A new algorithm is presented for processing SAR data acquired in the hybrid strip-map/spotlight SAR configuration whose acquisition mode allows the generation of microwave images with an azimuth resolution better than that obtained in the strip-map case and with an imaged area larger than that achieved in the spotlight operation. The algorithm extends the focusing capability of conventional strip-map processing techniques to the hybrid SAR data; this result is achieved by generalising a recently proposed two-step focusing technique for spotlight SAR data processing, the spotlight mode being a particular case of the more general hybrid one. The key point of the procedure is the first filtering step which implements an azimuth convolution between the raw data and a chirp signal whose rate is selected, in the generalised approach, depending on the characteristics of the hybrid acquisition mode. Following this stage, standard strip-map processing procedures can be used to implement the second processing step, leading to fully focused SAR images. The algorithm, mainly oriented to spaceborne systems, is simple and efficient and allows the design of a generalised processing code suitable for data acquired in the hybrid strip-map/spotlight SAR configuration including the strip-map and spotlight modes as special cases. Experiments carried out on simulated data clarify the rationale of the method and confirm its validity.

The use of synthetic aperture radar (SAR) data is presently well established in operational services for flood management. However, some events might be missed because of the limited area that can be observed through a SAR image and the need of programming SAR acquisitions in advance. To tackle these problems, it is possible to setup a system that is able to trigger the SAR acquisitions based on flood forecasts and to take advantage of the various satellite SAR sensors that are presently operating. On behalf of the Italian Civil Protection Department (DPC), a prototype of this kind of system has been setup and preliminary tested, using COSMO-SkyMed (CSK) and Sentinel-1 (S-1) data, to monitor the Po River (Northern Italy) flood occurred in November 2014. This paper presents the prototype system and describes in detail the near real-time flood mapping algorithm implemented in the system. The algorithm was previously developed to classify CSK images, and is modified here in order to be applied to S-1 data too. The major outcomes of the monitoring of the Po River flood are also analyzed in this paper, highlighting the importance of the in advance programming of the radar acquisitions. Results demonstrate the reliability of the flood predictions provided by the model and the accuracy of the flood mapping algorithm. It is also shown that, when CSK and S-1 data are simultaneously acquired, their joint use allows for an interpretation of some ambiguous radar signatures in agricultural areas.

On 25 August 2018, a G3-class geomagnetic storm reached the Earth’s magnetosphere, causing a transient rearrangement of the charged particle environment around the planet, which was detected by the High-Energy Particle Detector (HEPD) on board the China Seismo-Electromagnetic Satellite (CSES-01). We found that the count rates of electrons in the MeV range were characterized by a depletion during the storm’s main phase and a clear enhancement during the recovery caused by large substorm activity, with the key role played by auroral processes mapped into the outer belt. A post-storm rate increase was localized at L-shells immediately above ∼3 and mostly driven by non-adiabatic local acceleration caused by possible resonant interaction with low-frequency magnetospheric waves.

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development.