A study of vegetation cover effects on ERS scatterometer data

Wagner, Wolfgang; Lemoine, Guido; Borgeaud, M.; Rott, Helmut

doi:10.1109/36.752212

Cited by 231 publications

(171 citation statements)

References 11 publications

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“…The Essential Climate Variable Soil Moisture (ECV SM) product (Liu et al, 2011;Liu et al, 2012;Wagner et al, 2012) (Wagner et al, 1999a;Wagner et al, 1999b;Naeimi et al, 2009) is used to convert backscatter measurements to soil moisture values and the Land Parameter Retrieval Model (LPRM) developed jointly by VU University Amsterdam and the NASA Goddard Space Flight Center (Owe et al, 2001;De Jeu and Owe, 2003;Owe et al, 2008) is used to convert brightness temperatures to soil moisture respectively. The ECV SM product has been validated across different regions using in-situ, model and SAR-derived soil moisture datasets in previous studies (e.g.…”

Section: Ecv Soil Moisture Observationsmentioning

confidence: 99%

“…Consequently, the ASAR WS data were aggregated to 1km spatial resolution, supporting the comparison with the ECV product and also improving the radiometric accuracy of the satellite data. There are different approaches to soil moisture estimation using SAR data (Barrett et al, 2009;Petropoulos et al, 2015) and in this study, soil moisture values were retrieved from the ASAR WS acquisitions by applying the TU Wien change detection algorithm (Wagner et al, 1999a;Wagner et al, 1999b). This technique was originally developed for ERS scatterometer and Advanced Scatterometer (ASCAT) data but has been successfully adapted to both ASAR WS and GM data (e.g.…”

Section: Asar Soil Moisture Observationsmentioning

confidence: 99%

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Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Barrett¹,

Pratola²,

Gruber³

et al. 2016

Satellite Soil Moisture Retrieval

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The availability of satellite-derived global surface soil moisture products during the last decade has opened up great opportunities to incorporate these observations into applications in hydrology, meteorology and climatic modelling. This study evaluates a new global soil moisture product developed under the framework of the European Space Agency (ESA) Climate Change Initiative (CCI), using finer spatial resolution Synthetic Aperture Radar (SAR) and ground-based measurements of soil moisture. The analysis is carried out over selected in-situ networks over Ireland, Spain, and Finland with the aim of assessing the temporal representativeness of the coarse scale CCI Essential Climate Variable (ECV) soil moisture product (ECV SM) in these different areas. A good agreement (correlation coefficient (R) values between 0.53 and 0.92) was observed between the three soil moisture datasets for the Irish and Spanish sites while a reasonable agreement (R values between 0.41 and 0.52) was observed between the SAR and ECV SM soil moisture datasets at the Finnish sites. Overall, the two different satellite derived products captured the soil moisture temporal variations well and were in good agreement with each other, highlighting the confidence of using the coarse scale ECV SM product to track soil moisture variability in time.

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Section: Ecv Soil Moisture Observationsmentioning

confidence: 99%

Section: Asar Soil Moisture Observationsmentioning

confidence: 99%

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Barrett¹,

Pratola²,

Gruber³

et al. 2016

Satellite Soil Moisture Retrieval

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“…The effects of plant growth and decay are taken into account through the application of varying seasonally σ 0 dry and σ 0 wet values as proposed by Wagner et al (1999b). This method exploits the multi-incidence capabilities of the ERS scatterometer to describe the effect of enhanced volume scattering in the vegetation layer and the corresponding decrease of the ground scattering contribution.…”

Section: Satellite Derived Soil Moisture Datamentioning

confidence: 99%

“…The retrieved absolute soil moisture values thus depend on the assumptions about which soil moisture states are represented by the two backscatter reference values. The standard assumption for the thin remotely sensed surface layer (m s ) is that minimum backscatter represents a completely dry soil and maximum backscatter water saturated soil (Wagner et al, 1999b). For the soil profile (SWI) minimum backscatter is in general related to a soil with water content at wilting point and maximum backscatter to a soil with a soil moisture content halfway between field capacity and total water capacity (Wagner et al, 1999c).…”

Section: Uncertainties Associated With Remote Sensingmentioning

confidence: 99%

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Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Vischel

Pegram²,

Sinclair³

et al. 2008

Hydrol. Earth Syst. Sci.

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Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km 2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R 2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.

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Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

et al. 2014

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[1] We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km 2 ), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally.Citation: Sutanudjaja, E. H., L. P. H. van Beek, S. M. de Jong, F. C. van Geer, and M. F. P. Bierkens (2014), Calibrating a largeextent high-resolution coupled groundwater-land surface model using soil moisture and discharge data, Water Resour. Res., 50,[687][688][689][690][691][692][693][694][695][696][697][698][699][700][701][702][703][704][705]

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A study of vegetation cover effects on ERS scatterometer data

Cited by 231 publications

References 11 publications

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Intercomparison of Soil Moisture Retrievals From In Situ, ASAR, and ECV SM Data Sets Over Different European Sites

Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Calibrating a large-extent high-resolution coupled groundwater-land surface model using soil moisture and discharge data

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