First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment

Dall'Amico, J.; Schlenz, F.; Loew, Alexander; Mauser, Wolfram

doi:10.1109/tgrs.2011.2171496

Cited by 88 publications

(44 citation statements)

References 30 publications

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“…They find mean correlation coefficients of 0.25-0.3 and a dry bias in the order of 0.23 m 3 m −3 -0.267 m 3 m −3 for the comparison of SMOS data with in situ data in 2010. For the period April to October 2011 these figures improve considerably with a correlation coefficient of 0.52 and a dry bias of 0.15 m 3 m −3 for the same comparisons (dall'Amico, 2012). For comparisons between modelled soil moisture and SMOS soil moisture, the mean correlation coefficient in the Vils test site for 2011 is 0.54, the mean bias 0.13 m 3 m 3 .…”

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

confidence: 65%

“…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;dall'Amico et al, 2012a). 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%

“…Generally the SMOS performance in Central Europe seems to be degraded compared to other regions of the world. For the Vils test site in the Upper Danube Catchment in southern Germany, that is also the area of interest in this study, Albergel et al (2012), dall 'Amico et al (2012a) and dall'Amico (2012) have compared SMOS L2 soil moisture products to in situ and modelled reference data. They find mean correlation coefficients of 0.25-0.3 and a dry bias in the order of 0.23 m 3 m −3 -0.267 m 3 m −3 for the comparison of SMOS data with in situ data in 2010.…”

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

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.

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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.

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

confidence: 65%

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

confidence: 99%

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

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

“…The soil water model has been validated with good results, e.g., by Loew et al [14] and Schlenz et al [55]. The model results were inter alia used to validate passive microwave remote sensing recordings from the SMOS mission [28,56,57].…”

Section: Land-surface Model Prometmentioning

confidence: 96%

“…As reference, in situ time domain reflectometry (TDR) or frequency domain (FD) probes and gravimetric measurements are used as well as model approaches [28][29][30][31][32][33][34]. A comprehensive overview on in situ SM measurements from many different networks around the globe can be obtained in the International Soil Moisture Network (ISMN) [35].…”

Section: Introductionmentioning

confidence: 99%

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

Self Cite

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Soil moisture (SM) is a highly relevant variable for agriculture, the emergence of floods and a key variable in the global energy and water cycle. In the last years, several satellite missions have been launched especially to derive large-scale products of the SM dynamics on the Earth. However, in situ validation data are often scarce. We developed a new method to retrieve SM of bare soil from measurements of low-cost GPS (Global Positioning System) sensors that receive the freely available GPS L1-band signals. The experimental setup of three GPS sensors was installed at a bare soil field at the German Weather Service (DWD) in Munich for almost 1.5 years. Two GPS antennas were installed within the soil column at a depth of 10 cm and one above the soil. SM was successfully retrieved based on GPS signal strength losses through the integral soil volume. The results show high agreement with measured and modelled SM validation data. Due to its non-destructive, cheap and low power setup, GPS sensor networks could also be used for potential applications in remote areas, aiming to serve as satellite validation data and to support the fields of agriculture, water supply, flood forecasting and climate change.

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The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

Wanders

Bierkens

Jong

et al. 2014

Water Resources Research

187

133

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Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km 2 , calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.

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First Results of SMOS Soil Moisture Validation in the Upper Danube Catchment

Cited by 88 publications

References 30 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

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

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