An analysis of the polarimetric scattering properties of oil-covered waters is conducted using the classic Poincaré ellipticity parameter chi (χ) and the degree of polarization (m) from the Stokes parameters of hybrid polarized mode synthetic aperture radar (SAR). Oil spills reduce m for all four cases considered in this study. However, for the natural oil seep case considered, χ has a change in signs, comparing oil-covered waters with the “clean” ocean surface. The χ sign reversal is basic for “sign difference oil spill detection methods.” However, a problem is that the oil spill related to the deep water horizon (DWH) disaster did not exhibit a reversal in χ signs, comparing the “clean” ocean surface to the area contaminated by crude oil. The scattering mechanism related to the oil seep is different from that of the DWH oil spill; the former is dominated with even bounce scattering and the latter is dominated by Bragg scattering, similar to that of the clean oil-free ocean surface, in the imaging area.

A characteristic analysis of sea ice is conducted using the classic Poincare ellipticity parameter chi (χ) and the degree of polarization (m) from the Stokes parameters of the compact polarized (CP) mode of Synthetic Aperture Radar (SAR). The polarimetric characteristics of sea ice for the simulated CP mode are compared with the classic polarimetric parameters, specifically the polarimetric entropy (H) and polarimetric scattering angle (α), for quad-polarized SAR systems. The physically based m-χ decomposition, obtained from the Stokes parameters of hybrid polarimetry mode CP SAR, is applied to sea ice classification, because the colorcoded images of the m-χ decomposition can be related to the basic scattering processes directly. Therefore, this methodology is able to provide more details in terms of sea ice classification than is possible with single polarimetic parameters. The classification results with the m-χ decomposition from CP SAR are shown to be comparable to those resulting from the Pauli RGB decomposition from quadpol SAR systems. Therefore, due to the planned wide swath widths for future CP SAR missions, such as the Canadian RADARSAT Constellation Mission, we suggest that hybrid polarimetry is an attractive mode for future CP SAR-based sea ice monitoring.

The oil–water mixture ratio for oil spills on the ocean surface is an important parameter for volume estimation of oil spills, response strategy for the oil spills, cleanup operations, and remediation planning for the impacts on wildlife. Hybrid-polarized (HP) mode compact polarization (CP) synthetic aperture radar (SAR) imagery will soon be available with the launch of the RADARSAT Constellation Mission. The advantage of the proposed new SAR system is that CP images will have wider swath and shorter revisit time compared to quad-polarization (QP) images, which are presently available from space-borne and air-borne SAR. We present a methodology to retrieve the oil–water mixture ratio at the ocean surface using CP SAR imagery. We emulated the HP mode of CP SAR image using Uninhabited Aerial Vehicle SAR (UAVSAR) L band observations collected on 23 June 2010 over the site of the Deep Water Horizon drilling rig. The gap between elements ratio of CP SAR covariance matrix and that of QP SAR Sinclair matrix is bridged. Numerical optimization and look up table methods are used to relate the oil–water mixture ratio to elements of the covariance matrix for the HP data backscatter. The mixture ratio estimates determined from the ratio of diagonal elements of the covariance matrix for HP mode CP data are compared with results retrieved from the co-polarization ratio from the original QP SAR observations. Results from the proposed methodology for SAR images captured in the HP mode of CP data are shown to compare favourably to observed in situ data of the mixture ratios.

The rapid growth of remote sensing data calls for the construction of new computational models for algorithmic exploration, which requires on-demand execution, instant response, and multitenancy. We call this model on-the-fly computing, which could reduce the complexity of cloud programming for remote sensing data analysis and benefit from efficient multiplexing. As an advancement of cloud computing, serverless computing makes it possible to realize the on-the-fly computational model. In the study, the concise definition of an on-the-fly computing model for remote sensing data analysis and the corresponding software architecture based on the serverless computing commodities are presented. The proof-of-concept experiments have suggested that the on-the-fly computing model for remote sensing data analysis can be efficiently implemented as a serverless software. The response time is mainly related to the tile reading operation and data structure conversion. In the case of high concurrency, the system can scale to hundreds of instances in seconds.