Global soil moisture products retrieved from various remote sensing sensors are becoming readily available with a nearly daily temporal resolution. Active and passive microwave sensors are generally ...considered as the best technologies for retrieving soil moisture from space. The Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E) on-board the Aqua satellite and the Advanced SCATterometer (ASCAT) on-board the MetOp (Meteorological Operational) satellite are among the sensors most widely used for soil moisture retrieval in the last years. However, due to differences in the spatial resolution, observation depths and measurement uncertainties, validation of satellite data with in situ observations and/or modelled data is not straightforward. In this study, a comprehensive assessment of the reliability of soil moisture estimations from the ASCAT and AMSR-E sensors is carried out by using observed and modelled soil moisture data over 17 sites located in 4 countries across Europe (Italy, Spain, France and Luxembourg). As regards satellite data, products generated by implementing three different algorithms with AMSR-E data are considered: (i) the Land Parameter Retrieval Model, LPRM, (ii) the standard NASA (National Aeronautics and Space Administration) algorithm, and (iii) the Polarization Ratio Index, PRI. For ASCAT the Vienna University of Technology, TUWIEN, change detection algorithm is employed. An exponential filter is applied to approach root-zone soil moisture. Moreover, two different scaling strategies, based respectively on linear regression correction and Cumulative Density Function (CDF) matching, are employed to remove systematic differences between satellite and site-specific soil moisture data. Results are shown in terms of both relative soil moisture values (i.e., between 0 and 1) and anomalies from the climatological expectation.
Among the three soil moisture products derived from AMSR-E sensor data, for most sites the highest correlation with observed and modelled data is found using the LPRM algorithm. Considering relative soil moisture values for an ~
5
cm soil layer, the TUWIEN ASCAT product outperforms AMSR-E over all sites in France and central Italy while similar results are obtained in all other regions. Specifically, the average correlation coefficient with observed (modelled) data equals to 0.71 (0.74) and 0.62 (0.72) for ASCAT and AMSR-E-LPRM, respectively. Correlation values increase up to 0.81 (0.81) and 0.69 (0.77) for the two satellite products when exponential filtering and CDF matching approaches are applied. On the other hand, considering the anomalies, correlation values decrease but, more significantly, in this case ASCAT outperforms all the other products for all sites except the Spanish ones. Overall, the reliability of all the satellite soil moisture products was found to decrease with increasing vegetation density and to be in good accordance with previous studies. The results provide an overview of the ASCAT and AMSR-E reliability and robustness over different regions in Europe, thereby highlighting advantages and shortcomings for the effective use of these data sets for operational applications such as flood forecasting and numerical weather prediction.
► Validation of four different soil moisture products from either ASCAT or AMSR-E. ► In-situ soil moisture data for 17 sites across Europe are used as benchmark. ► The ASCAT and AMSRE-LPRM products provide a good agreement with in-situ observations. ► The reliability of the products decrease with increasing vegetation density. ► The integration of modeled and observed data is a robust validation strategy.
This paper aims at contributing to the elaboration of new concepts for an efficient and standardized Synthetic Aperture Radar (SAR) based monitoring of floods. Algorithms that enable an automatic ...delineation of flooded areas are an essential component of any SAR-based monitoring service but are to date quasi non-existent. Here we propose a hybrid methodology, which combines radiometric thresholding and region growing as an approach enabling the automatic, objective and reliable flood extent extraction from SAR images. The method relies on the calibration of a statistical distribution of ‘open water’ backscatter values inferred from SAR images of floods. A radiometric thresholding provides the seed region for a subsequent region growing process. Change detection is included as an additional step that limits over-detection of inundated areas. Two variants of the proposed flood extraction algorithm (with and without integration of reference images) are tested against four state-of-the-art benchmark methods. The methods are evaluated through two case studies: the July 2007 flood of the Severn river (UK) and the February 1997 flood of the Red river (US). Our trial cases show that considering a reference pre- or post-flood image gives the same performance as optimized manual approaches. This encouraging result indicates that the proposed method may indeed outperform all manual approaches if no training data are available and the parameters associated with these methods are determined in a non-optimal way. The results further demonstrate the algorithm’s potential for accurately processing data from different SAR sensors.
