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

1D-Var multilayer assimilation of X-band SAR data into a detailed snowpack model

International audienceThe structure and physical properties of a snow-pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver-gences and accumulated errors, to a limited spatial resolu-tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im-prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame-ters, like its depth, snow grain size and density. SAR acqui-sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv-ity of a snowpack from a set of physical descriptors, in or-der to constrain a physical snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the snowpack evolution model Crocus. The physical properties of a snowpack, such as snow density and optical diameter of each layer, are simu-lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow-pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi-tivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa-tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated snowpack. The algorithm then re-initializes Crocus with the modified snowpack physical parameters, allowing it to continue the simulation of snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us-ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentière glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of snowpack simulated by Crocus

Polarimetric Synthetic Aperture Radar (SAR) Application for Geological Mapping and Resource Exploration in the Canadian Arctic

The role of remote sensing in geological mapping has been rapidly growing by providing predictive maps in advance of field surveys. Remote predictive maps with broad spatial coverage have been produced for northern Canada and the Canadian Arctic which are typically very difficult to access. Multi and hyperspectral airborne and spaceborne sensors are widely used for geological mapping as spectral characteristics are able to constrain the minerals and rocks that are present in a target region. Rock surfaces in the Canadian Arctic are altered by extensive glacial activity and freeze-thaw weathering, and form different surface roughnesses depending on rock type. Different physical surface properties, such as surface roughness and soil moisture, can be revealed by distinct radar backscattering signatures at different polarizations. This thesis aims to provide a multidisciplinary approach for remote predictive mapping that integrates the lithological and physical surface properties of target rocks. This work investigates the physical surface properties of geological units in the Tunnunik and Haughton impact structures in the Canadian Arctic characterized by polarimetric synthetic aperture radar (SAR). It relates the radar scattering mechanisms of target surfaces to their lithological compositions from multispectral analysis for remote predictive geological mapping in the Canadian Arctic. This work quantitatively estimates the surface roughness relative to the transmitted radar wavelength and volumetric soil moisture by radar scattering model inversion. The SAR polarization signatures of different geological units were also characterized, which showed a significant correlation with their surface roughness. This work presents a modified radar scattering model for weathered rock surfaces. More broadly, it presents an integrative remote predictive mapping algorithm by combining multispectral and polarimetric SAR parameters

Surface Soil Moisture Retrievals from Remote Sensing:Current Status, Products & Future Trends

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth’s land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today’s world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space

Snow monitoring using microwave radars

Remote sensing has proven its usefulness in various applications. For mapping, land-use classification and forest monitoring optical satellite and airborne images are used operationally. However, this is not the case with snow monitoring. Currently only ground-based in situ and weather measurements are used operationally for snow monitoring in Finland. Ground measurements are conducted once a month on special snow courses. These measurements are used to update the hydrological model that simulates the runoff. Recently optical images (NOAA AVHRR) have been tested to derive a map of the areal extent of snow. However, during the snow melt, which is the most important period for hydrology, there are few cloudless days and, therefore, the availability of optical data is limited. That is why microwave remote sensing can play an important role in snow melt monitoring due to its unique capability to provide data independent of sun light and in almost all weather conditions. The synthetic aperture radar (SAR) data may make a significant contribution to satellite observations of snow by bridging the period between the on-set and end of snow melt. Microwave radiometers can be used to retrieve the snow water equivalent of dry snow, but they cannot be used to distinguish wet snow and wet ground during the melting period. The results of the thesis indicate that, even in the presence of forest canopies, (1) wet snow can be distinguished from dry snow and bare ground, (2) snow-free areas can be identified, (3) seasonal evolution of snow cover can be monitored and (4) snow-melt maps showing the fraction of snow-free ground (wet ground) and snow (wet snow) can be derived from SAR images.reviewe

Advances in SAR-Based Soil Moisture Retrieval Techniques, Applications, and Challenges

Synthetic Aperture Radar (SAR) remote sensing has emerged as a promising tool for soil moisture monitoring, due to its ability to penetrate clouds and measure the moisture content of the soil surface. In recent years, a wide range of SAR-based soil moisture retrieval techniques have been developed, from empirical regression models to physically-based models that incorporate surface roughness and vegetation effects. This review paper provides an overview of the latest advances in SAR-based soil moisture retrieval, covering the different techniques and algorithms that have been proposed, as well as the applications and challenges associated with the use of SAR data for soil moisture monitoring and management. The paper also discusses the limitations and uncertainties of SAR-based soil moisture retrieval, and provides recommendations for future research directions. Overall, this paper aims to provide a comprehensive and up-to-date overview of the state-of-the-art in SAR-based soil moisture retrieval, and to highlight the potential benefits and limitations of this approach for soil moisture monitoring and management

Summaries of the Fifth Annual JPL Airborne Earth Science Workshop. Volume 3: AIRSAR Workshop

This publication is the third containing summaries for the Fifth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on January 23-26, 1995. The main workshop is divided into three smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on January 23-24. The summaries for this workshop appear in Volume 1; (2) The Airborne synthetic Aperture Radar (AIRSAR) workshop, on January 25-26. The summaries for this workshop appear in this volume; and (3) The Thermal Infrared Multispectral Scanner (TIMS) workshop, on January 26. The summaries for this workshop appear in Volume 2

Effective roughness modelling as a tool for soil moisture retrieval from C-and L-band SAR

Soil moisture retrieval from Synthetic Aperture Radar (SAR) using state-of-the-art backscatter 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 parameters 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%.The research presented in this paper is funded by the Belgian Science Policy Office and the National Research Fund of Luxembourg in the frame of the Stereo II programme –project SR/00/100, and by the Spanish Government’s National Research, Development and Innovation Plan, project CGL2007- 63453/HID

Dynamic Programming and Bayesian Inference

Dynamic programming and Bayesian inference have been both intensively and extensively developed during recent years. Because of these developments, interest in dynamic programming and Bayesian inference and their applications has greatly increased at all mathematical levels. The purpose of this book is to provide some applications of Bayesian optimization and dynamic programming