Pearse C. Murphy

Soil moisture content is typically measured in situ using various instruments; however, due to the heterogeneous nature of soil, these measurements are only suitable at a very local scale. To overcome this limitation, earth observation satellite remote sensing data, particularly through the inversion of the closure phases of interferometric synthetic aperture radar (InSAR) observations, enables the determination of soil moisture content at regional to global scales. Here, we present, for the first time, a regional-scale study of soil moisture determined from remote sensing observations of Malawi, specifically, two areas of interest capturing arable and national parklands in Kasungu and Liwonde. We invert the closure phases of InSAR acquisitions from Sentinel-1 between 1 January 2023 and 31 May 2024 to measure the soil moisture content in the same time range. We show that soil moisture content is heavily influenced by local precipitation and highlight common trends in soil moisture in both regions. We suggest the difference in soil moisture observed inside and outside the national parks is a result of different overlying vegetation and conservation agriculture practices during the maize crop cycle in Malawi. Our results show the effectiveness and suitability of remote sensing techniques to monitor soil moisture at a regional scale. The upcoming additions to ESA’s fleet of earth observation satellites, in particular Sentinel-1C, will allow for higher-time-resolution soil moisture measurements.

The extraction, processing and transport of crude oil in the Niger Delta region of Nigeria has long been associated with collateral environmental damage to the largest mangrove ecosystem in Africa. Oil pollution is impacting not only one of the planet’s most ecologically diverse regions but also the health, livelihoods, and social cohesion of the Delta region inhabitants. Quantifying and directly associating localised oil pollution events to specific petrochemical infrastructure is complicated by the difficulty of monitoring such vast and complex terrain, with documented concerns regarding the thoroughness and impartiality of reported oil pollution events. Earth Observation (EO) offers a means to deliver such a monitoring and assessment capability using Normalised Difference Vegetation Index (NDVI) measurements as a proxy for mangrove biomass health. However, the utility of EO can be impacted by persistent cloud cover in such regions. To overcome such challenges here, we present a workflow that leverages EO-derived high-resolution (10 m) synthetic aperture radar data from the Sentinel-1 satellite constellation combined with machine learning to conduct observations of the spatial land cover changes associated with oil pollution-induced mangrove mortality proximal to pipeline networks in a 9000 km² region of Rivers State located near Port Harcourt. Our analysis identified significant deforestation from 2016–2024, with an estimated mangrove mortality rate of 5644 hectares/year. Using our empirically derived Pipeline Impact Indicator (PII), we mapped the oil pipeline network to 1 km resolution, highlighting specific pipeline locations in need of immediate intervention and restoration, and identified several new pipeline sites showing evidence of significant oil spill damage that have yet to be formally reported. Our findings emphasise the critical need for the continuous and comprehensive monitoring of oil extractive regions using satellite remote sensing to support decision-making and policies to mitigate environmental and societal damage from pipeline oil spills, particularly in ecologically vulnerable regions such as the Niger Delta.

The Low Frequency Array (LOFAR) is an international radio telescope array, consisting of 38 stations in the Netherlands and 14 international stations spread over Europe. Here, we present an observation method to study the Jovian decametric radio emissions from several LOFAR stations (here Birr Castle in Ireland, Nançay in France, and Postdam in Germany), at high temporal and spectral resolution. This method is based on prediction tools, such as radio emission simulations and probability maps, and data processing. We report an observation of Io-induced decametric emission from 2021 June, and a first case study of the substructures that compose the macroscopic emissions (called millisecond bursts). The study of these bursts makes it possible to determine the electron populations at the origin of these emissions. We then present several possible future avenues for study based on these observations. The methodology and study perspectives described in this paper can be applied to new observations of Jovian radio emissions induced by Io, but also by Ganymede or Europa, or Jovian auroral radio emissions.

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts, rotating radio transients, the search for extraterrestrial intelligence, Jupiter, and the Sun.

Low frequency radio wave scattering and refraction can have a dramatic effect on the observed size and position of radio sources in the solar corona. The scattering and refraction is thought to be due to fluctuations in electron density caused by turbulence. Hence, determining the true radio source size can provide information on the turbulence in coronal plasma. However, the lack of high spatial resolution radio interferometric observations at low frequencies, such as with the LOw Frequency ARray (LOFAR), has made it difficult to determine the true radio source size and level of radio wave scattering. Here we directly fit the visibilities of a LOFAR observation of a Type IIIb radio burst with an elliptical Gaussian to determine its source size and position. This circumvents the need to image the source and then de-convolve LOFAR’s point spread function, which can introduce spurious effects to the source size and shape. For a burst at 34.76 MHz, we find full width at half maximum (FWHM) heights along the major and minor axes to be 18.8′ ± 0.1′ and 10.2′ ± 0.1′, respectively, at a plane of sky heliocentric distance of 1.75 R_⊙. Our results suggest that the level of density fluctuations in the solar corona is the main cause of the scattering of radio waves, resulting in large source sizes. However, the magnitude of ε may be smaller than what has been previously derived in observations of radio wave scattering in tied-array images.

The discovery of the ubiquity of habitable extrasolar planets, combined with revolutionary advances in instrumentation and observational capabilities, have ushered in a renaissance in the millenia-old quest to answer our most profound question about the Universe and our place within it - Are we alone? The Breakthrough Listen Initiative, announced in July 2015 as a 10-year 100M USD program, is the most comprehensive effort in history to quantify the distribution of advanced, technologically capable life in the universe. In this white paper, we outline the status of the on-going observing campaign with our primary observing facilities, as well as planned activities with these instruments over the next few years. We also list collaborative facilities which will conduct searches for technosignatures in either primary observing mode, or commensally. We highlight some of the novel analysis techniques we are bringing to bear on multi-petabyte data sets, including machine learning tools we are deploying to search for a broader range of technosignatures than was previously possible.