Satellite-based active microwave sensors not only provide synoptic overviews of flooded areas, but also offer an effective way to estimate spatially distributed river water levels. If rapidly ...produced and processed, these data can be used for updating hydraulic models in near real-time. The usefulness of such approaches with real event data sets provided by currently existing sensors has yet to be demonstrated. In this case study, a Particle Filter-based assimilation scheme is used to integrate ERS-2 SAR and ENVISAT ASAR-derived water level data into a one-dimensional (1-D) hydraulic model of the Alzette River. Two variants of the Particle Filter assimilation scheme are proposed with a global and local particle weighting procedure. The first option finds the best water stage line across all cross sections, while the second option finds the best solution at individual cross sections. The variant that is to be preferred depends on the level of confidence that is attributed to the observations or to the model. The results show that the Particle Filter-based assimilation of remote sensing-derived water elevation data provides a significant reduction in the uncertainty at the analysis step. Moreover, it is shown that the periodical updating of hydraulic models through the proposed assimilation scheme leads to an improvement of model predictions over several time steps. However, the performance of the assimilation depends on the skill of the hydraulic model and the quality of the observation data.
Digital elevation models (DEMs) are at the core of most environmental process modelling and disaster management. In flood inundation modelling, surface elevation constitutes one of the most important ...model boundary conditions. With the availability of high-precision DEMs (e.g. LiDAR) and globally available DEMs (e.g. SRTM InSAR) a big step seems to have been taken in terms of hydraulic modelling application or hydraulic information retrieval from such DEMs, with high potential in particular for ungauged basins. Comparative studies exist that report on both the validation of different remotely sensed elevation sources and their use for both hydrologic and hydraulic studies. To contribute to the existing literature on DEMs and hydraulic information, this study aims at comparing water stages derived from LiDAR, topographic contours and SRTM. A flood inundation model calibrated with distributed ground-surveyed high water marks is used to evaluate the remotely sensed water stages. The results show that, as expected, LiDAR derived water stages exhibit the lowest RMSE (0.35 m), followed by the contour DEM (0.7 m). A relatively good performance of the SRTM (1.07 m), which is possibly linked to the low-lying floodplain, suggests that the SRTM is a valuable source for initial vital flood information extraction in large, homogeneous floodplains. Subsequent 3D flood mapping from remotely sensed water stages confirms this but also indicates that flood mapping with low-resolution, low-precision surface elevation data is hardly possible on the small scale, as the accuracy of the resulting map depends too much on DEM uncertainties and errors both in the horizontal and vertical directions.
Soil moisture (SM) datasets at high spatial resolutions are beneficial for a wide range of applications, such as monitoring and prediction of hydrological extremes, numerical weather prediction, and ...precision agriculture. For large scale applications in particular, remotely sensed SM has advantages over in situ data because it provides gridded estimates and because it is less labour-intensive. However, until present, active microwave SM data have not been presented at their native spatial resolution, since the quality of these data is limited by speckle.
We explored the potential and limits of high spatial resolution of active microwave SM observations. We used a Sentinel-1 C-band SAR SM dataset at six spatial resolutions ranging from 20 × 20 to 120 × 120 m2. This was compared to a closely spaced (20 m) in situ dataset collected on a non-irrigated agricultural field (±2.5 ha) in the Southeast of Luxembourg.
A comparison of the field and satellite datasets demonstrated how Sentinel-1 data with a high spatial resolution can be used to quantify temporal within-field SM variability. SM was accurately estimated at spatial resolutions of 60 × 60 m2 and coarser, where the temporal correlation was found to be 0.67 and sub-field variations in SM were still detected. Spatial correlation was limited by the absence of SM variability within the field.
These results indicate that high spatial resolution SM estimates from Sentinel-1 data can be valuable for monitoring temporal SM variations within agricultural fields.
•Limits in spatial resolution were tested in Sentinel-1 soil moisture retrievals.•Spatiotemporal variability was evaluated using closely spaced field reference data.•The optimal spatial resolution is 60 × 60 m2 with a temporal correlation of 0.67.•Temporal soil moisture variability could be retrieved accurately within the field.
