ESA CCI Soil moisture: Recent improvements of the soil moisture retrievals from passive

microwave sensors

Presenter: Robin van der SchalieLocation: Vienna University of Technology

CCI Soil Moisture User Workshop, 18-9-2017

Outline

• Goal: Providing an overview of the status and recent developments of the CCI Passive Soil Moisture dataset

1. Overview CCI Passive Soil Moisture dataset2. Integration of SMOS L-band mission

– Outlook: SMAP integration

3. Recalibrated parameterization for higher frequencies (C/X-band)– Outlook: Improved RFI filtering and daytime effective temperature input

4. Conclusion

1. CCI Passive Soil Moisture dataset

• ESA CCI SM v03.2 - Passive– Global soil moisture retrievals from

1978 – 2016 in a quarter degree grid– Combines retrievals based on multiple

satellite missions and frequencies,ranging between 1.4 and 18.7 GHz.

• Land Parameter Retrieval Model (LPRM, Owe et al., 2008)

– LPRM algorithm forms the base for the passive soil moisture retrievals

– Retrieves both soil moisture and vegetation optical depth simultaneously

– Using vertical and horizontal polarized passive microwave observations

– Derives VOD using an analytical derivation (Meesters et al., 2005), without the use of external information on vegetation

– Single global parameterization

1. The Land Parameter Retrieval Model

• The algorithm structure:

2. Integration of SMOS

• ESA’s Soil Moisture and Ocean Salinity satellite(SMOS, Kerr et al., 2010)

– Microwave Imaging Radiometer with Aperture Synthesis, MIRAS, measures at 1 frequency, 1.4GHz (V/H)

– Active: 2010 – now– 6:00 am/pm overpass– Improved sensing depth and vegetation penetration

• LPRM algorithm updated for SMOS applications– Successfully integrated CCI Soil Moisture v03.2 and on– Publications: Van der Schalie et al. (2015&2016)

• In combination with ESA’s Passive Microwave Soil Moisture Data Fusion Study:– Goal was to design and evaluate different data fusion methods for passive microwave soil moisture

retrievals from SMOS & AMSRE and evaluate the added value of SMOS based L-band retrievals – Our focus on optimization of LPRM for L-band retrievals– Other methods include:

• Neural Networks (Rodríguez-Fernández et al., 2016)• Regressions (Al-Yaari et al., 2016)

2. Integration of SMOS

• Quality of SMOS L-band retrievals compared to other sources of soil moisture information

– Right: Comparison against in situ data withSMOS and AMSRE in overlapping period

– Lower right: Comparison between AMSRE,ASCAT and SMOS using the Rvalue Technique

– Lower left: Comparison between AMSRE,ASCAT and SMOS using the TCA

– Publication: Van der Schalie et al. (In Review)

2. Outlook: SMAP

• First test runs of LPRM on the Soil Moisture Active Passive (SMAP, Entekhabi et al. 2010) satellite done

– LPRM used as developed for SMOS LPRM– First quality check by comparing both

MERRA and In Situ (± 1 year data)– Planning to run first tests within the CCI soon

2. Outlook: SMAP

• First test run of LPRM on SMAPIN SITU MERRA2

3. Parameterization update C- & X-band

• Recalibrated parameterization for higher frequencies (C- & X-band)

– Optimization of parameterization to best match SMOS LPRM& Scaling using SMOS LPRM for improved absolute values

– Included in CCI SM v03.2 for AMSRE and AMSR2, • to be applied to SMMR, TRMM and WindSat in future

reprocessing activities

– Major performance increase over moderate vegetation– Publication: Van der Schalie et al. (2017)

3. Outlook: Daytime temperature input

• Improved temperature input for AMSR-E/AMSR2 day-time observations– Overall improvement of approximately 15%– Confirmed through:

• Rvalue technique• Triple collocation technique• In situ Soil Moisture Network

– To be included in future reprocessing of AMSRE and AMSR2, could lead to a doublingof observations in areas with good results

– Publication: Parinussa et al. (2016)

Spatial distribution of optimal imposed bias on Teff (asc) Rvalue improvement (%)

3. Outlook: Improved RFI filtering

• Current approach for RFI detection (De Nijs et al., 2015) for AMSR2 is based on calculating the Standard Error

– Uses temporal information from C-, X- and Ku-band– Decision tree to choose which frequency to use– Currently only applicable for AMSR2 due to an extra C-band channel

• New approach for RFI detection extends this SE based method to be also applied on historical missions

– Can be used for AMSRE, WindSat, SMMR– Look both at SE and the ratio between the

SE from C- and X-band

4. Conclusion

• Goal: Providing an overview of the status and recent developments of the CCI Passive Soil Moisture dataset

• Successful integration of SMOS (L-band) mission– Now part of the passive CCI soil moisture dataset– Especially impacts areas with moderate to dense vegetation– Outlook: SMAP integration to extend/improve L-band retrievals

• Algorithm improvements higher frequencies (C/X-band)– Increased both consistency and quality, especially under moderate vegetation– Outlook: Daytime temperature improvements – Outlook: Improved RFI filtering