1. EVALUATION OF SMAP LEVEL 2 SOIL MOISTURE ALGORITHMS USING SMOS DATA
Rajat Bindlish1, Thomas Jackson1, Tianjie Zhao1, Michael Cosh1, Steven Chan2, Peggy O'Neill3, Eni Njoku2, Andreas Colliander2, Yann H. Kerr4, Jiancheng Shi5
1USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD
2Jet Propulsion Lab, Pasadena, CA
3NASA Goddard Space Center, Greenbelt, MD
4CESBIO, France
5University of California, Santa Barbara, CA

2. Objectives
Reprocess SMOS observations to simulate SMAP observations at a constant incidence angle of 40o.
This provides a brightness temperature data set that closely matches the observations that would be provided by the SMAP radiometer.
Conduct an evaluation of the different SMAP soil moisture algorithms under consideration using the simulated data.
Results will aid in the development and selection of the different land surface parameters (roughness and vegetation) and ancillary data sets.

3. Evaluations
Analysis will involve several steps that will progressively move toward the actual SMAP characteristics.
Evaluate the SMAP ancillary data options
Vegetation
SMOS Tau
MODIS Climatology
Real time MODIS
Soil Temperature
ECMWF
GMAO/MERRA
NCEP
Algorithm inter-comparisons
Single Channel Algorithm (H-pol) (baseline)
Single Channel Algorithm (V-pol)
Dual Channel Algorithm
LPRM

4. Metrics USDA ARS watersheds SMOS soil moisture ECMWF soil moisture
SCAN sites
Other sites from the ISMN and SMOS Cal/Val

5. USDA watersheds Watershed Size (km2) Soil Moisture Sites Climate
Annual Rainfall (mm)
Topography
Land Use
Little Washita, OK
610 16
Sub humid
750
Rolling
Range/ wheat
Little River, GA
334 29
Humid
1200
Flat
Row crop/ forest
Walnut Gulch, AZ
148 21
Semi-arid
320
Range
Reynolds Creek, ID
238 19
500
Mountainous

6. Approach
Develop a SMOS/SMAP data product that includes TBH and TBV at an incidence angle of 40o.
Evaluate the algorithms using different ancillary dataset for soil moisture retrievals.
Full SMAP retrievals using SMOS/SMAP data along with SMAP ancillary data sets on SMAP grid.
Period of Analysis: Nov May 2011

7. Development of SMOS/SMAP data product
Uses L1c data
SMOS observations from extended FOV areas can influence the overall brightness temperatures for a location (x,y)
The use of observations from alias-free zones provides a more reliable TB at 40o. Observations from extended FOV are noisier.
600 km
1400 km

8. Development of SMOS/SMAP data product
Basic steps performed in this processing:
Removing the aliased portions of the SMOS orbit
Filtering to remove anomalous TB observations + RFI check
Interpolation to fill-in full/dual-pol TB observations for each snapshot
Transforming from antenna to Earth reference frame (Computing X-Y to H-V TB)
RFI check (0<TB<320 K, TBH<TBV)
Curve fitting of available TB observations at multiple incidence angles to estimate 40o TB

9. Development of SMOS/SMAP data product
The SMOS/SMAP product has a narrower swath (extended FOV zones are not included)
The reprocessed product has less noise. This is especially true for the edges of the swath. Higher quality TB is important for SMAP algorithm development.
SMOS does not perform a multi-parameter retrieval in the EFOV zones
Full Swath Processing
Reduced Swath Processing

10. Baseline Results Single Channel Algorithm (SCA) – baseline
Vegetation – MODIS climatology
Land cover – MODIS IGBP
Soil temperature - ECMWF
Precipitation, Snow, Frozen soil – ECMWF
Vegetation parameter (b), roughness parameter (h) and single scattering albedo constant for all land covers

11. SCA – Global Results
Low soil moisture over desert and arid regions (Africa, Middle East, Central Asia, and Central Australia).
High values over forested areas in northern latitudes (Canada and Russia) and over portions of South America.
Northern latitudes flagged due to either snow or frozen soil in June.
South-East Asia, Northern South America flagged because ECMWF forecasts indicated precipitation at the time of SMOS overpass.

