1. SMAP Cal/Val T. J. Jackson USDA ARS Hydrology and Remote Sensing Lab December 11, 2012

2. Soil Moisture Satellite Missions: Past, Present and Future SMAP –Mission –Cal/Val Sparse Networks in Cal/Val Challenges to using COSMOS Outline

3. 100 km10 km1 km Day Week Month Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) Passive Active Historic Perspective on Remote Sensing Scale ranges are based on the NRC Decadal Survey

4. 100 km10 km1 km Day Week Month Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) AMSR-E ASCAT 2002-2011 AMSR-E X-band 40 km Scale ranges are based on the NRC Decadal Survey ASCAT GCOM-W ALOS SAR ALOS-2 2012 GCOM-W Soil moisture product is the same as AMSR-E

5. European Space Agency (ESA) 1.4 GHz Microwave Radiometer 40 km footprint, three day global coverage Launch November 2009 Soil Moisture and Ocean Salinity Mission (SMOS)

6. 100 km10 km1 km Day Week Month Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) AMSR-E ASCAT SMOS 1.4 GHz 6.0 GHz 10.0 GHz Same resolution but a better product Technology demo

7. Aquarius/SAC-D 390 km Inner beam 76×94 km Outer beam 96×156 km Middle beam 84×120 km Mission (NASA and CONAE) –Sun-synch orbit –6 am (Des.)/6 pm (Asc.) –Night time look direction –657 km Alt; 7 day revisit –Launch: June 2011 Aquarius Instrument –L-band Polarimetric –Radiometer and Scatterometer –3 Beam Pushbroom –Incidence angles of 29.36°, 38.49°, and 46.29° SAC-D –MWR –Other

8. 100 km10 km1 km Day Week Month Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) AMSR-E ASCAT SMOS Aquarius Scale ranges are based on the NRC Decadal Survey Active and passive L- band but coarser spatial resolution and temporal repeat

9. SAOCOM: SAtélite Argentino de Observación COn Microondas Comisión Nacional de Actividades Espaciales (CONAE)-Argentina Space Agency Constellation of two identical satellites SAOCOM 1A and SAOCOM 1B carrying an L-band polarimetric SAR instrument SAOCOM will be in a sun-synchronous nearly circular frozen polar orbit (06:12 am LTAN/619.6 km) Repeat cycle of 16 days (8 days with full constellation of 2 satellites) Launch of SAOCOM 1A in 2015. Challenge: Infer surface soil moisture values from SAR measurements with varying incidence angles.

10. 100 km10 km1 km Day Week Month Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) GCOM-W ASCAT SMOS ALOS-2 Aquarius Scale ranges are based on the NRC Decadal Survey Commitment to a soil moisture product SAOCOM

11. Soil Moisture Satellite Missions: Past, Present and Future SMAP –Mission –Cal/Val Sparse Networks in Cal/Val Challenges to using COSMOS Outline

12. SMAP Level 1 Science Requirements TJJ–12 (a) North of 45N latitude, (b) Percent classification accuracy (binary freeze/thaw), (c) Volumetric water content, 1-σ in [cm 3 /cm 3 ] units Requirement Hydro- Meteorology Hydro- Climatology Carbon Cycle Baseline Mission Threshold Mission Soil Moisture Freeze/Thaw Freeze/Thaw Resolution4–15 km50–100 km1–10 km10 km3 km10 km Refresh Rate2–3 days3–4 days2–3 days (a) 3 days2 days3 days Accuracy0.04-0.06 (c) 80–70% (b) 0.04 (c) 80% (b) 0.06 (c) 70% (b) Mission Duration36 months18 months These are the L1 priority products and requirements. Other product accuracies derive from L2 requirements. Defines the baseline mission. The SMAP Project proposed the active-passive approach for meeting these requirements. The NRC Decadal Survey identified numerous potential applications for SM/FT observations. These were grouped into three categories with a spatial resolution, refresh rate, and accuracy.

13. L-band unfocused SAR and radiometer system, offset-fed 6 m light-weight deployable mesh reflector. Shared feed for  1.26 GHz HH, VV, HV Radar at 1-3 km (30% nadir gap)  1.4 GHz H, V, 3 rd and 4 th Stokes Radiometer at 40 km Conical scan, fixed incidence angle (40 o across swath Contiguous 1000 km swath with 2-3 days revisit (8 day repeat) Sun-synchronous 6am/6pm orbit (680 km) Launch October 31, 2014 (now in Phase C/D) Mission duration 3 years SMAP Project Approach TJJ–13

