Decomposing Dual Scale Soil Surface Roughness for Microwave Remote Sensing Applications

Soil surface roughness, as investigated in this study, is decomposed in a dual scale process. Therefore, we investigated photogrammetrically acquired roughness information over different agricultural fields in the size of 6-22 m(2) and decomposed them into a dual scale process by using geostatistical techniques. For the characterization of soil surface roughness, we calculated two different roughness indices (the RMS height s and the autocorrelation length l) differing significantly for each scale. While we could relate the small scale roughness pattern clearly to the seedbed rows, the larger second scale pattern could be related to the appearance of wheel tracks of the tillage machine used. As a result, major progress was made in the understanding of the different scales in soil surface roughness characterization and its quantification possibilities

Analysis of the Effect of Soil Roughness in the Forward-Scattering Interference Pattern Using Second-Order Small Perturbation Method Simulations

Soil moisture (SM) is a key geophysical variable that can be estimated at regional scales using remote sensing techniques, by making use of the known relationship between soil reflectivity and the dielectric constant in the microwave regime. In this context, the exploitation of available illuminators of opportunity that currently emit large amounts of power at microwave frequencies (compared to typical synthetic aperture radar systems) is promising. Some published techniques estimate SM by analyzing the interference pattern (IP) between direct and reflected signal as measured by a single antenna (i.e., IP technique). In this letter, a new approach to simulate the IP is proposed, in which the soil roughness is modeled straightforwardly using the second-order small perturbation model. Results illustrate that the ``notch´´ in the VV-polarization IP (related to the Brewster angle) can only be directly observed for very low values of soil rms roughness (s < 0.5 cm). For typical values of soil roughness (s~ 1.2 cm), the notch disappears and only a minimum in the IP is observed near the Brewster angle.Fil: Franco, Mariano Andrés. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: More, Emanuel. Instituto de Altos Estudios Espaciales-mario Gulich; ArgentinaFil: Roitberg, Esteban Gabriel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Grings, Francisco Matias. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Piegari, Estefanía. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Douna, Vanesa Mariel. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; ArgentinaFil: Perna, Pablo Alejandro. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentin

Soil moisture retrieval through a merging of multi-temporal L-band SAR data and hydrologic modelling

The objective of the study is to investigate the potential of retrieving superficial soil moisture content (m(v)) from multi-temporal L-band synthetic aperture radar (SAR) data and hydrologic modelling. The study focuses on assessing the performances of an L-band SAR retrieval algorithm intended for agricultural areas and for watershed spatial scales (e. g. from 100 to 10 000 km(2)). The algorithm transforms temporal series of L-band SAR data into soil moisture contents by using a constrained minimization technique integrating a priori information on soil parameters. The rationale of the approach consists of exploiting soil moisture predictions, obtained at coarse spatial resolution ( e. g. 1530 km2) by point scale hydrologic models ( or by simplified estimators), as a priori information for the SAR retrieval algorithm that provides soil moisture maps at high spatial resolution (e. g. 0.01 km(2)). In the present form, the retrieval algorithm applies to cereal fields and has been assessed on simulated and experimental data. The latter were acquired by the airborne E-SAR system during the AgriSAR campaign carried out over the Demmin site (Northern Germany) in 2006. Results indicate that the retrieval algorithm always improves the a priori information on soil moisture content though the improvement may be marginal when the accuracy of prior mv estimates is better than 5%

Influence of surface roughness sample size for C-band SAR backscatter applications on agricultural soils

