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  1. Aquarius/SAC-D Soil Moisture Retrieval and ApplicationsT. J. Jackson1 and H. Karszenbaum21USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD USA2Instituto de Astronomia y Fisica del espacio, Buenos Aires, ArgentinaWith contributions fromM. Cosh, R. Bindlish, P. O’Neill6th Aquarius Science Team MeetingJuly 19-21, 2010

  2. Outline • Passive, active, and combined microwave soil moisture algorithms • Basis of passive microwave soil moisture algorithms • Strategy for developing an Aquarius baseline soil moisture algorithm • Validation • Recent field campaigns (CanEx) • Updates of related Aquarius/SAC-D projects

  3. Passive and Active Soil Moisture Algorithms • Passive microwave • Several passive algorithms have been developed, applied and validated using AMSR-E and WindSat • Translating these to L-band is possible and should result in improved estimates • Recent implementation of SMOS algorithms • Still early but looks good (See Yann’s talk!) • All algorithms are based on the same radiative transfer equation (t-w model). They vary in their approaches to parameter estimation and related assumptions. • Single channel, Iterative optimization, Polarization indices… • The optimal algorithm for a satellite mission will vary with the sensor system configuration (i.e. Aquarius is quite different from SMOS or AMSR-E)

  4. Passive and Active Soil Moisture Algorithms • Active microwave • No robust retrieval technique has been developed and validated for use with high resolution SAR data • Two decades of C-band (ERS, Radarsat, ...) and one decade of L-band (JERS, PALSAR) • Statistical and semi-empirical methods for specific sites/conditions, i.e. the Dubois model. • SMAP and SAOCOM are focusing on this problem. • Operational products have been developed using coarse resolution (50 km) C-band scatterometers • Temporal change technique. • Extended period of observations required to develop frequency specific calibration of each footprint! • Considered an index as opposed to actual soil moisture.

  5. Passive and Active Soil Moisture Algorithms • Passive and active microwave synergy • There have been a few very limited studies. • SMAP has this capability but the primary focus at the moment is on resolution enhancement. • Utilizing the radar data directly in the retrieval algorithm is under consideration. • Mixing and matching passive and active data from existing satellites as a means of simulating Aquarius may not be useful. • AMSR-E/SMOS and ASCAT/QUIKSCAT: vegetation and roughness effects are very important and vary with frequencies and polarizations. Need concurrent observations. • SMOS and ALOS: issues with disparate scales need to be resolved. Need concurrent observations. (or worth the effort at this point).

  6. Aquarius Soil Moisture Algorithms • Our approach will be to build from proven techniques and add in the capabilities of Aquarius/SAC-D • Start with a description of the radiative transfer model used in passive microwave methods for soil-vegetation conditions.

  7. From Brightness Temperature to Soil Moisture Brightness temperature (TB) is a function of emissivity (e) and physical temperature (T). Observations are made at a specific polarization (p), frequency (f), and angle (Q) The second term is small resulting in a simple relationship for e The observed e is the result of contributions from the soil surface (esurf) modified by the scattering (w) and attenuation (g) of the vegetation (v) Unknowns esurf is the soil emission (esoil) modified by the surface roughness (h) The contributing depth of the soil is on the order of 0.25*wavelength. For 1.4 GHz or L-band this is 5 cm The esoil is a function of the soil dielectric properties (er) A dielectric mixing model relates er to soil moisture based on sand and clay fractions

  8. Soil Moisture Retrieval Algorithms • Problem: There are more unknowns than measurements: under determined system of equations • Solutions attempt to reduce the dimensionality of the problem. Some approaches are: • Multiple angle observations. • Assuming polarization independence (or defining the dependence) of parameters. • Assuming frequency independence of parameters. • Estimating parameters using ancillary information. • Localized calibration. • Initial selection for this study: • Single Channel Algorithm (SCA) • Alternatives: three algorithms being evaluated by SMAP X X Not compatible with Aquarius/SAC-D

