This study, conducted under ESA’s Global Development Assistance programme, presents a pilot demonstration integrating Earth Observation (EO) data into livestock management in Paraguay. Led by GMV in collaboration with FECOPROD and the World Bank, the study applied the Carnegie-Ames-Stanford Approach (CASA) model, driven by Sentinel-2 and climate data, to generate high-resolution, 10-metre dry matter biomass (DMB) maps. These products were calibrated using field plots and integrated into a dedicated application for trend analysis. Model validation against ground-truth bale weights showed strong performance with an R2 of 0.89 and an RMSE of 8.83%. The results suggest that EO-derived insights can support the optimisation of grazing patterns, potentially helping to prevent overgrazing. This provides farmers with actionable data to align feed production with seasonal cycles, improving resource management. However, the current validation is limited to a single year and confined to managed plots within the study area; further work is required to assess model performance across different seasonal conditions and in the broader Chaco landscape, where mixed woody vegetation is prevalent. Notwithstanding these limitations, this scalable approach demonstrates potential to reduce environmental impact while enhancing productivity and offers a replicable framework for Paraguay’s agricultural sector and similar regions globally, pending further validation.

Land cover classification (LCC) and natural disaster response (NDR) are important issues in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation (EO) imaging data for LCC and NDR often rely on fully annotated and segmented datasets. Creating these datasets requires a large amount of effort, and a lack of suitable datasets has become an obstacle in scaling the use of machine learning for EO. In this study, we extend our prior work on Scene-to-Patch models: an alternative machine learning approach for EO that utilizes Multiple Instance Learning (MIL). As our approach only requires high-level scene labels, it enables much faster development of new datasets while still providing segmentation through patch-level predictions, ultimately increasing the accessibility of using machine learning for EO. We propose new multi-resolution MIL architectures that outperform single-resolution MIL models and non-MIL baselines on the DeepGlobe LCC and FloodNet NDR datasets. In addition, we conduct a thorough analysis of model performance and interpretability.

Realizing packaged state-of-the-art performance of monolithic microwave integrated circuits (MMICs) operating at millimeter wavelengths presents significant challenges in terms of electrical interface circuitry and physical construction. For instance, even with the aid of modern electromagnetic simulation tools, modeling the interaction between the MMIC and its package embedding circuit can lack the necessary precision to achieve optimum device performance. Physical implementation also introduces inaccuracies and requires iterative interface component substitution that can produce variable results, is invasive and risks damaging the MMIC. This paper describes a novel method for in situ optimization of packaged millimeter-wave devices using a pulsed ultraviolet laser to remove pre-selected areas of interface circuit metallization. The method was successfully demonstrated through the optimization of a 183 GHz low noise amplifier destined for use on the MetOp-SG meteorological satellite series. An improvement in amplifier output return loss from an average of 12.9 dB to 22.7 dB was achieved across an operational frequency range of 175–191 GHz and the improved circuit reproduced. We believe that our in situ tuning technique can be applied more widely to planar millimeter-wave interface circuits that are critical in achieving optimum device performance.

As staples of science fiction, space technologies, much like outer space itself, have often been regarded as being ‘out there’ objects of international security analysis. However, as a growing subset of security scholarship indicates, terrestrial politics and practices are ever more dependent on space technologies and systems. Existing scholarship in ‘astropolitics’ and ‘critical astropolitics’ has tended to concentrate on how such technologies and systems underpin and impact the dynamics of military security, but this article makes the case for wider consideration of ‘orbital infrastructures’ as crucial to conceptions and governance of planetary security in the context of the ‘Anthropocene’. It does so by outlining and analysing in detail Earth Observation (EO) and Near-Earth Object (NEO) detection systems as exemplary cases of technological infrastructures for ‘looking in’ on and ‘looking out’ for forms of planetary insecurity. Drawing on and extending recent theorisations of technopolitics and of Large Technical Systems, we argue that EO and NEO technologies illustrate, in distinct ways, the extent to which orbital infrastructures should be considered not only part of the fabric of contemporary international security but as particularly significant within and even emblematic of the technopolitics of planetary (in)security.

The development of oceanography and meteorology has greatly benefited from satellite-based data of Earth’s atmosphere and ocean. Traditional Earth observation missions have utilised Sun-synchronous orbits with repeat ground tracks due to their advantages in visible and infrared wavelengths. However, diversification of observation wavelengths and massive deployment of miniaturised satellites are both enabling and necessitating new kinds of space missions. This paper proposes several unconventional satellite orbits intended for use in, but not limited to, Earth observation. This ‘toolbox’ of orbits and taxonomy thereof will thus support the definition of design requirements for the individual satellites in nano-satellite constellations developed by national space agencies, industries and academia.

Antenna-pattern measurements obtained from a double-metal supra-terahertz-frequency (supra-THz) quantum cascade laser (QCL) are presented. The QCL is mounted within a mechanically micro-machined waveguide cavity containing dual diagonal feedhorns. Operating in continuous-wave mode at 3.5 THz, and at an ambient temperature of ~60 K, QCL emission has been directed via the feedhorns to a supra-THz detector mounted on a multi-axis linear scanner. Comparison of simulated and measured far-field antenna patterns shows an excellent degree of correlation between beamwidth (full-width-half-maximum) and sidelobe content and a very substantial improvement when compared with unmounted devices. Additionally, a single output has been used to successfully illuminate and demonstrate an optical breadboard arrangement associated with a future supra-THz Earth observation space-borne payload. Our novel device has therefore provided a valuable demonstration of the effectiveness of supra-THz diagonal feedhorns and QCL devices for future space-borne ultra-high-frequency Earth-observing heterodyne radiometers.

Irrigation according to reliable estimates of crop water requirements (CWR) is one of the key strategies to ensure long-term sustainability of irrigated agriculture. In southern Mediterranean regions, during the irrigation season, CWR is almost totally controlled by the potential evapotranspiration of the irrigated crop. An innovative system for forecasting crop potential evapotranspiration (ETp) has been implemented recently in the Campania region (southern Italy). The system produces ETp forecasts with a lead time of up to 5 days, by coupling the visible and near-infrared crop imagery with numerical weather prediction outputs of a limited area model. The forecasts are delivered to farmers with a simple and intuitive web app interface, which makes daily real-time ETp maps accessible from desktop computers, tablets and smartphones. Forecast performances were evaluated for maize fields of two farms in two irrigation seasons (2014–2015). The mean absolute bias of the forecasted ETp was <0.3 mm/day and the RMSE was <0.6 mm/day, both for lead times up to 5 days.