Remote Sensing

Synthetic Aperture Radar (SAR) signal processing from airborne and satellite platforms, capable of millimetric resolution, is a novel sensing technology. It is able to generate high resolution images in all weather conditions and independently by sun light and the product final is comparable and complementary to that obtained with optical sensors. The SAR field of applications is very wide and is enormously growth in recent years: topography, oceanography, geology, monitoring of glacier and snow wetness in glaciology, cropping classification in agriculture, forestry, environment and disaster monitoring as oil spills, flooding, earthquake urban growth, global change represent only a part of areas where these technique is used. In recent years, the number of sensors which use this type of technology is noticeably increased and today, more than 15 spaceborne SAR systems operate regularly sending numerous Earth Observation (EO) data for many applications.
Consequently to the availability of all these data, it appears evident the necessity to provide an improved accuracy and reliability of EO data and above all a method to compare these data, sent by different sensors. Moreover, looking at the recommendation made by Committee Earth Observation Satellites (CEOS) in 2001, it is also indispensable to ensure that all measurements and associated instrumentation used for any quantitative purpose in remote sensing be fully traceable to SI (International System of Units) as part of the Quality Assurance process.
To realise their potential in traceable and trustworthy measurement, robust assessments of the uncertainties associated with SAR products are required. This project is part of the Global Sensing and Satellite Centre of Excellence (GloSS), which is a new collaboration between the University of Surrey and the National Physical Laboratory (NPL) (the first UK's National Measurement Institute), to build a unique joint research centre connecting satellite research and measurement. Precisely in this project is proposed a methodology based on the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) for the analysis of uncertainties in SAR remote sensing technologies. The GUM defines general rules which permit one to evaluate and to express uncertainty in measurement and this methodology can be applied to a wide range of uncertainty evaluation problems within standardization, calibration, laboratory accreditation and measurement services. The principal idea of this project is to assess the sources of uncertainty, developing computational approaches to propagate uncertainties through the whole SAR signal processing chain, from the acquisition step of data echoes reradiated from scene under observation to the final product available for the end user. The goal is to construct uncertainties budgets to quantify the total uncertainty of SAR products and to assess requirements for a ground-based metrology infrastructure to calibrate and enhance SAR technologies. Particular attention will be given in this project above all on the UK Nova SAR and European Sentinel-1 missions. This project represents a complete novelty in the SAR and more generally in remote sensing community. It appears to be an ambitious research field and it has all the characteristics to make an important contribution to defining a quality parameter for SAR products. It opens a door for EO data in the metrology world, which is still not properly explored and it represents a good starting point for future research activity. For the first time it attempts to assess how it is possible to evaluate the uncertainties in all different steps of the processing chain of this data, extending to it the concept of traceability to SI.