In this paper, the feasibility of satellite remote sensing in detecting and predicting locations of buried objects in the archaeological site of Saruq Al-Hadid, United Arab Emirates (UAE) was investigated. Satellite-borne synthetic aperture radar (SAR) is proposed as the main technology for this initial investigation. In fact, SAR is the only satellite-based technology able to detect buried artefacts from space, and it is expected that fine-resolution images of ALOS/PALSAR-2 (L-band SAR) would be able to detect large features (>1 m) that might be buried in the subsurface (<2 m) under optimum conditions, i.e., dry and bare soil. SAR data were complemented with very high-resolution Worldview-3 multispectral images (0.31 m panchromatic, 1.24 m VNIR) to obtain a visual assessment of the study area and its land cover features. An integrated approach, featuring the application of advanced image processing techniques and geospatial analysis using machine learning, was adopted to characterise the site while automating the process and investigating its applicability. Results from SAR feature extraction and geospatial analyses showed detection of the areas on the site that were already under excavation and predicted new, hitherto unexplored archaeological areas. The validation of these results was performed using previous archaeological works as well as geological and geomorphological field surveys. The modelling and prediction accuracies are expected to improve with the insertion of a neural network and backpropagation algorithms based on the performed cluster groups following more recent field surveys. The validated results can provide guidance for future on-site archaeological work. The pilot process developed in this work can therefore be applied to similar arid environments for the detection of archaeological features and guidance of on-site investigations.

Heatwaves are intensifying across the Middle East with distinct daytime and nighttime impacts. We quantify the climatology and trends of daytime, nighttime, and compound heatwaves using station observations for 2005 to 2025, and evaluate four AI-weather models for near-surface air temperature during a recent high-impact episode (Zittis et al., 2025).Daytime heatwaves recur from the eastern Mediterranean through northern Iraq and Iran into Anatolia during warm seasons, whereas nighttime and compound events cluster along the southern Red Sea and Arabian Gulf coasts. Trends show significant summer and autumn increases across all classes (daytime: +4.09 and +6.09; nighttime: +4.13 and +6.22; compound: +3.42 and +2.40 per year), a winter increase led by nighttime events, and a spring increase in both nighttime and compound events, consistent with asymmetric diurnal warming, strong land–atmosphere coupling over arid interiors, and coastal humidity that limits nocturnal cooling.A case study of 10 to 27 June 2024 documents record-breaking daytime anomalies over the northern Middle East and persistent nocturnal warmth along maritime margins, with peak impacts around 17 June in the Mecca region. ERA-5 diagnostics indicate a quasi-stationary Rossby wavetrain, an anomalous ridge over southwest Asia, and an intensified Arabian Heat Low that weakened low-level ventilation and sustained humid coastal nights.We assess GraphCast, PanguWeather, FourCastNetv2, and Aurora using six-hourly forecasts initialized at 00 UTC on 12 June 2024 and verified against ERA-5 from 12 to 22 June. All capture synoptic timing and multi-day persistence but underestimate daytime peaks and show lead-dependent cold biases. PanguWeather provides the strongest deterministic temperature guidance; GraphCast corroborates synoptic evolution. Operationally, a bias-corrected PanguWeather and GraphCast blend is recommended.Reference:Zittis, G., Alberti, T., Almazroui, M. et al. Analysis of the 2024 Hajj heat event and future temperature extremes in Mecca. npj Nat. Hazards 2, 107 (2025). https://doi.org/10.1038/s44304-025-00159-3