Remote sensing of earth terrain

In remote sensing, the encountered geophysical media such as agricultural canopy, forest, snow, or ice are inhomogeneous and contain scatters in a random manner. Furthermore, weather conditions such as fog, mist, or snow cover can intervene the electromagnetic observation of the remotely sensed media. In the modelling of such media accounting for the weather effects, a multi-layer random medium model has been developed. The scattering effects of the random media are described by three-dimensional correlation functions with variances and correlation lengths corresponding to the fluctuation strengths and the physical geometry of the inhomogeneities, respectively. With proper consideration of the dyadic Green's function and its singularities, the strong fluctuation theory is used to calculate the effective permittivities which account for the modification of the wave speed and attenuation in the presence of the scatters. The distorted Born approximation is then applied to obtain the correlations of the scattered fields. From the correlation of the scattered field, calculated is the complete set of scattering coefficients for polarimetric radar observation or brightness temperature in passive radiometer applications. In the remote sensing of terrestrial ecosystems, the development of microwave remote sensing technology and the potential of SAR to measure vegetation structure and biomass have increased effort to conduct experimental and theoretical researches on the interactions between microwave and vegetation canopies. The overall objective is to develop inversion algorithms to retrieve biophysical parameters from radar data. In this perspective, theoretical models and experimental data are methodically interconnected in the following manner: Due to the complexity of the interactions involved, all theoretical models have limited domains of validity; the proposed solution is to use theoretical models, which is validated by experiments, to establish the region in which the radar response is most sensitive to the parameters of interest; theoretically simulated data will be used to generate simple invertible models over the region. For applications to the remote sensing of sea ice, the developed theoretical models need to be tested with experimental measurements. With measured ground truth such as ice thickness, temperature, salinity, and structure, input parameters to the theoretical models can be obtained to calculate the polarimetric scattering coefficients for radars or brightness temperature for radiometers and then compare theoretical results with experimental data. Validated models will play an important role in the interpretation and classification of ice in monitoring global ice cover from space borne remote sensors in the future. We present an inversion algorithm based on a recently developed inversion method referred to as the Renormalized Source-Type Integral Equation approach. The objective of this method is to overcome some of the limitations and difficulties of the iterative Born technique. It recasts the inversion, which is nonlinear in nature, in terms of the solution of a set of linear equations; however, the final inversion equation is still nonlinear. The derived inversion equation is an exact equation which sums up the iterative Neuman (or Born) series in a closed form and, thus, is a valid representation even in the case when the Born series diverges; hence, the name Renormalized Source-Type Integral Equation Approach

Remote sensing of earth terrain

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others

Remote sensing of Earth terrain

Remote sensing of earth terrain is examined. The layered random medium model is used to investigate the fully polarimetric scattering of electromagnetic waves from vegetation. The model is used to interpret the measured data for vegetation fields such as rice, wheat, or soybean over water or soil. Accurate calibration of polarimetric radar systems is essential for the polarimetric remote sensing of earth terrain. A polarimetric calibration algorithm using three arbitrary in-scene reflectors is developed. In the interpretation of active and passive microwave remote sensing data from the earth terrain, the random medium model was shown to be quite successful. A multivariate K-distribution is proposed to model the statistics of fully polarimetric radar returns from earth terrain. In the terrain cover classification using the synthetic aperture radar (SAR) images, the applications of the K-distribution model will provide better performance than the conventional Gaussian classifiers. The layered random medium model is used to study the polarimetric response of sea ice. Supervised and unsupervised classification procedures are also developed and applied to synthetic aperture radar polarimetric images in order to identify their various earth terrain components for more than two classes. These classification procedures were applied to San Francisco Bay and Traverse City SAR images

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Cryosphere Applications

Synthetic aperture radar (SAR) provides large coverage and high resolution, and it has been proven to be sensitive to both surface and near-surface features related to accumulation, ablation, and metamorphism of snow and firn. Exploiting this sensitivity, SAR polarimetry and polarimetric interferometry found application to land ice for instance for the estimation of wave extinction (which relates to sub surface ice volume structure) and for the estimation of snow water equivalent (which relates to snow density and depth). After presenting these applications, the Chapter proceeds by reviewing applications of SAR polarimetry to sea ice for the classification of different ice types, the estimation of thickness, and the characterisation of its surface. Finally, an application to the characterisation of permafrost regions is considered. For each application, the used (model-based) decomposition and polarimetric parameters are critically described, and real data results from relevant airborne campaigns and space borne acquisitions are reported