14. ProductDescription Gridding (Resolution ) Latency** L1A_RadiometerRadiometer Data in Time-Order-12 hrs Instrument Data L1A_RadarRadar Data in Time-Order-12 hrs L1B_TBRadiometer T B in Time-Order(36x47 km)12 hrs L1B_S0_LoResLow Resolution Radar σ o in Time-Order(5x30 km)12 hrs L1C_S0_HiResHigh Resolution Radar σ o in Half-Orbits 1 km (1-3 km) 12 hrs L1C_TBRadiometer T B in Half-Orbits36 km12 hrs L2_SM_ASoil Moisture (Radar)3 km24 hrs Science Data (Half-Orbit) L2_SM_PSoil Moisture (Radiometer)36 km24 hrs L2_SM_APSoil Moisture (Radar + Radiometer)9 km24 hrs L3_FT_AFreeze/Thaw State (Radar)3 km50 hrs Science Data (Daily Composite) L3_SM_ASoil Moisture (Radar)3 km50 hrs L3_SM_PSoil Moisture (Radiometer)36 km50 hrs L3_SM_APSoil Moisture (Radar + Radiometer)9 km50 hrs L4_SMSoil Moisture (Surface and Root Zone )9 km7 days Science Value-Added L4_C Carbon Net Ecosystem Exchange (NEE) 9 km14 days * Over outer 70% of swath. ** The SMAP project will make a best effort to reduce the data latencies beyond those shown in this table. TJJ–14 SMAP Science Products

15. 100 km10 km1 km Day Week Month SMAP Radar-Radiometer Climate Applications Weather Applications Carbon Cycle Applications Applications Resolved Spatial Scales Resolved Temporal Scales Evolution of Microwave Remote Sensing (Land) GCOM-W ASCAT SMOS ALOS-2 Scale ranges are based on the NRC Decadal Survey SMAP will support established climate and carbon cycle applications and will open up new applications in weather and carbon cycle. Aquarius SAOCOM

16. SMAP L1 Requirements Impacting Cal/Val Level 1 (Baseline) Science Requirements and Mission Success Criteria Provide estimates of soil moisture in the top 5 cm of soil with an error of no greater than 0.04 m 3 /m 3 volumetric (one sigma) at 10 km spatial resolution and 3-day average intervals over non-excluded regions. Conduct a calibration and validation program to verify data delivered meets the requirements. Provide estimates of surface binary freeze/thaw state in the region north of 45N latitude, which includes the boreal forest zone, with a classification accuracy of 80% at 3 km spatial resolution and 2-day average intervals. Threshold mission requirements are 0.06 m 3 /m 3 and 70% What the SMAP Project and NASA have agreed to do.

17. CEOS Validation Stages Adopted for SMAP TJJ–17 Validation StageDescription Stage 1 Product accuracy is assessed from a small (typically < 30) set of locations and time periods by comparison with in situ or other suitable reference data. Stage 2 Product accuracy is estimated over a significant set of locations and time periods by comparison with reference in situ or other suitable reference data. Spatial and temporal consistency of the product and with similar products have been evaluated over globally representative locations and time periods. Results are published in the peer-reviewed literature. Stage 3 Uncertainties in the product and its associated structure are well quantified from comparison with reference in situ or other suitable reference data. Uncertainties are characterized in a statistically robust way over multiple locations and time periods representing global conditions. Spatial and temporal consistency of the product and with similar products have been evaluated over globally representative locations and periods. Results are published in the peer-reviewed literature. Stage 4 Validation results for stage 3 are systematically updated when new product versions are released and as the time-series expands. Validation: The process of assessing, by independent means, the quality of the data products derived from the system outputs. The quality is determined with respect to the specified requirements.

18. SMAP Validation Methodologies TJJ–18 MethodologyRoleConstraintsResolution Core Validation Sites Accurate estimates of products at matching scales for a limited set of conditions In situ sensor calibration Limited number of sites In Situ Testbed Cal/Val Partners Sparse Networks One point in the grid cell for a wide range of conditions In situ sensor calibration Up-scaling Limited number of sites In Situ Testbed Scaling methods Cal/Val Partners Satellite Products Estimates over a very wide range of conditions at matching scales Validation Comparability Continuity Validation studies Distribution matching Model Products Estimates over a very wide range of conditions at matching scales Validation Comparability Validation studies Distribution matching Field Campaigns Detailed estimates for a very limited set of conditions Resources Schedule conflicts Airborne simulators Partnerships

19. SMAP Cal/Val Approach Pre-launch Focus on insuring that there are means in place to fulfill the mission objectives –Acquire and process data with which to calibrate, test, and improve models and algorithms used for retrieving SMAP science data products –Develop and test the infrastructure and protocols for post-launch validation Post-launch Focus on validating that the products meet their quantified requirements –Calibrate, verify, and improve the performance of the science algorithms –Validate accuracies of the science data products as specified in L1 science requirements according to Cal/Val timeline TJJ–19

20. Science Data Validation and Delivery Timeline TJJ–20

21. Soil Moisture Satellite Missions: Past, Present and Future SMAP –Mission –Cal/Val Sparse Networks in Cal/Val Challenges to using COSMOS Outline