12. SCA – Watershed Results
Wide range of observed soil moisture conditions
SCA captures the range of observed soil moisture
Low bias and RMSE over LR
Most of error over LW is due to dry bias
Good agreement over WG with near zero bias
Underestimation bias over RC

13. SCA – Watershed Results
Ascending
Descending
RMSE
Bias R N
Little Washita, OK
0.046
-0.040
0.899 41
0.039
-0.028
0.940 34
Little River, GA
0.027
0.005
0.867 36
0.029
0.880 39
Walnut Gulch, AZ
-0.005
0.718 38
0.023
-0.014
0.664 45
Reynolds Creek, ID
0.041
-0.037
0.383 14
0.058
-0.054
0.621 9
RMSE (Root mean square error), and Bias are in m3/m3.
R=Linear correlation coefficient, N=Number of samples
The sample size is reduced due to removal of extended FOV TBs.
This results in a repeat cycle of about 9-10 days.

14. Vegetation Ancillary Data
MODIS Climatology Tau (July 1-10)
MODIS derived tau has greater spatial variability than the SMOS tau
SMOS tau is lower over high vegetated areas
SMOS tau is higher over low vegetation areas
No SMOS tau over dense forests
SMOS Estimated Tau (July 1-10)

15. Vegetation Ancillary Data
SCA using MODIS (July 1-10)
SCA using SMOS Tau results in higher soil moisture (higher tau results in over correction)
Lower soil moisture estimates over northern latitudes using MODIS NDVI (Canada, Russia) due to lower tau values
SCA using SMOS Tau (July 1-10)

16. Vegetation Ancillary Data: SMOS, MODIS-CI, and MODIS-RT
Using the SMOS tau results in greater scatter due to day to day variability in tau. Also a positive bias.
Very little differences between MODIS climatology/realtime based tau.
Using the MODIS tau results in near zero bias over LR and WG and underestimates over LW and RC.
Some of the bias may be due to use of constant vegetation and roughness parameters.

17. Vegetation Ancillary Data: SMOS, MODIS-CI, and MODIS-RT
Watershed
Flag
SMOS Asc. 0600
RMSE
Bias R N
Little Washita, OK
MODIS Cl
0.046
-0.040
0.899 41
MODIS RT
0.045
-0.041
0.922
SMOS Tau
0.059
0.052
0.869 40
Little River, GA
0.027
0.005
0.867 36
0.026
0.004
0.874
0.127
0.118
0.252 35
Walnut Gulch, AZ
-0.005
0.718 38
0.028
-0.004
0.731
0.074
0.043
0.726 30
Reynolds Creek, ID
0.041
-0.037
0.383 14
0.040
0.007
0.010
0.480 11
MODIS Cl – MODIS Climatology, MODIS RT – MODIS Realtime
RMSE (Root mean square error), and Bias are in m3/m3.
R=Linear correlation coefficient, N=Number of samples

18. SCA (V pol) - Results Similar to SCA (H pol) results.
Low soil moisture over desert and arid regions (Africa, Middle East, Central Asia, and Central Australia).
High values over forested areas in northern latitudes (Canada and Russia) and over portions of South America.

19. SCA (V pol) - Results H pol better over LR and WG
V pol better over LW and RC
Choice of a constant set of global vegetation and roughness parameters results in different biases
Vegetation parameters need to be land cover type specific
H pol (b=0.08, ω=0.05)
V pol (b=0.10, ω=0.06)
Need to take a closer look at these results.

20. Summary
A procedure was developed to reprocess SMOS TB to simulate SMAP radiometer data.
The SCA algorithm was implemented using the SMOS/SMAP data set at a 40o incidence angle.
SCA (MODIS) performs well in comparison with in situ observations.
SCA using V pol observations performs satisfactorily. The choice of vegetation parameter can greatly affect the overall bias. Vegetation parameters need to be land cover specific to minimize bias over different domains.
Initial results indicate the SMAP algorithms can meet the target accuracy requirement of 0.04 m3/m3.
Further analysis and research is ongoing.
This work will help in the selection and development of the SMAP passive L2 soil moisture algorithm.