The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land Surface

Dall'Amico, J.; Schlenz, F.; Loew, Alexander; Mauser, Wolfram; Kainulainen, Juha; Balling, Jan E.; Bouzinac, Catherine

doi:10.1109/tgrs.2012.2196523

Cited by 8 publications

(16 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are publicly available over the International Soil Moisture Network (ISMN) (Dorigo et al, 2011;http://www.ipf.tuwien.ac.at/insitu/). In addition to that ground-based L-band radiometer measurements and spatially distributed datasets of soil moisture, vegetation and airborne L-band radiometer measurements are available dall'Amico et al, 2012b;Schlenz et al, 2012b). The land surface model PROMET (Mauser and Bach, 2009) has been coupled to the radiative transfer model L-MEB to model land surface states in the Upper Danube Catchment on a 1 km grid as well as the resulting microwave emissions in the L-band.…”

Section: F Schlenz Et Al: Analysis Of Smos Brightness Temperature Amentioning

confidence: 99%

“…During the SMOS Validation Campaign the airborne L-band radiometer EMIRAD 2 (owned by the technical University of Denmark; Skou et al, 2010) was flown on five days onboard the Skyvan aircraft over the Vils test site to measure brightness temperatures emitted by the land surface over roughly 20 % of the central SMOS pixel (dall'Amico et al, 2012b) around SMOS morning overpass time. EMIRAD is a thoroughly validated radiometer that has been used in a variety of studies (Skou et al, 2010;Delwart et al, 2008) and is, therefore, used as reference in this study.…”

Section: Airborne Datamentioning

confidence: 99%

“…Most of these threshold have been taken from while some are more strict than the values used in the SMOS L2 processing. They have been tested with airborne and SMOS brightness temperatures and proven to be valuable under local conditions (dall'Amico et al, 2012b). L1c data are delivered as top of atmosphere (TOA) brightness temperatures in antenna geometry that need to be rotated to enable a comparison with brightness temperatures on the Earth's surface which is performed in the next step.…”

Section: Smos Datamentioning

confidence: 99%

See 2 more Smart Citations

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Schlenz¹,

Dall'Amico²,

Mauser³

et al. 2012

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB). The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and land surface temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in southern Germany. The brightness temperature root-meansquared errors are between 6 K and 9 K. The L-MEB parameterisation is considered appropriate under local conditions even though it might possibly be further optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (R between 0.1-0.41). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for a considerable part of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles and the horizontal polarisation are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (R = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote-sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a longer term comparison.

show abstract

Section: F Schlenz Et Al: Analysis Of Smos Brightness Temperature Amentioning

confidence: 99%

Section: Airborne Datamentioning

confidence: 99%

Section: Smos Datamentioning

confidence: 99%

See 1 more Smart Citation

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Schlenz¹,

Dall'Amico²,

Mauser³

et al. 2012

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…In situ measurements for satellite validation are usually collected in field campaigns over extended areas and during short periods of time or over longer time spans at few selected measuring locations. In addition to other remotesensing datasets, the outputs of spatially distributed environmental process models can make a valuable contribution to the validation of remotely sensed soil moisture products (Crow et al, 2005;Albergel et al, 2010;Juglea et al, 2010;. These datasets can help to extend long-term validation activities to larger areas.…”

Section: F Schlenz Et Al: Analysis Of Smos Brightness Temperature Amentioning

confidence: 99%

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Schlenz¹,

Dall'Amico²,

Mauser³

et al. 2012

Preprint

View full text Add to dashboard Cite

Soil Moisture and Ocean Salinity (SMOS) L1c brightness temperature and L2 optical depth data are analysed with a coupled land surface (PROMET) and radiative transfer model (L-MEB) that are used as tool for the analysis and validation of passive microwave satellite observations. The coupled models are validated with ground and airborne measurements under contrasting soil moisture, vegetation and temperature conditions during the SMOS Validation Campaign in May and June 2010 in the SMOS test site Upper Danube Catchment in Southern Germany with good results. The brightness temperature root-mean-squared errors are between 6 K and 9 K and can partly be attributed to a known bias in the airborne L-band measurements. The L-MEB parameterization is considered appropriate under local conditions even though it might possibly further be optimised. SMOS L1c brightness temperature data are processed and analysed in the Upper Danube Catchment using the coupled models in 2011 and during the SMOS Validation Campaign 2010 together with airborne L-band brightness temperature data. Only low to fair correlations are found for this comparison (<i>R</i> < 0.5). SMOS L1c brightness temperature data do not show the expected seasonal behaviour and are positively biased. It is concluded that RFI is responsible for most of the observed problems in the SMOS data products in the Upper Danube Catchment. This is consistent with the observed dry bias in the SMOS L2 soil moisture products which can also be related to RFI. It is confirmed that the brightness temperature data from the lower SMOS look angles are less reliable. This information could be used to improve the brightness temperature data filtering before the soil moisture retrieval. SMOS L2 optical depth values have been compared to modelled data and are not considered a reliable source of information about vegetation due to missing seasonal behaviour and a very high mean value. A fairly strong correlation between SMOS L2 soil moisture and optical depth was found (<i>R</i> = 0.65) even though the two variables are considered independent in the study area. The value of coupled models as a tool for the analysis of passive microwave remote sensing data is demonstrated by extending this SMOS data analysis from a few days during a field campaign to a long term comparison

show abstract

“…Since high‐resolution TB measurements from field campaigns have finite temporal sampling, often less than 10 days [ dall'Amico et al , ; Magagi et al , ; Gherboudj et al , ], CMEM is used to simulate TB rather than using temporally limited observations. This is required for deriving a meaningful statistical relationship.…”

Section: Introductionmentioning

confidence: 99%

Potential of bias correction for downscaling passive microwave and soil moisture data

2015

View full text Add to dashboard Cite

Passive microwave satellites such as Soil Moisture and Ocean Salinity or Soil Moisture Active Passive observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disaggregate soil moisture to a scale more appropriate for hydrological applications. Temporal stability of soil moisture and TB was demonstrated at the Little Washita and Little River Experimental Watersheds using in situ observations and the Community Microwave Emissions Model. Decomposition of the mean square difference (MSD) between the watershed average soil moisture and TB showed that bias was a major contributor to differences between watershed average and local-scale soil moisture and TB, particularly at sites with high MSD. The mean RMSD between watershed average and local soil moisture was 0.04 m 3 m À3 and 0.06 m 3 m À3 at Little River and Little Washita, respectively. Following a simple bias correction the RMSD was reduced to 0.03 m 3 m À3 at both sites. Considering multiple incidence angles at both horizontal and vertical polarization, bias correction of watershed average TB V reduced the RMSD by approximately 75% and 45% and TB H RMSD by 68% and 36% for Little River and Little Washita, respectively, at all incidence angles. Therefore, at subsatellite grid scale, bias correction can be considered a viable technique for downscaling passive microwave observations and soil moisture retrievals.

show abstract

The SMOS Validation Campaign 2010 in the Upper Danube Catchment: A Data Set for Studies of Soil Moisture, Brightness Temperature, and Their Spatial Variability Over a Heterogeneous Land Surface

Cited by 8 publications

References 29 publications

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Analysis of SMOS brightness temperature and vegetation optical depth data with coupled land surface and radiative transfer models in Southern Germany

Potential of bias correction for downscaling passive microwave and soil moisture data

Contact Info

Product

Resources

About