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

Watershed Size(km2)

Soil Moisture

SitesClimate

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 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

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Estim

ated

VSM

(m3 /

m3 )

In Situ VSM (m3/m3)

Comparison with In Situ Watersheds (Asc)

LRLWWGRC

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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Estim

ated

VSM

(m3 /

m3 )

In Situ VSM (m3/m3)

Comparison with In Situ Watersheds (Dsc)

LRLWWGRC

SCA – Watershed Results

WatershedAscending Descending

RMSE Bias R N RMSE Bias R NLittle 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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Estim

ated

VSM

(m3 /m

3 )

In Situ VSM (m3/m3)

Reynolds Creek Watershed (Asc)

SCA (Tau)

SCA (MODIS RT)

SCA (MODIS Cl)0

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Estim

ated

VSM

(m3 /m

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In Situ VSM (m3/m3)

Walnut Gulch Watershed (Asc)

SCA (Tau)

SCA (MODIS RT)

SCA (MODIS Cl)

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Estim

ated

VSM

(m3 /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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Estim

ated

VSM

(m3 /m

3 )

In Situ VSM (m3/m3)

Little River Watershed (Asc)

SCA (Tau)

SCA (MODIS RT)

SCA (MODIS Cl)

Watershed Flag SMOS Asc. 0600RMSE 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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Estim

ated

VSM

(m3 /m

3 )

In Situ VSM (m3/m3)

Reynolds Creek Watershed (Asc)

SCA (V pol)

SCA (H pol)

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Estim

ated

VSM

(m3 /m

3 )

In Situ VSM (m3/m3)

Little Washita Watershed (Asc)

SCA (V pol)

SCA (H pol)

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Estim

ated

VSM

(m3 /m

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In Situ VSM (m3/m3)

Walnut Gulch Watershed (Asc)

SCA (V pol)

SCA (H pol)

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Estim

ated

VSM

(m3 /m

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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