Exploitation of river inundation satellite images, particularly for operational applications, is mostly restricted to flood extent mapping. However, there lies significant potential for improvement ...in a 3-D characterization of floods (i.e., flood depth maps) and an integration of the remote-sensing-derived (RSD) characteristics in hydraulic models. This paper aims at developing synthetic aperture radar (SAR) image analysis methods that go beyond flood extent mapping to assess the potential of these images in the spatiotemporal characterization of flood events. To meet this aim, two research issues were addressed. The first issue relates to water level estimation. The proposed method, which is an adaptation to SAR images of the method developed for water level estimation using flood aerial photographs, is composed of three steps: (1) extraction of flood extent limits that are relevant for water level estimation; (2) water level estimation by merging relevant limits with a Digital Elevation Model; and (3) constraining of the water level estimates using hydraulic coherence concepts. Applied to an ENVISAT image of an Alzette River flood (2003, Grand Duchy of Luxembourg), this provides plusmn54-cm average vertical uncertainty water levels that were validated using a sample of ground surveyed high water marks. The second issue aims at better constraining hydraulic models using these RSD water levels. To meet this aim, a "traditional" calibration using recorded hydrographs is completed via comparison between simulated and RSD water levels. This integration of the RSD characteristics proves to better constrain the model (i.e., the number of parameter sets providing acceptable results with respect to observations has been reduced). Furthermore, simulations of a flood event of a different return period (2007) using the model calibrated for the 2003 flood event shows the reliability of the latter for flood forecasting.
This paper presents a remote-sensing-based steady-state flood inundation model to improve preventive flood-management strategies and flood disaster management. The Regression and Elevation-based ...Flood Information eXtraction (REFIX) model is based on regression analysis and uses a remotely sensed flood extent and a high-resolution floodplain digital elevation model to compute flood depths for a given flood event. The root mean squared error of the REFIX, compared to ground-surveyed high water marks, is 18 cm for the January 2003 flood event on the River Alzette floodplain (G.D. of Luxembourg), on which the model is developed. Applying the same methodology on a reach of the River Mosel, France, shows that for some more complex river configurations (in this case, a meandering river reach that contains a number of hydraulic structures), piecewise regression is required to yield more accurate flood water-line estimations. A comparison with a simulation from the Hydrologic Engineering Centers River Analysis System hydraulic flood model, calibrated on the same events, shows that, for both events, the REFIX model approximates the water line reliably
Fine sediments represent an important vector of pollutant diffusion in rivers. When deposited in floodplains and riverbeds, they can be responsible for soil pollution. In this context, this paper ...proposes a modelling exercise aimed at predicting transport and diffusion of fine sediments and dissolved pollutants. The model is based upon the Telemac hydro-informatic system (dynamical coupling Telemac-2D-Sysiphe). As empirical and semiempirical parameters need to be calibrated for such a modelling exercise, a sensitivity analysis is proposed. An innovative point in this study is the assessment of the usefulness of dissolved trace metal contamination information for model calibration. Moreover, for supporting the modelling exercise, an extensive database was set up during two flood events. It includes water surface elevation records, discharge measurements and geochemistry data such as time series of dissolved/particulate contaminants and suspended-sediment concentrations. The most sensitive parameters were found to be the hydraulic friction coefficients and the sediment particle settling velocity in water. It was also found that model calibration did not benefit from dissolved trace metal contamination information. Using the two monitored hydrological events as calibration and validation, it was found that the model is able to satisfyingly predict suspended sediment and dissolve pollutant transport in the river channel. In addition, a qualitative comparison between simulated sediment deposition in the floodplain and a soil contamination map shows that the preferential zones for deposition identified by the model are realistic.
Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art back\-scatter models is not fully operational at present, mainly due to difficulties involved in the ...parameterisation of soil surface roughness. Recently, increasing interest has been drawn to the use of calibrated or effective roughness parameters, as they circumvent issues known to the parameterisation of field-measured roughness. This paper analyses effective roughness parameters derived from C- and L-band SAR observations over a large number of agricultural seedbed sites in Europe. It shows that param\-eters may largely differ between SAR acquisitions, as they are related to the observed backscatter coefficients and variations in the local incidence angle. Therefore, a statistical model is developed that allows for estimating effective roughness parameters from microwave backscatter observations. Subsequently, these parameters can be propagated through the Integral Equation Model (IEM) for soil moisture retrieval. It is shown that fairly accurate soil moisture results are obtained both at C- and L-band, with an RMSE ranging between 4 vol% and 6.5 vol%.
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