Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment

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

doi:10.1109/tgrs.2011.2171694

Cited by 49 publications

(30 citation statements)

References 54 publications

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“…Muerth [54] evaluated the soil temperature modelling abilities of PROMET in the Upper Danube Catchment with measurements and remote-sensing data. According to Schlenz et al [55], modelled and measured SM data were compared extensively on different scales and locations in several large scale field campaigns around Munich between 2007 and 2012 and a detailed analysis of the SM modelling abilities and uncertainties of PROMET is given. The soil water model has been validated with good results, e.g., by Loew et al [14] and Schlenz et al [55].…”

Section: Land-surface Model Prometmentioning

confidence: 99%

“…According to Schlenz et al [55], modelled and measured SM data were compared extensively on different scales and locations in several large scale field campaigns around Munich between 2007 and 2012 and a detailed analysis of the SM modelling abilities and uncertainties of PROMET is given. 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: 99%

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Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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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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Section: Land-surface Model Prometmentioning

confidence: 99%

Section: Land-surface Model Prometmentioning

confidence: 99%

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Koch

Schlenz

Prasch

et al. 2016

Water

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“…The stations are equipped with horizontally installed probes in several depths. Details about the stations and the related uncertainties can be found in Schlenz et al (2012a). From the soil moisture stations the hourly 5 cm measurements from all available probes have been averaged per station and are being used as reference on the local scale in this study.…”

Section: Study Area and In Situ Datamentioning

confidence: 99%

“…EMIRAD has an antenna system consisting of two Potter horns, one pointed nadir and one 40 • aft and has a footprint size of about 1.5 km for the nadir antenna and 2 km for the 40 • looking antenna for an average flight altitude of 2 km above ground. The data processing is described in Schlenz et al (2012a) and involved RFI filtering with RFI flags that were provided with the data and a threshold filtering using the same threshold values as for the SMOS data (see Sect. 2.3).…”

Section: Airborne Datamentioning

confidence: 99%

“…Two publications have already validated the models and discussed the uncertainties related to this modelling approach thoroughly. While Schlenz et al (2012a) have focussed on the validation and uncertainties related to the land surface modelling from point to SMOS-like scale in the Upper Danube Catchment and brightness temperature modelling on the SMOS-like scale in the Vils test site, have analysed the radiative transfer modelling on the point scale in a test site roughly 100 km southwest of the Vils test site. Therefore, it is referred to these publications for a more thorough discussion of the related uncertainties.…”

Section: Coupled Land Surface and Radiative Transfer Modellingmentioning

confidence: 99%

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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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Estimating error cross‐correlations in soil moisture data sets using extended collocation analysis

et al. 2016

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Global soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based soil moisture data into water balance models or merging multisource soil moisture retrievals into a unified data set. However, this requires an appropriate parameterization of the error structures of the underlying data sets. While triple collocation (TC) analysis has been widely recognized as a powerful tool for estimating random error variances of coarse-resolution soil moisture data sets, the estimation of error cross covariances remains an unresolved challenge. Here we propose a method-referred to as extended collocation (EC) analysis-for estimating error cross-correlations by generalizing the TC method to an arbitrary number of data sets and relaxing the therein made assumption of zero error cross-correlation for certain data set combinations. A synthetic experiment shows that EC analysis is able to reliably recover true error cross-correlation levels. Applied to real soil moisture retrievals from Advanced Microwave Scanning Radiometer-EOS (AMSR-E) C-band and X-band observations together with advanced scatterometer (ASCAT) retrievals, modeled data from Global Land Data Assimilation System (GLDAS)-Noah and in situ measurements drawn from the International Soil Moisture Network, EC yields reasonable and strong nonzero error cross-correlations between the two AMSR-E products. Against expectation, nonzero error cross-correlations are also found between ASCAT and AMSR-E. We conclude that the proposed EC method represents an important step toward a fully parameterized error covariance matrix for coarse-resolution soil moisture data sets, which is vital for any rigorous data assimilation framework or data merging scheme.

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Uncertainty Assessment of the SMOS Validation in the Upper Danube Catchment

Cited by 49 publications

References 54 publications

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

Soil Moisture Retrieval Based on GPS Signal Strength Attenuation

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

Estimating error cross‐correlations in soil moisture data sets using extended collocation analysis

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