Broad spectral, interdisciplinary investigation of the electromagnetic properties of sea ice

Journal ArticleThis paper highlights the interrelationship of research completed by a team of investigators and presented in the several individual papers comprising this Special Section on the Office of Naval Research (ONR), Arlington, VA, Sponsored Sea Ice Electromagnetics Accelerated Research Initiative (ARI)

A broad spectral, interdisciplinary investigation of the electromagnetic properties of sea ice

This paper highlights the interrelationship of research completed by a team of investigators and presented in the several individual papers comprising this Special Section on the Office of Naval Research (ONR), Arlington, VA, Sponsored Sea Ice Electromagnetics Accelerated Research Initiative (ARI). The objectives of the initiative were the following: 1) understand the mechanisms and processes that link the morphological and physical properties of sea ice to its electromagnetic (EM) characteristics; 2) develop and verify predictive models for the interaction of visible, infrared, and microwave radiation with sea ice; 3) develop and verify inverse scattering techniques applicable to problems involving the interaction of EM radiation with sea ice. Guiding principles for the program were that all EM data be taken with concurrent physical property data (salinity, density, roughness, etc.) and that broad spectral data be acquired in as nearly a simultaneous fashion as possible. Over 30 investigators participated in laboratory, field, and modeling studies that spanned the EM spectrum from radio to ultraviolet wavelengths. An interdisciplinary approach that brought together sea ice physicists, remote-sensing experts tin EM measurements), and forward and inverse modelers (primarily mathematicians and EM theorists) was a hallmark of the program. Along with describing results from experiments and modeling efforts, possible paradigms for using broad spectral data in developing algorithms for analyzing remote-sensing data in terms of ice concentration, age, type, and possibly thickness are briefly discussed

Laboratory Studies of the Electromagnetic Properties of Saline Ice: A Multi-disciplinary Research Plan Submitted to the Office of Naval Research

This plan describes laboratory and theoretical research to be carried out under the Sea Ice Electromagnetics Accelerated Research Initiative of the Office of Naval Research. The plan is built around three broad objectives: 1) to understand the mechanisms and processes that link the orphological/physical and the electromagnetic properties of sea ice; 2) to further develop and verify predictive models for the interaction of visible, infrared and microwave radiation with sea ice; 3) to develop and verify selected techniques in the mathematical theory of inverse scattering that are applicable to problems arising in the interaction of EM radiation with sea ice. The plan will be executed by over 30 investigators from 15 institutions. Research includes measuring and quantifying the physical properties of sea ice, collecting radiometric signatures of different ice types and morphologies, developing and testing forward models of scattering and emission from sea ice, and developing and testing inverse models to extract geophysical data about sea ice from remotely sensed data. Experiments will begin in January of 1993 at the Cold Regions Research and Engineering Laboratory in Hanover, New Hampshire. Work will focus around studies on the Geophysical Research Facility which is a new, concrete lined pool filled with saline water. The facility can be shielded from local fluctuations in weather by using a movable roof and refrigerated blanket. Three measurement series are planned for the winter of 1993. These will focus on collecting data on the microwave and optical properties of an undeformed ice sheet grown from the melt. Measurements to resolve the contributions of volume and surface scattering to sea ice signatures will be performed on an artificially roughened ice sheet. A snow covered ice sheet will be created to study the effects of brine wicking and scattering from snow grains on electromagnetic signatures. Data from these measurements will be used to evaluate the performance of existing forward models. The data will also be used to begin the development of inverse models.The Office of Naval Researc

Radar and laser altimeter measurements over Arctic sea ice.

To validate sea ice models, basin wide sea ice thickness measurements with an accuracy of 0.5 m are required to analyse trends in sea ice thickness, it is necessary to detect changes in sea ice thickness of 4 cm per year on a basin wide scale. The estimated error on satellite radar altimeter estimates of sea ice thickness is 0.45 m and the estimated error on satellite laser altimetry estimates of sea ice thickness is 0.78 m. The Laser Radar Altimetry (LaRA) field campaign took place in the Arctic during 2002. It was the first experiment to collect coincident radar and laser altimetry over sea ice. This thesis analyses the data from LaRA to explore the potential of combining radar and laser altimetry to reduce the uncertainties in measurements of sea ice thickness. Two new methods to analyse the LaRA data are described. The first is the University College London (UCL) Delay/Doppler radar altimeter (D2P) re-tracking algorithm and the second is the UCL D2P power simulator. Each method is calibrated and the associated error is estimated. The UCL D2P power simulator reproduces the D2P returns closely, and is used to estimate the elevation difference between the reflecting surface of the radar and the laser with an accuracy of 0.07 m. The laser is shown to consistently reflect from a higher surface than the radar. The offset between the laser and the radar is consistent with observed snow depths and compares well to snow depth distributions from in-situ data. We find that reducing the error in snow depth to 7 cm reduces the radar error in sea ice thickness from 0.45 m to 0.37 m and the laser error in sea ice thickness from 0.78 m to 0.55 m

Microwave remote sensing from space for earth resource surveys

The concepts of radar remote sensing and microwave radiometry are discussed and their utility in earth resource sensing is examined. The direct relationship between the character of the remotely sensed data and the level of decision making for which the data are appropriate is considered. Applications of active and a passive microwave sensing covered include hydrology, land use, mapping, vegetation classification, environmental monitoring, coastal features and processes, geology, and ice and snow. Approved and proposed microwave sensors are described and the use of space shuttle as a development platform is evaluated

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

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

Research & Technology Report Goddard Space Flight Center

The main theme of this edition of the annual Research and Technology Report is Mission Operations and Data Systems. Shifting from centralized to distributed mission operations, and from human interactive operations to highly automated operations is reported. The following aspects are addressed: Mission planning and operations; TDRSS, Positioning Systems, and orbit determination; hardware and software associated with Ground System and Networks; data processing and analysis; and World Wide Web. Flight projects are described along with the achievements in space sciences and earth sciences. Spacecraft subsystems, cryogenic developments, and new tools and capabilities are also discussed

Research and Technology Report. Goddard Space Flight Center

This issue of Goddard Space Flight Center's annual report highlights the importance of mission operations and data systems covering mission planning and operations; TDRSS, positioning systems, and orbit determination; ground system and networks, hardware and software; data processing and analysis; and World Wide Web use. The report also includes flight projects, space sciences, Earth system science, and engineering and materials