Time Series Analysis Based on SAR Images

Dear Colleagues,

With its weather-independent imaging capability, Synthetic Aperture Radar (SAR) is the ideal tool for continuous monitoring of the Earth’s surface. A number of major civilian SAR satellite missions have accumulated repeated observations over the last few decades. Today, especially thanks to the Sentinel-1 constellation, almost every corner of the Earth is covered routinely every 24 days in general and numerous high priority targets are imaged every 6–12 days in multiple imaging geometries. The global observation capability is growing rapidly, as new players are entering the SAR field. Alongside large space agencies, which have traditionally focused their efforts on building large and capable SAR instruments, relatively small and dynamic private companies are launching constellations of dozens of small SAR satellites, significantly increasing temporal sampling. Consequently, in addition to large missions like NISAR, SAOCOM, Tandem-L, etc., we expect to see rich datasets being acquired by small satellite constellations launched by organizations like Capella Space, Urthecast, Iceye, and NovaSAR. Another chapter will open once SAR geo–synchronous satellites are launched.

Temporal series of various radar-derived properties, such as backscatter, interferometric phase, interferometric coherence, and polarimetric decompositions, have been used to monitor a wide range of processes, like ice flows of glaciers, which are so important for understanding climate change; volcanic unrest (inflation/deflation) to predict eruptions; detecting changes occurring on the surface, from flooding events to very subtle ones. Permanent or persistent scatterer SAR interferometry in particular has been used to monitor the physical infrastructure of all kinds of structures, such as railways, roads, bridges, dams, or pipelines. Following the phase variations, one can reconstruct deformation with sub-centimeter to millimeter precision. SAR, Polarimetric SAR, and Interferometric SAR have also enabled monitoring of moisture content (soils, forests), with important implications for fire prevention, agriculture, crop monitoring, snow water equivalent estimation, grounding line delineation, permafrost monitoring, land use classification, and much more.

The benefits of improved sampling in the temporal dimension are manifold: It enhances our ability to follow and decouple physical processes as they develop in time, and allows us to measure rate of change with unprecedented precision and, in other cases, to enable advanced processing techniques that can preserve a finer spatial resolution.

Considering the processing capabilities available today, we are certainly facing a shortage of efficient algorithms that can fully exploit the potential of the large volumes of SAR data that are already being gathered. A whole spectrum of algorithms, ranging from heuristic approaches to physics-driven techniques, needs to be developed to maximize the return from these large datasets. While artificial intelligence and machine-learning approaches represent an important path forward, there is still room for developing techniques that build on explicit physical modeling of the interaction of the radar waves with the target of interest. Fusion of backscatter and interferometric phase information from almost contemporaneous multi-polarization, multi-frequency SAR data will further contribute to our understanding of the basic scattering of radar waves, opening up new avenues for SAR-related applications.

We are looking forward to receiving your contribution to this Special Issue on “Time Series Analysis Based on SAR Images”.

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• Synthetic Aperture Radar
• time series
• backscatter
• interferometry
• coherent change detection