Sudden surface anomalies—ranging from earthquakes and floods to wildfires and industrial accidents—pose escalating threats to ecosystems and societies worldwide. Real-time remote sensing has emerged as a transformative approach for monitoring and early warning of such abrupt events, driven by recent advances in satellite constellations, on-orbit artificial intelligence, and multisource data fusion. This review systematically synthesizes the global landscape of sudden surface anomalies, elucidates their spatiotemporal patterns and underlying drivers, and critically assesses the capabilities and limitations of current remote sensing technologies for rapid detection and assessment. We highlight the shift from traditional, latency-prone processing pipelines toward integrated systems that leverage edge computing, lightweight deep learning models, and in-orbit data fusion to enable timely and automated anomaly detection. Key technical challenges are identified—including real-time atmospheric correction, model deployment under severe on-orbit resource constraints, and robust multihazard identification across heterogeneous sensor platforms. We further discuss the blueprint for next-generation systems, advocating for constellation-scale coordination, adaptive sensing, and seamless integration from detection to decision support. By bridging technical innovations with operational needs, we outline a pathway toward resilient, scalable, and intelligent remote sensing networks capable of providing actionable insights for disaster mitigation and environmental management. This review not only frames the state of the art but also charts the course for future research and system development in real-time remote sensing for sudden surface anomaly monitoring and early warning.

Crop residue burning (CRB) is a major contributor to air pollution in China. Current fire detection methods, however, are limited by either temporal resolution or accuracy, hindering the analysis of CRB's diurnal characteristics. Here we explore the diurnal spatiotemporal patterns and environmental impacts of CRB in China from 2019 to 2021 using the recently released NSMC-Himawari-8 hourly fire product. Our analysis identifies a decreasing directionality in CRB distribution in the Northeast and a notable southward shift of the CRB center, especially in winter, averaging an annual southward movement of 7.5°. Additionally, we observe a pronounced skewed distribution in daily CRB, predominantly between 17:00 and 20:00. Notably, nighttime CRB in China for the years 2019, 2020, and 2021 accounted for 51.9%, 48.5%, and 38.0% respectively, underscoring its significant environmental impact. The study further quantifies the hourly emissions from CRB in China over this period, with total emissions of CO, PM₁₀, and PM_2.5 amounting to 12,236, 2,530, and 2,258 Gg, respectively. Our findings also reveal variable lag effects of CRB on regional air quality and pollutants across different seasons, with the strongest impacts in spring and more immediate effects in late autumn. This research provides valuable insights for the regulation and control of diurnal CRB before and after large-scale agricultural activities in China, as well as the associated haze and other pollution weather conditions it causes.