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Value of Ground Network Observations in Development of Satellite Soil Moisture Data Products

Value of Ground Network Observations in Development of Satellite Soil Moisture Data Products. X. Zhan 1 , J. Liu 1 , M. Cosh 2 , T. Jackson 2 , and Y. Yu 1 1 NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD

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Value of Ground Network Observations in Development of Satellite Soil Moisture Data Products

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  1. Value of Ground Network Observations in Development of Satellite Soil Moisture Data Products X. Zhan1, J. Liu1, M. Cosh2, T. Jackson2,andY. Yu1 1 NOAA-NESDIS Center for Satellite Applications and Research, Camp Springs, MD 2USDA-ARS Hydrology and Remote Sensing Lab, Beltsville, MD

  2. OUTLINE • Satellite SM data • Ground SM and ST observations • Results and issues in comparing them • Suggestions for USCRN

  3. Satellite Soil Moisture Data Products: • VUT ESCAT/ASCAT (Wagner et al, 1999) • USDA TMI (Bindlish et al, 2003) • Princeton TMI (Gao et al, 2006) • NASA AMSR-E (Njoku et al, 2003) • USDA AMSR-E (Jackson et al, 2007) • VUA AMSR-E (Owe et al, 2008) • USDA WindSat (Jackson et al, 2008) • NRL WindSat (Li et al, 2008)

  4. USDA TMI (Bindlish et al, 2003): Daily estimates, from July 06 to July 21, 1999 0.0 – 0.52%v/v

  5. Princeton University TMI (Gao et al, 2006): Jan. 1, 1999 with quality masks applied

  6. NASA AMSR-E (Njoku et al, 2003): Within US: 0.1 – 0.2 v/v

  7. VUA-GSFC AMSR-E (Owe et al, 2008): Monthly for July 2003: Top: 6.9GHz Bottom: 10.7 GHz

  8. USDA WindSat (Jackson et al, 2008): WindSat global volumetric soil moisture (%) for July 30, 2005. 0.0 – 0.5 v/v

  9. NRL WindSat (Li et al, 2008): WindSat global volumetric soil moisture (%) and vegetation water content (kg/m2) retrievals for 1 – 12 September 2003.

  10. MetOp ASCAT (Wagner et al, 1999): VUT ASCAT soil moisture is actually soil wetness, could be converted to volumetric soil moisture by dividing them with their soil porosity

  11. Ground SM & ST Observations: • Watershed SM Vitel Network (2001- present) • Soil Climate Analysis Network (1996- present) • Surface Radiation Budget Network (1993- present) • US Climate Reference Network (2002- present)

  12. AMSR-E U.S. Soil Moisture Validation Sites USDA-ARS Watershed SM Vitel Network: LW: Little Washita, OK RC: Reynolds Creek, ID LR: Little River, GA WG: Walnut Gulch, AZ

  13. USDA-ARS Watershed SM Vitel Network: LW LR • Multiple sites within a satellite footprint • Rain gauge overlay SM sites • Multiple layers (5cm, 15cm, 30cm) • Continuous data sampling (30 min.) • Stevens-Vitel Hydra Probes WG RC

  14. USDA-NRCS Soil Climate Analysis Network (SCAN): • Mostly single site in a satellite footprint • Rain gauge at the same site • Multiple layers (2”, 4”, 8”, 20”, 40”) • Hourly data • Hydra Probes

  15. NOAA Surface Radiation Budget Network (SurfRad): • 6 sites from 1995 and 1 site from 2003 • Mainly solar and thermal radiation • LST observational Data • Sample per 1 or 3 minutes • Precision Infrared Radiometers

  16. NOAA Climate Reference Network (USCRN): • >100 stations with a few paired ones • Most climate variables including precipitation • SM/ST planned • SM/ST Sample freq ? • SM/ST sensors ?

  17. Validating AMSR-E Soil Moisture Retrievals: with Watershed SM Vitel Network (USDA-ARS)

  18. Validating AMSR-E Soil Moisture Retrievals: with SCAN Data (USDA-NRCS)

  19. Validating Multiple Soil Moisture Retrievals: with SCAN Data (USDA-NRCS)

  20. Evaluating LST Estimates (Tskin) for SM Retrievals: Multi-channel Inversion (MCI) Algorithm (Njoku & Li, 1999): TB,icmp = Tskin {er,p exp (-i/cos) + (1 – ) [1 – exp (-i/cos)] [1 + Rr,i exp (-i/cos)]} i = b *VWC Rr,i=Rs exp(h cos2θ) Rs = f(ε) -- Fresnel Equation ε= g(SM) -- Mixing model TB,iobs= TB06h, TB06v , TB10h , TB10v , TB18h , TB18v

  21. Evaluating LST Estimates (Tskin) for SM Retrievals: Single Channel Retrieval (SCR) Algorithm (Jackson, 1993): TB10h = Ts [1 –Rr exp (-2 /cos)] Rr=Rs exp(h cos2θ) Rs = f(ε) -- Fresnel Equation ε= g(SM) -- Mixing model Ts= reg1(TB37v) or TsLSM = b * VWC VWC = reg2(NDVI)

  22. Evaluating NCEP-GDAS Tskin Estimates: with SurfRad (NOAA)

  23. Evaluating NCEP-GDAS Tskin Estimates: with SurfRad (NOAA)

  24. Evaluating NCEP-GDAS Tskin Estimates: with SurfRad (NOAA)

  25. Evaluating Tskin Estimates from AMSR-E TB36v:

  26. Evaluating Tskin Estimates from AMSR-E TB36v:

  27. Evaluating Tskin Estimates from AMSR-E TB36v:

  28. Issues in Satellite SM Validation with in situ Data: Footprint representation/ heterogeneity issue

  29. Issues in Satellite SM Validation with in situ Data: • SCAN probe depth: 2”, 4”, 8”, 20”, 40” • USDA Vitel Network: 5cm, 15cm, 30cm • Noah LSM depth: 10cm, 40cm, 100cm, 200cm • AMSR-E (C-band) sensible depth: < 2cm • SMAP (L-band): < 5cm • USCRN: ? Hydra probe depth vs satellite sensor depth

  30. Issues in Satellite SM Validation with in situ Data: Hydra Probe calibration standardization

  31. SUGGESTIONS FOR USCRN • Representation issue: Identify those sites with good area landscape heterogeneity for SM/ST sensor installation if not all sites; • Probe depth issue: Consider future operational or long term C/X-band satellite sensors (MIS, AMSR2, GPM, etc) as well as L-band sensors (SMOS, SMAP); • Data quality issue: Plan frequent sensor calibration based on timely data analysis; • LST (Tskin): LST observations are desirable for both satellite (SM/ST) data products validation and climate monitoring; • Data access: Open, timely, and convenient access to USCRN data benefits all of their potential applications (drought monitoring, satellite data validation, etc)

  32. SUMMARY • Currently available satellite SM data products are significantly differing from each other and their qualities need to be improved for operational uses; • SM ground measurements are useful for satellite SM/ST data product validation and verification of land surface model SM/ST data assimilation; • There are spaces for improving the ground SM/ST measurement quality with consideration to satellite footprint representation, sensor depth, calibration standardization and open, timely, convenient data access; • In addition to soil temperature measurements, Land Surface Temperature (Tskin) observations are also desirable for satellite (SM/ST) data product validations;

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