Studies of the Deepwater Horizon Oil Spill With the UAVSAR Radar

On 22- 23 June 2010, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) L band radar imaged the Deepwater Horizon oil spill and the effects of oil that was transported within the Gulf of Mexico. We describe the campaign and discuss the unique contributions of the UAVSAR radar to the study of the detection, migration, and impact of oil from the spill. We present an overview of UAVSAR data analyses that support the original science goals of the campaign, namely, (1) algorithm development for oil slick detection and characterization, (2) mapping of oil intrusion into coastal wetlands and intercoastal waterways, and (3) ecosystem impact studies. Our study area focuses on oil-affected wetlands in Barataria Bay, Louisiana. The results indicate that fine resolution, low-noise, L band radar can detect surface oil-on-water with sufficient sensitivity to identify regions in a slick with different types of oil/emulsions and/or oil coverage; identify oil on waters in inland bays and differentiate mixed/weathered oil from fresh oil as it moves into the area; identify areas of potentially impacted wetlands and vegetation in the marshes; and support the crisis response through location of compromised booms and heavily oiled beaches

Space Remote Sensing and Detecting Systems of Oceangoing Ships

This paper introduces the implementation of space remote sensing and detecting systems of oceangoing ships as an alternative to the Radio – Automatic Identification System (R-AIS), Satellite – Automatic Identification System (S-AIS), Long Range Identification and Tracking (LRIT), and other current vessel tracking systems. In this paper will be not included  a new project known as a Global Ship Tracking (GST) as an autonomous and discrete satellite network designed by the Space Science Centre (SSC) for research and postgraduate studies in Satellite Communication, Navigation and Surveillance (CNS) at Durban University of Technology (DUT). The ship detection from satellite remote sensing imagery system is a crucial application for maritime safety and security, which includes among others ship tracking, detecting and traffic surveillance, oil spill detection service, and discharge control, sea pollution monitoring, sea ice monitoring service, and protection against illegal fisheries activities. The establishment of a modern sea surface and ships monitoring system needs enhancement of the Satellite Synthetic Aperture Radar (SSAR) that is here discussed as a modern observation infrastructure integrated with Ships Surveillance and Detecting via SSAR TerraSAR-X Spacecraft, Ships Surveillance and Detecting via SSAR Radarsat Spacecraft and Vessels Detecting System (VDS) via SSAR

6-meter wavelength polarimetric inverse synthetic aperture radar mapping of the Moon

Remote sensing of planetary surfaces is an effective method for gaining knowledge of the processes that shape the planetary bodies in our solar system. This is useful for uncovering the environment of the primordial solar system and to study the current state of the upper crusts of the other planets in our neighborhood. A recent 6-meter wavelength polarimetric radar map of the Moon showed unexpectedly low depolarized radar returns in two regions on the lunar nearside. These two areas were a highland region between Mare Imbrium and Mare Frigoris, and the highland area surrounding the Schiller-Zucchius impact basin. These two regions showed characteristics unlike those of typical highland regions of the lunar surface. So far, there has been no readily available explanation for this observation. In this study, it is shown that the likely cause is an increased loss tangent due to chemical differences in the first few hundred meters of the lunar soil. We also show the absence of any coherent subsurface, which could be the preserved remains of an ancient basaltic plain. We do this by comparing the 6-meter polarimetric radar map to other relevant data sets: 1) surface TiO_2 and FeO abundance, 2) surface rock population, 3) radar maps of the Moon with other wavelengths, and 4) visual spectrum images of the Moon. The area near the Schiller-Zucchius basin was shown to be consistent with other areas with similar surface chemical compositions, but the region between Mare Imbrium and Mare Frigoris showed significantly lower mean power in comparison to otherwise similar regions. While we can not conclusively determine the cause, we hypothesize that the low radar return is explained by an increased concentration of iron and titanium oxides in the volume beneath the surface, potentially due to remnants of primordial lunar volcanism. The results show that long wavelength polarimetric radar measurements of the Moon are very powerful tools for studying the earliest stages of the evolution of the Moon