Soil surface roughness determines the backscatter coefficient observed by radar sensors. The objective of this letter was to determine the surface roughness sample size required in synthetic aperture radar applications and to provide some guidelines on roughness characterization in agricultural soils for these applications. With this aim, a data set consisting of ten ENVISAT/ASAR observations acquired coinciding with soil moisture and surface roughness surveys has been processed. The analysis consisted of: 1) assessing the accuracies of roughness parameters s and l depending on the number of 1-m-long profiles measured per field; 2) computing the correlation of field average roughness parameters with backscatter observations; and 3) evaluating the goodness of fit of three widely used backscatter models, i.e., integral equation model (IEM), geometrical optics model (GOM), and Oh model. The results obtained illustrate a different behavior of the two roughness parameters. A minimum of 10-15 profiles can be considered sufficient for an accurate determination of s, while 20 profiles might still be not enough for accurately estimating l. The correlation analysis revealed a clear sensitivity of backscatter to surface roughness. For sample sizes > 15 profiles, R values were as high as 0.6 for s and similar to 0.35 for l, while for smaller sample sizes R values dropped significantly. Similar results were obtained when applying the backscatter models, with enhanced model precision for larger sample sizes. However, IEM and GOM results were poorer than those obtained with the Oh model and more affected by lower sample sizes, probably due to larger uncertainly of l

A multi-site methodology for understanding dependencies in flood risk exposure in the UK

PhD ThesisRecent large scale flood events in the UK and the continued threat of a major North Sea surge have motivated a re-appraisal of how flood risk is modelled. A new generation of flood risk models are starting to consider the spatial and temporal dependencies in flood events. This is important for a wide range of risk based decision making, with one of its most significant applications being the understanding of insurance exposure. The aim of this thesis is to increase understanding of flood risk exposure in the UK and identify areas where existing modelling capabilities and data limitations contribute to large uncertainties in the estimation of risk. Illustrating a successful collaboration between academia and the insurance industry, a case study of one company’s exposure from static caravans is used to develop a methodology for flood risk assessment at multiple sites nested within a national framework. This novel nested approach allows for greater detail to be included at sites of interest resulting in increased understanding of the risk driving processes while retaining the large scale dependence structure. This is demonstrated at high risk locations on the Lincolnshire and North Wales coastline and inland on the Rivers Severn and Thames. The proposed methodology takes a flexible component based approach and has potential adaptations to different receptors and end users. A systems based model is used which explicitly considers all key components of risk. Extreme fluvial and coastal events are modelled statistically using the conditional dependence model of Heffernan and Tawn (2004). Coastal flood defences are essential for the protection of static caravan sites however their inclusion in existing risk models contributes significant uncertainties. The quality of data available on flood defence heights is reviewed and a methodology to incorporate spatial variations is proposed. The failure of flood defences is modelled using fragility curves and inundation modelling is used to route water on the floodplain. Finally the damage to the static caravans is modelled using depth-damage curves with reference to the impact of limited observed data on flood damage for caravans. One of the biggest challenges of considering dependencies across multiple scales within a systems model is matching the data requirements across each component. To address this problem this thesis investigates the relationship between skew surge and wave height to estimate the total inshore water level, and develops a UK specific method to transform daily mean flow to peak flow. The modular structure of the proposed methodology means different component models can be used to suit the available data; here the integration of both 1D and 2D floodplain inundation models is demonstrated.EPSR

Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust

Le comportement du signal radar TerraSAR-X en fonction des paramètres du sol (rugosité, humidité, structure) a été analysé sur des données 2009 et 2010. Les résultats montrent que la sensibilité du signal radar à l'humidité est plus importante pour des faibles incidences (25° en comparaison à 50°). Pour des fortes valeurs d'humidité, le signal TerraSAR-X est plus sensible à la rugosité du sol à forte incidence (50°). La forte résolution spatiale des données TerraSAR-X (1 m) permet de détecter la croûte de battance à l'échelle intra parcellaire. / Soils play a key role in shaping the environment and in risk assessment. We characterized the soils of bare agricultural plots using TerraSAR-X (9.5 GHz) data acquired in 2009 and 2010. We analyzed the behavior of the TerraSAR-X signal for two configurations, HH-25° and HH-50°, with regard to several soil conditions: moisture content, surface roughness, soil composition and soil-surface structure (slaking crust).The TerraSAR-X signal was more sensitive to soil moisture at a low (25°) incidence angle than at a high incidence angle (50°). For high soil moisture (N25%), the TerraSAR-X signal was more sensitive to soil roughness at a high incidence angle (50°) than at a low incidence angle (25°). The high spatial resolution of the TerraSAR-X data (1 m) enabled the soil composition and slaking crust to be analyzed at the within-plot scale based on the radar signal. The two loamy-soil categories that composed our training plots did not differ sufficiently in their percentages of sand and clay to be discriminated by the X-band radar signal.However, the spatial distribution of slaking crust could be detected when soil moisture variation is observed between soil crusted and soil without crust. Indeed, areas covered by slaking crust could have greater soil moisture and consequently a greater backscattering signal than soils without crust