22. SMAP Validation Methodologies TJJ–22 MethodologyRoleConstraintsResolution Core Validation Sites Accurate estimates of products at matching scales for a limited set of conditions In situ sensor calibration Limited number of sites In Situ Testbed Cal/Val Partners Sparse Networks One point in the grid cell for a wide range of conditions In situ sensor calibration Up-scaling Limited number of sites In Situ Testbed Scaling methods Cal/Val Partners Satellite Products Estimates over a very wide range of conditions at matching scales Validation Comparability Continuity Validation studies Distribution matching Model Products Estimates over a very wide range of conditions at matching scales Validation Comparability Validation studies Distribution matching Field Campaigns Detailed estimates for a very limited set of conditions Resources Schedule conflicts Airborne simulators Partnerships

23. SMAP Cal/Val Partners Program In situ observations are essential to SMAP Cal/Val There were only a few high quality resources available Increasing the number was constrained by –The time and effort to establish a site –No $ to support these Action: ROSES DCL –No cost collaboration –Minimum standards –In situ data in exchange for early access to SMAP products Based on responses –Refined definitions –Missed some important resources TJJ–23

24. SMAP Cal/Val Partners: Site Types Core Validation Sites: In situ observing sites that provide well- characterized estimates of a L2-L4 product at a matching spatial scale, a direct benchmark reference for the products. Additional minimum criteria are: –Provides calibration of the in situ sensors –Up-scaling strategy provided (implemented by Project) –Provides data in a timely manner –Long term commitment by the sponsor/host Contributing Validation Sites: In situ observing sites that provide estimates of a L2-L4 product but do not meet all of the minimum criteria for a Core Validation Site. (i.e. sparse networks) –Contributing Validation Sites are a supplemental resource (In assessing meeting mission requirements but important in Stage 2 Validation). –The baseline approach to using sparse networks is the triple-collocation technique. Efforts to improve this approach are desirable. TJJ–24

25. Soil Moisture Satellite Missions: Past, Present and Future SMAP –Mission –Cal/Val Sparse Networks in Cal/Val Challenges to using COSMOS –Up-scaling –Contributing depth –Integrating networks –Resolving “noise” Outline

26. TJJ–26 Proposed best practices * : First, apply temporal stability analysis (Cosh et al., 2006; 2008) to select sampling sites with temporal dynamics that best mimic footprint scale variability. Second, use land surface modeling (Crow et al., 2005) and/or an intensive field campaign (De Rosnay et al. 2009) to refine understanding of the relationship between point- and footprint-scale variability (i.e., F↑ on left). Third, apply triple collocation (Mirrales et al., 2010) to estimate impact of residual sampling errors on RMSE validation results. Footprint-scale θ POINT F ↑ ( θ POINT ) * Based on: Crow et al., “Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products,” Reviews of Geophysics, 50, RG2002, doi:10.1029/2011RG000372, 2012. Up-scaling Challenge: Using point-scale soil moisture observations to validate footprint-scale SMAP retrievals. Challenge: Scaling Points to Footprints

27. Application of Triple Co-Location To Estimate Random Sampling Error in Sparse Ground Observations Remote Sensing (RS)-SMAP Land Surface Model (LSM) Sparse Ground Observation (SPARSE) 1) Obtain three independent (and uncertain) estimates of footprint-scale soil moisture: 2) Assume independent errors and sample the following temporal average to estimate random sampling error in SPARSE: 3) Use this estimate to correct soil moisture RMSE estimates derived from RS versus SPARSE comparisons for sampling error in SPARSE. TJJ–27

28. Intensive sampling of a limited number of sites? Exploit the Rover? How many conditions? Challenge: Scaling Points to Footprints

29. Challenge: Matching Depth to a SMAP Product We all understand why the contributing depth varies with the wetness and shape of the profile. SMAP is only concerned with soil moisture of two layers; 0-5 cm and the root-zone (1 m) (for validation). Can COSMOS produce standard depth products?

30. Challenge: Integrating Networks Lots of points that are currently not compatible. Still need to address the variable contributing depth issue but there are more options for matching the depths of other networks. First step: co-location of instruments (i.e MOISST)

31. USDA-NRCS-Soil Climate Analysis Network – D. Harms Natural Resource Conservation Service monitoring SMAP Soil Moisture (surface and profile) CONUS - 181 Telemetry and FTP Hourly Status – Operating and Developing MeasurementMethodDepths Soil MoistureHydra5, 10, 20, 50, and 100 cm Soil TemperatureHydra5, 10, 20, 50, and 100 cm PrecipitationTipping Bucket - TJJ–31

32. U.S. Climate Reference Network M. A. Palecki and J.E. Bell, NCDC USCRN observes climate change Soil Moisture/Temperature Product Validation with sparse network 114 sites, 20 field calibrated in FY13 Satellite to NCDC, Internet to SMAP 2-3 hours Instruments in place, communication in place, gravimetric sampling of subset planned for FY13 Measurement TypeMethodDepths (cm) Soil MoistureCoaxial Impedance Dielectric 5,10,20,50,100 Soil TemperatureThermistor5,10,20,50,100 Meteorological Variables (air T, prec, surface T, global solar Platinum resistance thermometer, weighing bucket, IR, pyranometer 150 Above Ground TJJ–32

33. Challenge: Resolving “Noise” Vegetation, atmosphere,….