  9. Potential of Aquarius/SAC-D Measurements in Soil Moisture Retrieval Aquarius Radiometer Brightness temperature (TB) is a function of emissivity (e) and physical temperature (T). Observations are made at a specific polarization (p), frequency (f), and angle (Q) The second term is small resulting in a simple relationship for e SAC-D 36.5 GHz or NIRST The observed e is the result of contributions from the soil surface (esurf) modified by the scattering (w) and attenuation (g) of the vegetation (v) Aquarius Radar Aquarius Radar esurf is the soil emission (esoil) modified by the surface roughness (h) The contributing depth of the soil is on the order of 0.25*wavelength. For 1.4 GHz or L-band this is 5 cm The esoil is a function of the soil dielectric properties (er) A dielectric mixing model relates er to soil moisture based on sand and clay fractions

  10. Land Surface Temperature (LST) • Required for all algorithms (TB to emissivity) • MWR 36.5 GHz V algorithm • Heritage from SSM/I, TMI, AMSR-E, and WindSat • Potential mission product • Data integration issues? • Numerical Weather Forecast Model products • SMOS and SMAP approach • Several options • NIRST LST product (research) • Currently conducting a comparison of three NWF temperature products (MERRA, ECMWF, and NCEP) and impacts on soil moisture retrieval.

  11. Aquarius/SAC-D Soil Moisture Retrieval • Approaches using various inputs and algorithms will be systematically developed and evaluated. Inputs Algorithm Product L Band passive H pol. SCA Ver. X.1 . . . . . L Band passive V pol. Alternatives L Band radar Forecast model LST MWR 36.5 V Ver. X.2 NIRST TIR NDVI-Climatological NDVI-MODIS

  12. Aquarius/SAC-D Soil Moisture Retrieval Ver. 1 • Ver. 1 represents the adaptation of heritage passive microwave X and C-band algorithms to L-band. • Model LST will be used until MWR 36.5 V data are validated and available in the integrated data set. Inputs Algorithm Product L Band passive H pol. SCA Ver. 1.1 (BASELINE) L Band passive V pol. Alternatives Ver. 1.2 L Band radar Forecast model LST MWR 36.5 V NIRST TIR NDVI-Climatological NDVI-MODIS Update of our current AVHRR to MODIS almost complete

  13. Aquarius/SAC-D Soil Moisture Retrieval Ver. ? • Ver. ? will attempt to utilize both passive and active L-band data in a modified (or new) retrieval algorithm. • This is the long-term objective of the project. Inputs Algorithm Product L Band passive H pol. TBD Ver. ? L Band passive V pol. L Band radar Forecast model LST MWR 36.5 V NIRST TIR NDVI-Climatological NDVI-MODIS

  14. Aquarius Soil Moisture Validation • Exploit existing resources (satellite/model/in situ) • AMSR-E, SMOS, SMAP • Argentina collaboration • In Situ • Sparse networks • Usually geographically distributed, public domain, real time • Scaling is a major challenge • Dense networks • Not many • Compatibility and standardization

  15. AZ-WG OK-LW GA-LR ID-RC Land Cover Conditions in the Watersheds DENSE: ARS Soil Moisture Validation Watersheds SPARSE:. The USDA SCAN Sparse and Dense In Situ Soil Moisture Networks (Examples)

  16. AMSR-E Soil Moisture Validation • Some AMSR-E experiences • Wide range of results with different algorithms • Validation period of record is a factor

  17. AMSR-E Validation: 4 Soil Moisture Algorithms - 7 Years of Data

  18. AMSR-E Validation: Season and Anomalies Can Impact Performance

  19. Recent SMAP Related Field Campaigns (Active and Passive Aircraft/Satellite Observations) • CanEx: June 2-16, 2010 • SJV10: June 29, October 25, 2010, TBD • SMAPEx: July 6-10, Dec. 4-8, 2010, and two additional in 2011.

  20. CanEx SM 2010: CSA and NASA • Primary mission objectives: (1) validation of SMOS brightness temperature and soil moisture products and (2) concurrent time series of active and passive microwave observations for SMAP passive, active, and combined soil moisture algorithms. • Flight dates: • June 2-15 Kenaston (KEN) (7 dates) (agricultural) • June 16 BERMS (1 date) (forest) • Other mission considerations • Both domains include two independent SMOS grid footprints. • Coordination with SMOS over passes. • Calibration and scaling of permanent in situ networks

  21. CanEx Permanent In Situ Networks Environment Canada BERMS University of Guelph Environment Canada Kenaston

  22. CanEx SMOS Pixel Centers and Aircraft Coverage SMOS products are at ~40 km resolution and gridded at ~16 km. Aircraft logistics limit the size of the coverage domain. Each area includes ~ 2 independent pixels (~33 by 70 km).