Quantifying subglacial roughness and its link to glacial geomorphology and ice speed

The shape of subglacial bed topography, termed its roughness, is a recognised control on basal ice-flow. Although glaciologists have observed patterns of variations in ice speed over beds with different roughness values, the strength of this relationship has rarely been quantified, and measurements of roughness are based on just a few methods. Moreover, the shape of topography can vary in a number of ways, but how this influences roughness and the quantification of roughness is largely unknown. This project investigates methods of measuring roughness, and how such measurements might be related to spatial patterns in ice speed in both contemporary and palaeo-settings. Roughness of ice-sheet beds has traditionally been summarised using spectral analysis. The first part of this projected was aimed at reviewing this method. The influence of the number of data points was explored by developing a new technique for re-digitising radio-echo sounding records, which remain the most extensive source of bed data from Antarctica. This yielded measurements with a resolution (c.250 m) eight-times higher than those used in previous work, and allowed assessment of roughness over short window lengths. Significantly, subjective decisions about, for example, the choice of window length can lead to differing results using spectral analysis. The second part of this project was, therefore, to identify and evaluate 36 alternative methods of quantifying roughness, many of which had never before been used to analyse subglacial beds. The project looked at the broader approach to quantifying roughness, exploring the benefits of 2D versus 3D techniques for investigating subglacial data. The relationship between roughness and ice speed was tested using these alternative techniques in isolation, but also in a combination. Indeed, the use of generalised linear models (GLMs) allowed the strength of this relationship to be quantified for the first time, and permitted the roughness variables most related to ice speed to be identified. Testing the agreement between patterns in roughness in ice speed for the Siple Coast showed a pattern of increasing ice speed as roughness decreased. Modelling revealed a 98% fit between ice speed and roughness for the MacAyeal Ice Stream indicating that roughness is a strong control on basal ice flow. It was revealed that the measures of roughness most related to ice speed were those that summarised changes in the vertical height of the surface, rather than the shape or wavelength of the features. It was also found that the lateral margin of the MacAyeal Ice Stream corresponds with an area of high bed roughness. Analysis of formerly glaciated areas of Britain showed that the size and frequency of subglacial bedforms influence parameter results as do subtle changes in the orientation of 2D profiles across bedform fields. It was demonstrated how this might be used to identify subglacial features beneath contemporary ice sheets. In conclusion, alternative roughness parameters were found to be less restrictive and arguably more informative than spectral analysis, because they have the advantage of allowing differing characteristics of the topography to be measured. Conversely, this meant that no single parameter could provide a complete summary. Thus, a key conclusion of this work is that the most suitable approach to quantifying roughness is to use a suite of roughness parameters, designed to summarise a range of variables that are most relevant to the specific investigation

