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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, MD2Jet Propulsion Lab, Pasadena, CA
3NASA Goddard Space Center, Greenbelt, MD4CESBIO, France
5University of California, Santa Barbara, CA
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.
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
Metrics
USDA ARS watersheds SMOS soil moisture ECMWF soil moisture SCAN sites Other sites from the ISMN and SMOS Cal/Val
USDA watersheds
WatershedSize
(km2)
Soil Moisture
SitesClimate
Annual Rainfall (mm)
Topography Land Use
Little Washita, OK
610 16Sub humid
750 RollingRange/ wheat
Little River, GA
334 29 Humid 1200 FlatRow crop/ forest
Walnut Gulch, AZ
148 21 Semi-arid 320 Rolling Range
Reynolds Creek, ID
238 19 Semi-arid 500 Mountainous Range
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 2009 - May 2011
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
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
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
Development of SMOS/SMAP data product
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
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.
SCA – Watershed Results
0
0.1
0.2
0.3
0.4
0.5
0 0.1 0.2 0.3 0.4 0.5
Esti
mat
ed V
SM (m
3 /m
3 )
In Situ VSM (m3/m3)
Comparison with In Situ Watersheds (Asc)
LR
LW
WG
RC
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
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Esti
mat
ed V
SM (m
3 /m
3 )
In Situ VSM (m3/m3)
Comparison with In Situ Watersheds (Dsc)
LR
LW
WG
RC
SCA – Watershed Results
WatershedAscending Descending
RMSE Bias R N RMSE Bias R N
Little Washita, OK 0.046 -0.040 0.899 41 0.039 -0.028 0.940 34Little River, GA 0.027 0.005 0.867 36 0.039 0.029 0.880 39
Walnut Gulch, AZ 0.027 -0.005 0.718 38 0.023 -0.014 0.664 45Reynolds 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.
MODIS Climatology Tau (July 1-10)
SMOS Estimated Tau (July 1-10)
Vegetation Ancillary Data
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
SCA using MODIS (July 1-10)
SCA using SMOS Tau (July 1-10)
Vegetation Ancillary Data
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
Vegetation Ancillary Data: SMOS, MODIS-CI, and MODIS-RT
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Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Reynolds Creek Watershed (Asc)
SCA (Tau)
SCA (MODIS RT)
SCA (MODIS Cl)0
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0 0.1 0.2 0.3 0.4 0.5
Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Walnut Gulch Watershed (Asc)
SCA (Tau)
SCA (MODIS RT)
SCA (MODIS Cl)
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0 0.1 0.2 0.3 0.4 0.5
Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Little Washita Watershed (Asc)
SCA (Tau)
SCA (MODIS RT)
SCA (MODIS Cl)
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.
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Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Little River Watershed (Asc)
SCA (Tau)
SCA (MODIS RT)
SCA (MODIS Cl)
Watershed FlagSMOS Asc. 0600
RMSE Bias R N
Little Washita, OKMODIS Cl 0.046 -0.040 0.899 41
MODIS RT 0.045 -0.041 0.922 41SMOS Tau 0.059 0.052 0.869 40
Little River, GAMODIS Cl 0.027 0.005 0.867 36
MODIS RT 0.026 0.004 0.874 36SMOS Tau 0.127 0.118 0.252 35
Walnut Gulch, AZMODIS Cl 0.027 -0.005 0.718 38
MODIS RT 0.028 -0.004 0.731 38SMOS Tau 0.074 0.043 0.726 30
Reynolds Creek, IDMODIS Cl 0.041 -0.037 0.383 14MODIS RT 0.040 -0.037 0.383 14SMOS Tau 0.007 0.010 0.480 11
MODIS Cl – MODIS Climatology, MODIS RT – MODIS RealtimeRMSE (Root mean square error), and Bias are in m3/m3.R=Linear correlation coefficient, N=Number of samples
Vegetation Ancillary Data: SMOS, MODIS-CI, and MODIS-RT
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.
SCA (V pol) - Results
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imat
ed V
SM
(m
3 /m
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In Situ VSM (m3/m3)
Reynolds Creek Watershed (Asc)
SCA (V pol)
SCA (H pol)
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0.5
0 0.1 0.2 0.3 0.4 0.5
Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Little Washita Watershed (Asc)
SCA (V pol)
SCA (H pol)
0
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0.5
0 0.1 0.2 0.3 0.4 0.5
Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Walnut Gulch Watershed (Asc)
SCA (V pol)
SCA (H pol)
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Est
imat
ed V
SM
(m
3 /m
3 )
In Situ VSM (m3/m3)
Little River Watershed (Asc)
SCA (V pol)
SCA (H pol)
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.
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.
Summary