Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Hahn, Sebastian; Wagner, Wolfgang; Steele‐Dunne, Susan; Vreugdenhil, Mariëtte; Melzer, Thomas

doi:10.1109/tgrs.2020.3041340

Cited by 16 publications

(8 citation statements)

References 59 publications

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“…The H SAF ASCAT SSM retrieval scheme is based on the TU Wien change detection model originally developed for the ERS scatterometer [38], and later adapted to ASCAT [2], [39], [40]. This backscatter model is formulated in the decibel domain, assuming that a change in soil moisture leads to a change in backscatter…”

Section: Theorymentioning

confidence: 99%

“…To obtain estimates of θ, the model is calibrated for each land surface pixel by extracting minimum and maximum backscatter values from multi-year backscatter time series standardized to a reference angle of 40 • and corrected for seasonal vegetation cover effects [40]- [42]. The so-derived values of σ 0 min and σ 0 max do not only vary from pixel to pixel, but also over the seasons and are assumed to represent completely dry and saturated soil conditions, respectively.…”

Section: Theorymentioning

confidence: 99%

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Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Wagner,

Lindorfer,

Hahn

et al. 2023

Preprint

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<p>Microwave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.</p>

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Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Wagner,

Lindorfer,

Hahn

et al. 2023

Preprint

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“…The satellite soil moisture data used in this study is the soil water index (SWI) derived from an experimental version of the upcoming disaggregated Metop ASCAT surface Soil Moisture v2 product (H28) provided by the EUMET-SAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF). The original ASCAT surface soil moisture dataset at 12.5 km (before disaggregation) is based on a new parameterization for the vegetation correction (Hahn et al, 2021), which has shown improved performance over Austria (Pfeil et al, 2018). The disaggregation process consists of a directional resampling method utilizing the connection between regional-(12.5 km) and local-scale (0.5 km) Sentinel-1 backscatter observations describing temporally stable soil moisture patterns also reflected in the radar backscatter measurements (Wagner et al, 2008).…”

Section: Ascat Soil Moisturementioning

confidence: 99%

The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

Tong

Parajka

Széles

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations.

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“…The upper limit ("wet reference") reflects the highest value of backscattering coefficient observed over time at a certain location. They are given by [15]:…”

Section: Tu Wien Soil Moisture Retrievalmentioning

confidence: 99%

Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products

et al. 2021

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The TUWien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the MetOP series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.

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Improving ASCAT Soil Moisture Retrievals With an Enhanced Spatially Variable Vegetation Parameterization

Cited by 16 publications

References 59 publications

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products

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