asconstructed from a 10-year MODIS NDVI climatology and land cover products.

  8. Soil Texture 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. • soil sand & clay fraction needed by dielectric models used in SM retrieval • best available source used for any given region • resulting global map a combination of different data sets • potential for discontinuities at data set boundaries (e.g., US / Canada)

  9. Urban Areas Global Rural-Urban Mapping Project • GRUMP urban data (Columbia U.) gridded to SMAP 9 km EASE grid • better delineation between urban & rural areas • urban fraction > 0.5 shown • however, urban flag likely to be set much lower since TB cannot be • corrected for presence of urban areas

  10. Open Water Fraction Open water (both permanent & transient) in a SMAP footprint is a potential large error source for SMAP retrieval algorithms if its presence is not detected & corrected for Partial UAVSAR ratio image of Mono Lake. ~7% detection error • use SMAP HiRes radar to determine open water fraction • a 3 dB threshold is applied to HH to VV ratio to distinguish water from land • this SMAP parameter can be supplemented by static permanent water body • data sets like MODIS MOD44W and JERS-1/PALSAR (for boreal latitudes) • the water fraction is then used to correct TB for a mix of land & water in the • grid cell

  11. Input Data Set: US SRTM SRTM GTOPO Alaska DEM Canada DEM Coverage: United States 56 °S to 60 °N Global Alaska Canada Source: NASA-JPL NASA-JPL USGS USGS GeoBase Resolution: 1 arc-second 3 arc-seconds 30 arc-seconds 2 arc-seconds 3x6 arc-seconds Horz. Datum: WGS84 WGS84 WGS84 NAD27 NAD83 Vertical Datum: EGM96 EGM96 EGM96 NAVD29 CVGD28 Projection: Geographic Geographic Geographic Geographic Geographic Acquisition Date: February 2000 February 2000 Late 1996 1925 - 1999 -- Topography / DEM JPL Global DEM -- compiled from different sources -- 1 arc-second resolution -- GMTED2010 will eventually replace GTOPO30 -- above will be useful in assessing any discontinuities between existing data sets -- elevation and slope variance (TBC) could be used to set topography flag

  12. Error Analysis • Errors in ancillary data are factored into the SMAP soil moisture retrieval • algorithm error budget L2_SM_P Error Analysis Simulated error performance of candidate retrieval algorithms for the radiometer-derived soil moisture product using one year of simulated SMAP H- and V-pol TB with indicated errors in model and ancillary parameters.

  13. Ancillary Parameter Choices Anticipated Primary Sources of Ancillary Parameters

  14. Summary • All ancillary data will be resampled to the SMAP EASE grids at 1, 3, 9, 36 km • Preliminary choices have been made for primary source of each • ancillary parameter • -- these choices will be used pre-launch for SMAP simulations and • algorithm development • -- choices will be re-examined as new information becomes available • -- will leverage SMOS data and experience • -- SMOS / SMAP consistency desirable • Choices documented in SMAP Ancillary Data Reports • Wise choices in ancillary data will help SMAP to provide accurate • global measurements of SM & F/T

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