Aquarius/SAC-D Soil Moisture Retrieval and Applications

T. J. Jackson1 and H. Karszenbaum2

1 USDA ARS Hydrology and Remote Sensing Lab, Beltsville, MD USA2 Instituto de Astronomia y Fisica del espacio, Buenos Aires, Argentina

With contributions fromM. Cosh, R. Bindlish, P. O’Neill

6th Aquarius Science Team MeetingJuly 19-21, 2010

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

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)

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.

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).

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.

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Brightness temperature (TB) 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

(esurf) modified by the scattering () and attenuation () of the

vegetation (v)

esurf is the soil emission

(esoil) modified by the surface roughness (h)

The esoil 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

• 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

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surffp

soilfp hee

Brightness temperature (TB) 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

(esurf) modified by the scattering () and attenuation () of the

vegetation (v)

esurf is the soil emission

(esoil) modified by the surface roughness (h)

The esoil 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

Land Surface Temperature (LST)

• Required for all algorithms (TB 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.

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 Algorithm Product

.

.

.

.

.

• Approaches using various inputs and algorithms will be systematically developed and evaluated.

SCAL Band passive H pol.

Aquarius/SAC-D Soil Moisture RetrievalVer. 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 Algorithm Product

• 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

TBDL Band passive H pol.

Aquarius/SAC-D Soil Moisture RetrievalVer. ?

L Band passive V pol.

L Band radar

Forecast model LST

MWR 36.5 V

NIRST TIR

NDVI-Climatological

NDVI-MODIS

Ver. ?

Inputs Algorithm Product

• 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.

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

Sparse and Dense In Situ Soil Moisture Networks (Examples)

DENSE: ARS Soil Moisture Validation Watersheds

Land Cover Conditions in the WatershedsAZ-WG OK-LW GA-LR ID-RC

SPARSE:. The USDA SCAN

AMSR-E Soil Moisture Validation

• Some AMSR-E experiences– Wide range of results with different algorithms– Validation period of record is a factor

AMSR-E Validation: 4 Soil Moisture Algorithms - 7 Years of Data

Little Washita (Dsc)

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Observed Soil Moisture (m3/m3)

Es

tim

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d S

oil

Mo

istu

re (

m3 /m

3 )

JAXA

NASA

SCA

LPRM

AMSR-E Validation: Season and Anomalies Can Impact Performance

SCA Bias

-0.100-0.080-0.060-0.040-0.0200.0000.0200.0400.0600.0800.100

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M (

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)

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LR_d

WG_d

LW_a

LR_a

WG_a

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0.020

0.040

0.060

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0.100

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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.

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

CanEx Permanent In Situ Networks

Environment Canada

Kenaston

University of Guelph

Environment CanadaBERMS

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.

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.

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 45o.

L-band dual polarization radiometer, 40o (single beam, resolution 3 km). Multiple lines were required to provide coverage .

6.9, 19, 37 and 89 GHz radiometers, 53o (single beam, res. are 1.3 km for 6.9 GHz and 0.8 km for the others)

CanEx SummaryDate Saskatoon

Daily Rainfall (mm)

Prince Albert

Daily Rainfall (mm)

Aircraft Flights Site

Satellite Coverage Note

May 22-30 67.3 68.4

June 1

2 0.7 KEN SMOS

3 9.8 14.8 SMOS

4 2.2

5 6.8 KEN SMOS

6 0.2 KEN

7 5.5 2.8

8 16.9 7.2 SMOS Partial Twin Otter

9 KEN

10 5.5 4.0 SMOS

11 13.2

12

13 0.4 KEN SMOS

14 0.1 KEN

15 0.3 KEN SMOS Only UAVSAR

16 0.2 BERMS SMOS

Significant Rain

Re-wetting

25o

65o

CanEx Kenaston Area

Composite radar image (HH-red, VV-blue, HV-green)

June 5, 2010

~70 Data Sets!

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, 2010Dec. 4-8, 2010TBD, 2011

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

SMAP Major Field Campaignsver. 06/10

Year/Quarter

1 2 3 4

2008SMAPVEX08

2009 SMOS

2010SMAPEx

CanEx-SMSMAPEx

2011SMAPEx

Aquarius GCOM-WCanEx-FT

2012 SAOCOMSMAPVEX12

2013

2014 SMAP

2015 SMAPVEX15 SMAPVEX15 SMAPVEX15

• 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/RadiometerSatellite Launch in Red

L-Band Satellite Synergy• Continuity for L-band measurements of SMOS,

Aquarius, SAOCOM, and SMAP.

• Significant contributions to SMAP– Aquarius experience with RFI– Aquarius TB and o for SMAP L2 soil moisture algorithms

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2006 2008 2010 2012 2014 2016 2018 2020

SMAP

Aquarius

SMOS

SAOCOM

Estimated Mission Timeline

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.