1. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada National Aeronautics and Space Administration http://smap.jpl.nasa.gov Utilization of Ancillary Data Sets for SMAP Algorithm Development and Product Generation IGARSS’11, Vancouver, BC July 27, 2011 Peggy E. O’Neill, NASA GSFC Erika Podest, JPL Eni G. Njoku, JPL

2. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada 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. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada ProductShort Description Resolution/ Grid Latency L1A_S0Radar raw data in time order–12 hours Instrument Data L1A_TBRadiometer raw data in time order–12 hours L1B_S0_LoResLow resolution radar σ o in time order5x30 km12 hours L1B_TBRadiometer T B in time order36x47 km12 hours L1C_S0_HiResHigh resolution radar σ o 1-3 km12 hours L1C_TBRadiometer T B 36 km12 hours L2_SM_ASoil moisture (radar) [research product] 3 km24 hours Science Data (Half-Orbit) L2_SM_PSoil moisture (radiometer)36 km24 hours L2_SM_A/PSoil moisture (radar/radiometer)9 km24 hours L3_SM_ASoil moisture (radar) [research product] 3 km50 hours Science Data (Daily Composite) L3_F/T_AFreeze/thaw state (radar)3 km50 hours L3_SM_PSoil moisture (radiometer)36 km50 hours L3_SM_A/PSoil moisture (radar/radiometer)9 km50 hours L4_SMSoil moisture (surface & root zone)9 km7 days Science Value-Added L4_CCarbon net ecosystem exchange (NEE)9 km14 days SMAP Data Products

4. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Algorithm Needs All baseline SMAP products have associated algorithm(s) which require a variety of ancillary data to meet retrieval accuracies: -- 0.04 cm 3 /cm 3 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/m 2 ) 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. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Ancillary Parameters 1 Soil Temperature 2 Surface Air Temperature 3 Vegetation Water Content (VWC) 4 Sand & Clay Fraction 5 Urban Area 6 % Permanent Open Water 7 Crop Type 8 Land Cover Class 9 Precipitation 10 Snow 11 Mountainous Area [DEM] 12 Permanent Ice 13 b, ω, & τ Vegetation Parameters 14 h Roughness Parameter 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. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Soil Temperature Accuracy of synchronized NWP forecast surface soil temperature compared against in situ temperatures for the Oklahoma Mesonet for 2004 and 2009. 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 T S 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 T S data from the Oklahoma Mesonet indicates that at the 6 am overpass time, all NWP T S products have errors just below 2 K initial global estimates of NWP T S error against in situ point measurements are less optimistic, more in the range of 2.5 – 3.0 K; analysis on global T S error is continuing

7. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Vegetation Water Content Annual climatology of NDVI for Walnut Creek, IA snow 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/m 2 ) over the continental U.S. for the month of July on a 1-km EASE grid as constructed from a 10-year MODIS NDVI climatology and land cover products.

8. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Soil Texture 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) 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.

9. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Urban Areas 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 T B cannot be corrected for presence of urban areas Global Rural-Urban Mapping Project

10. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Open Water Fraction 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 T B for a mix of land & water in the grid cell Partial UAVSAR ratio image of Mono Lake. ~7% detection error 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

11. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada 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 Input Data Set:US SRTMSRTMGTOPOAlaska DEMCanada DEM Coverage: United States 56 °S to 60 °NGlobalAlaskaCanada Source: NASA-JPL USGS GeoBase Resolution: 1 arc-second 3 arc-seconds30 arc-seconds2 arc-seconds3x6 arc-seconds Horz. Datum: WGS84 NAD27NAD83 Vertical Datum: EGM96 NAVD29CVGD28 Projection: Geographic Acquisition Date: February 2000 Late 19961925 - 1999 --

12. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Error Analysis L2_SM_P Error Analysis Errors in ancillary data are factored into the SMAP soil moisture retrieval algorithm error budget Simulated error performance of candidate retrieval algorithms for the radiometer-derived soil moisture product using one year of simulated SMAP H- and V-pol T B with indicated errors in model and ancillary parameters.

13. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada Ancillary Parameter Choices 1 Soil Temperature GMAO or ECMWF forecast temperatures (TBD) 2 Surface Air Temperature GMAO or ECMWF forecast temperatures (TBD) 3 Vegetation Water Content (VWC) MODIS NDVI [R. Hunt approach] 4 Sand & Clay fraction HWSD (global), regional data sets (STATSGO-US, ASRIS-Australia, NSD- Canada), FAO 5 Urban AreaGRUMP data set – Columbia University 6 Open Water Fraction a priori static water fraction from MODIS MOD44W to be used in conjunction with open water fraction from SMAP HiRes radar 7 Crop Type combination of USDA Cropland Data Layer, AAFC-Canada, Ecoclimap-Europe 8 Land Cover Class MODIS IGBP; crop class will be further subdivided into four general crop types 9 Precipitation ECMWF total precipitation forecasts (or GPM once launched) 10 SnowSnow & Ice Mapping System (IMS) - NOAA 11 Mountainous Area [DEM] combination of SRTM, Alaska USGS DEM, Canada Geobase DEM, and GTOPO30 12 Permanent IceMODIS IGBP 13 b, ω, and τ Vegetation Parametersland cover-driven table lookup 14 h Roughness Parameter land cover-driven table lookup Anticipated Primary Sources of Ancillary Parameters

14. 7/27/11 Peggy O’Neill, NASA GSFC IGARSS’11, Vancouver, BC, Canada 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