Coupled modelling of land surface microwave interactions using ENVISAT ASAR data

In the last decades microwave remote sensing has proven its capability to provide valuable information about the land surface. New sensor generations as e.g. ENVISAT ASAR are capable to provide frequent imagery with an high information content. To make use of these multiple imaging capabilities, sophisticated parameter inversion and assimilation strategies have to be applied. A profound understanding of the microwave interactions at the land surface is therefore essential. The objective of the presented work is the analysis and quantitative description of the backscattering processes of vegetated areas by means of microwave backscattering models. The effect of changing imaging geometries is investigated and models for the description of bare soil and vegetation backscattering are developed. Spatially distributed model parameterisation is realized by synergistic coupling of the microwave scattering models with a physically based land surface process model. This enables the simulation of realistic SAR images, based on bioand geophysical parameters. The adequate preprocessing of the datasets is crucial for quantitative image analysis. A stringent preprocessing and sophisticated terrain geocoding and correction procedure is therefore suggested. It corrects the geometric and radiometric distortions of the image products and is taken as the basis for further analysis steps. A problem in recently available microwave backscattering models is the inadequate parameterisation of the surface roughness. It is shown, that the use of classical roughness descriptors, as the rms height and autocorrelation length, will lead to ambiguous model parameterisations. A new two parameter bare soil backscattering model is therefore recommended to overcome this drawback. It is derived from theoretical electromagnetic model simulations. The new bare soil surface scattering model allows for the accurate description of the bare soil backscattering coefficients. A new surface roughness parameter is introduced in this context, capable to describe the surface roughness components, affecting the backscattering coefficient. It is shown, that this parameter can be directly related to the intrinsic fractal properties of the surface. Spatially distributed information about the surface roughness is needed to derive land surface parameters from SAR imagery. An algorithm for the derivation of the new surface roughness parameter is therefore suggested. It is shown, that it can be derived directly from multitemporal SAR imagery. Starting from that point, the bare soil backscattering model is used to assess the vegetation influence on the signal. By comparison of the residuals between measured backscattering coefficients and those predicted by the bare soil backscattering model, the vegetation influence on the signal can be quantified. Significant difference between cereals (wheat and triticale) and maize is observed in this context. It is shown, that the vegetation influence on the signal can be directly derived from alternating polarisation data for cereal fields. It is dependant on plant biophysical variables as vegetation biomass and water content. The backscattering behaviour of a maize stand is significantly different from that of other cereals, due to its completely different density and shape of the plants. A dihedral corner reflection between the soil and the stalk is identified as the major source of backscattering from the vegetation. A semiempirical maize backscattering model is suggested to quantify the influences of the canopy over the vegetation period. Thus, the different scattering contributions of the soil and vegetation components are successfully separated. The combination of the bare soil and vegetation backscattering models allows for the accurate prediction of the backscattering coefficient for a wide range of surface conditions and variable incidence angles. To enable the spatially distributed simulation of the SAR backscattering coefficient, an interface to a process oriented land surface model is established, which provides the necessary input variables for the backscattering model. Using this synergistic, coupled modelling approach, a realistic simulation of SAR images becomes possible based on land surface model output variables. It is shown, that this coupled modelling approach leads to promising and accurate estimates of the backscattering coefficients. The remaining residuals between simulated and measured backscatter values are analysed to identify the sources of uncertainty in the model. A detailed field based analysis of the simulation results revealed that imprecise soil moisture predictions by the land surface model are a major source of uncertainty, which can be related to imprecise soil texture distribution and soil hydrological properties. The sensitivity of the backscattering coefficient to the soil moisture content of the upper soil layer can be used to generate soil moisture maps from SAR imagery. An algorithm for the inversion of soil moisture from the upper soil layer is suggested and validated. It makes use of initial soil moisture values, provided by the land surface process model. Soil moisture values are inverted by means of the coupled land surface backscattering model. The retrieved soil moisture results have an RMSE of 3.5 Vol %, which is comparable to the measurement accuracy of the reference field data. The developed models allow for the accurate prediction of the SAR backscattering coefficient. The various soil and vegetation scattering contributions can be separated. The direct interface to a physically based land surface process model allows for the spatially distributed modelling of the backscattering coefficient and the direct assimilation of remote sensing data into a land surface process model. The developed models allow for the derivation of static and dynamic landsurface parameters, as e.g. surface roughness, soil texture, soil moisture and biomass from remote sensing data and their assimilation in process models. They are therefore reliable tools, which can be used for sophisticated practice oriented problem solutions in manifold manner in the earth and environmental sciences