## 1 Introduction  
The escalating global demand for seamless communication and the proliferation of Internet of Things (IoT) applications have rendered terrestrial networks inadequate to fulfill the stringent real-time requirements of users, especially in remote environments such as deserts, oceans, and disaster-stricken areas where terrestrial infrastructure is either sparse or entirely absent [1,2]. These scenarios underscore the critical need for next-generation wireless networks that prioritize ultra-low latency and ultra-high reliability to support mission-critical IoT deployments [3]. In this context, the integration of satellite networks--particularly low-Earth orbit (LEO) constellations--into terrestrial ecosystems emerges as a transformative strategy to achieve ubiquitous connectivity, enabling continuous data acquisition and dissemination across underserved regions [4]. By leveraging the unique attributes of LEO satellites, including their low propagation delays, wide coverage footprints, and inherent resilience against ground-based disruptions, integrated satellite-terrestrial networks (ISTNs) can effectively bridge the digital divide, ensuring robust IoT services even in the most challenging operational theaters. The rapid expansion of remote sensing satellite systems has transformed them into a multifaceted ecosystem encompassing communication, navigation, Earth observation, and experimental satellites, enabling unprecedented integration with cutting-edge technologies like artificial intelligence (AI), advanced networking, and IoT. This convergence has dramatically expanded the utility of remote sensing data, rendering it indispensable across critical domains such as land resource management, economic development, disaster mitigation, meteorology, and environmental monitoring. As these fields increasingly rely on real-time, context-aware insights, the demand for remote sensing resources has undergone a paradigm shift, necessitating a departure from conventional query-based and subscription-based service models [5]. Existing paradigms often falter in addressing IoT-driven requirements due to their high technical barriers, limited contextual relevance, and inflexibility in dynamic scenarios, impeding efficient access to high-value, domain-specific data. This gap highlights the urgent need for innovative, proactive data acquisition strategies capable of delivering timely, tailored remote sensing resources to meet the evolving demands of smart applications, particularly within ISTNs where seamless cross-modal data fusion and adaptive decision making are paramount. To address this critical need, there is a pressing demand for a remote sensing information service that prioritizes speed, accuracy, and adaptability, particularly for IoT applications requiring dynamic real-time data to enhance situational awareness and enable informed decision making. Low-orbit remote sensing satellite networks have emerged as the preferred infrastructure for real-time data acquisition and dissemination, leveraging their inherent strengths in global coverage, cost-effective deployment, and minimal propagation delays [6,7]. These networks are pivotal for IoT-centric applications such as weather forecasting, disaster monitoring, and remote sensing mapping, where rapid access to high-resolution spatial data is non-negotiable. Recent advancements have yielded successful implementations of low-orbit satellite networks, demonstrating their potential to revolutionize data delivery [8, 9, 10]. However, traditional low-Earth-orbit (LEO) satellite systems often face constraints due to the limited onboard processing capabilities. Fortunately, breakthroughs in onboard hardware technology have introduced a new generation of processors that deliver high performance with minimal power consumption, enabling efficient onboard compression and the slicing of remote sensing data [11].