  23. CanEx Campaign Soil Moisture Sites Ground sampling sites included most permanent sites, the BERMS temporary network, and additional sites selected to be representative of domain, provide spatial coverage, and support multiple scaling objectives. 50 • BERMS Temporary Network • Will provide a longer record for SMOS/BERMS and to establish scaling of the limited permanent network. 59

  24. CanEx Campaign Aircraft NASA G-III UAVSAR: L-band fully polarimetric radar (Swath 20 km, resolution 6 m). Multiple lines were required to provide coverage between 35 and 45o. Environment Canada Twin Otter L-band dual polarization radiometer, 40o (single beam, resolution 3 km). Multiple lines were required to provide coverage . 6.9, 19, 37 and 89 GHz radiometers, 53o (single beam, res. are 1.3 km for 6.9 GHz and 0.8 km for the others)

  25. CanEx Summary Significant Rain Re-wetting

  26. CanEx Kenaston Area Composite radar image (HH-red, VV-blue, HV-green) 65o June 5, 2010 25o ~70 Data Sets!

  27. YA YB 40km Australia Campaign (SMAPEx) • Objective: concurrent multiple season active and passive microwave observations for passive, active, and combined soil moisture algorithms. (Supported by ARC) (Leads: J. Walker/R. Panciera Univ. Monash/Melbourne) • Details • 4, 1-week campaigns • Semi-arid, irrigated & dryland crops, grazing • Airborne: L-band radiometer & radar, VIS/IR/NIR/SWIR and Thermal IR • Ground: Soil Moisture, Vegetation Biomass/VWC/LAI/VIS&NIR, Roughness, Soil Temperature • Monitoring Network: 29 semi-permanent soil moisture stations (on SMAP 36km/9km/3km nested grids) July 6-10, 2010 Dec. 4-8, 2010 TBD, 2011

  28. MERIT Airborne Facility L-band Radar (focused SAR) 6 x SWIR 6 x Vis/NIR 6 x TIR L-band Radiometer (6 beams) PLIS: Polarimetric L-band Imaging Scatterometer: Frequency/bandwidth:1.26GHz/30MHz Polarisations: VV, VH, HV and HH Resolution: 10m Incidence angles 15º -45º on both sides of aircraft Antenna type: 2x2 patch array PLMR: Polarimetric L-band Multibeam Radiometer Frequency/bandwidth: 1.413GHz/24MHz Polarisations: V and H Resolution: 1km at 3km flying height, Incidence angles: +/- 7°, +/-21.5°, +/- 38.5° across track Antenna type: 8x8 patch array

  29. SMAP Major Field Campaignsver. 06/10 SMAPVEX08 High priority design/algorithm issues SMAPEx (Australia) 4 one-week campaigns to span four seasons Aircraft Radar/Radiometer CanEx-SM (Canada) Two-week soil moisture campaign Aircraft Radar/Radiometer CanEx-FT (Canada) Two-week freeze/thaw campaign Aircraft Radar/Radiometer SMAPVEX12 Major hydrology campaign Long duration Aircraft Radar/Radiometer SMAPVEX15 Extended campaign for both SM and FT Long duration Aircraft Radar/Radiometer Satellite Launch in Red

  30. L-Band Satellite Synergy • Continuity for L-band measurements of SMOS, Aquarius, SAOCOM, and SMAP. • Significant contributions to SMAP • Aquarius experience with RFI • Aquarius TB and so for SMAP L2 soil moisture algorithms Estimated Mission Timeline SMAP Aquarius SMOS SAOCOM

  31. Summary • Aquarius/SAC-D provides opportunities to explore new approaches to soil moisture retrieval. • First space borne data that can be used to assess the synergy of L-band passive and active observations for improving remote sensing of soils and vegetation. • Our approach will build from heritage satellite-based low frequency passive microwave algorithms • These utilize a unique element of Aquarius/SAC-D, 1.4 and 36.5 GHz radiometers. • Information from the MWR 36.5 V GHz is used to derive land surface temperature (LST), which is then employed in computing emissivity. • The retrieved LST is another potential Aquarius/SAC-D product. • These results will serve as a baseline for research on a combined passive-active soil moisture algorithm. • Improvements in corrections for roughness, vegetation, and transient water effects.

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