1. Aquarius/SAC-D Soil Moisture Retrieval and Applications T. J. Jackson 1 and H. Karszenbaum 2 1 USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD USA 2 Instituto de Astronomia y Fisica del espacio, Buenos Aires, Argentina With contributions from M. Cosh, R. Bindlish, P. O’Neill 6 th Aquarius Science Team Meeting July 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 (  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. Brightness temperature (T B ) is a function of emissivity (e) and physical temperature (T). The second term is small resulting in a simple relationship for e The observed e is the result of contributions from the soil surface (e surf ) modified by the scattering (  ) and attenuation (  ) of the vegetation (v) e surf is the soil emission (e soil ) modified by the surface roughness (h) The e soil is a function of the soil dielectric properties (  r ) Observations are made at a specific polarization (p), frequency (f), and angle (  ) A dielectric mixing model relates  r to soil moisture based on sand and clay fractions From Brightness Temperature to Soil Moisture 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 Unknowns

8. Problem: There are more unknowns than measurements: under determined system of equations Solutions attempt to reduce the dimensionality of the problem. Some approaches are: 1.Multiple angle observations. 2.Assuming polarization independence (or defining the dependence) of parameters. 3.Assuming frequency independence of parameters. 4.Estimating parameters using ancillary information. 5.Localized calibration. Initial selection for this study: –Single Channel Algorithm (SCA) –Alternatives: three algorithms being evaluated by SMAP Soil Moisture Retrieval Algorithms X X Not compatible with Aquarius/SAC-D

9. Brightness temperature (T B ) is a function of emissivity (e) and physical temperature (T). The second term is small resulting in a simple relationship for e The observed e is the result of contributions from the soil surface (e surf ) modified by the scattering (  ) and attenuation (  ) of the vegetation (v) e surf is the soil emission (e soil ) modified by the surface roughness (h) The e soil is a function of the soil dielectric properties (  r ) Observations are made at a specific polarization (p), frequency (f), and angle (  ) A dielectric mixing model relates  r to soil moisture based on sand and clay fractions 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 Aquarius Radiometer Aquarius Radar SAC-D 36.5 GHz or NIRST Aquarius Radar Potential of Aquarius/SAC-D Measurements in Soil Moisture Retrieval

10. Land Surface Temperature (LST) Required for all algorithms (T B 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. SCAL Band passive H pol. Aquarius/SAC-D Soil Moisture Retrieval L Band passive V pol. L Band radar Forecast model LST MWR 36.5 V NIRST TIR NDVI-Climatological NDVI-MODIS Alternatives Ver. X.1 Ver. X.2 Inputs AlgorithmProduct.......... Approaches using various inputs and algorithms will be systematically developed and evaluated.

12. SCAL Band passive H pol. Aquarius/SAC-D Soil Moisture Retrieval Ver. 1 L Band passive V pol. L Band radar Forecast model LST MWR 36.5 V NIRST TIR NDVI-Climatological NDVI-MODIS Alternatives Ver. 1.1 (BASELINE) Ver. 1.2 Inputs AlgorithmProduct 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. Update of our current AVHRR to MODIS almost complete

13. TBDL Band passive H pol. Aquarius/SAC-D Soil Moisture Retrieval Ver. ? L Band passive V pol. L Band radar Forecast model LST MWR 36.5 V NIRST TIR NDVI-Climatological NDVI-MODIS Ver. ? Inputs AlgorithmProduct 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.

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

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 Kenaston University of Guelph Environment Canada BERMS

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

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. 59 50 BERMS Temporary Network Will provide a longer record for SMOS/BERMS and to establish scaling of the limited permanent network.

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

25. CanEx Summary DateSaskatoon Daily Rainfall (mm) Prince Albert Daily Rainfall (mm) Aircraft Flights Site Satellite CoverageNote May 22-3067.368.4 June 1 20.7KENSMOS 39.814.8SMOS 42.2 56.8KENSMOS 60.2KEN 75.52.8 816.97.2SMOSPartial Twin Otter 9KEN 105.54.0SMOS 1113.2 12 130.4KENSMOS 140.1KEN 150.3KENSMOSOnly UAVSAR 160.2BERMSSMOS Significant Rain Re-wetting

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

27. 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) YA YB 40km July 6-10, 2010 Dec. 4-8, 2010 TBD, 2011

28. MERIT Airborne Facility 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 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 6 x TIR L-band Radar (focused SAR) L-band Radiometer (6 beams) 6 x Vis/NIR 6 x SWIR

29. SMAP Major Field Campaigns ver. 06/10 Year/ Quarter 1234 2008 SMAPVEX08 2009SMOS 2010 SMAPEx CanEx-SM SMAPEx 2011 SMAPEx AquariusGCOM-W CanEx-FT 2012SAOCOM SMAPVEX12 2013 2014SMAP 2015SMAPVEX15 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 T B and  o for SMAP L2 soil moisture algorithms SMAP Aquarius SMOS SAOCOM Estimated Mission Timeline

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.