Spaceborne interferometric synthetic aperture radar (InSAR) has emerged as a crucial geodetic tool for surface deformation monitoring with wide area, high precision, and high resolution. The increasing utilization of low-frequency (LF) (e.g., L-/P-band) SAR systems provides distinct advantages in addressing phase unwrapping and decorrelation challenges, making them indispensable for geological hazard monitoring. However, these LF SAR signals are particularly vulnerable to ionospheric disturbances, posing a significant challenge in maintaining the accuracy of the InSAR measurements. This study provides a systematic review of ionospheric error correction techniques for InSAR, focusing on errors in the line of sight (LOS) and azimuth. The spatiotemporal characteristics of the ionosphere and their impact on SAR and InSAR observations are first reviewed. Subsequently, the principles, methodologies, and performance of various ionospheric correction techniques for SAR LOS and azimuth directions are systematically analyzed. Finally, the key challenges and perspectives in InSAR ionospheric error correction are outlined. The ionosphere is expected to remain a research focus within the InSAR community as LF SAR satellites will continue to develop. Additionally, InSAR ionospheric correction methods offer a promising pathway for high-spatiotemporal-resolution ionospheric mapping, with potential applications in studying space weather and geospace physics.

Since the launch of the European Space Agency’s (ESA’s) Sentinel-1 mission, the synthetic aperture radar (SAR) user community has grown exponentially, adding a large number of users to the field who have only limited experience with the peculiarities of SAR and interferometric SAR (InSAR) data. This growth has been fueled by the increasing availability of globally acquired, free and open SAR data and by a growing number of freely accessible, open source processing tools, such as the Sentinel Application Platform, InSAR Scientific Computing Environment, GMTSAR, and the Alaska Satellite Facility’s cloud-based Hybrid Pluggable Processing Pipeline and OpenScienceLab platforms. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Anticipated to launch in early 2025, the NASA–Indian Space Research Organisation SAR (NISAR) mission will amplify this trend by bringing an unprecedented amount of new, free and open SAR data to the international SAR science community. As the first mission to provide L-band data freely, on a global scale, and in a regularly sampled manner, NISAR will further diversify the SAR application portfolio, likely accelerating community growth. Regional access to simultaneous L- and S-band data facilitated by NISAR will furthermore enable new SAR technology development that may lead to exciting new scientific discoveries. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Focusing on efforts made by NASA and the broader U.S. SAR community, this article summarizes innovative approaches to making the massive datasets of Sentinel-1 and NISAR more accessible to and usable by the SAR community. We summarize novel concepts for data discovery and distribution and highlight tools and services that enable working with SAR data directly at the archive. We also discuss recent education and training efforts developed across the U.S. SAR community to help new users of SAR build capacity to use this exciting dataset. Here, we introduce a set of independent, yet interrelated, projects aimed at developing educational materials and open source science workflows for SAR users with different experience levels.

Learn about IEEE Collabratec at ieee-collabratec.ieee.org The premier networking and collaboration site for technology professionals around the world. Create.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

The Earth observation (EO) sector has been growing rapidly, especially over the past decade, thanks to advancements in spaceborne remote sensing instruments, leading to the development of new applications. With this impressive growth, many questions are starting to be asked about the value of all of these data and whether EO as a technology will become mainstream and reach a level of pervasiveness akin to the Global Positioning System. In this article, we will explore the gaps and challenges that still exist to make remote sensing mainstream, present an overview of the opportunities for remote sensing experts, and finally, provide an outlook for the EO market.

In recent years, the application of deep learning (DL) has revolutionized remote sensing (RS) image analysis, allowing for the extraction of high-level features, and addressing complex tasks. However, the success of DL models relies heavily on the availability of labeled data, and acquiring labeled samples in the RS domain can be particularly challenging due to factors such as cost, time, and the dynamic nature of landscapes. Active learning (AL) has been a well-established concept in RS imagery analysis, even predating the widespread adoption of DL. Its significance lies in its ability to iteratively select the most informative samples from the unlabeled dataset, reducing the annotation cost and improving model performance with limited labeled data. In the era of DL, where the demand for labeled samples is higher than ever, AL has become increasingly crucial. Deep AL is an innovative and intricate approach that seeks to harness the strengths of both DL and AL methodologies. This integration aims to improve the performance of DL models while reducing the reliance on large amounts of labeled data. Integrating AL with deep architectures presents challenges but offers a promising approach to RS tasks.