## 1 Introduction
Additionally, the adoption of relay-assisted access strategies within the space-air-ground integrated network (SAGIN) framework enhances connectivity for diverse IoT devices, ensuring seamless data flow across heterogeneous network infrastructures [12,13]. Looking ahead, intelligent remote sensing satellites are poised to function as dynamic content service providers withinthe emerging satellite Internet ecosystem. By seamlessly integrating remote sensing capabilities with communication satellite networks, it becomes feasible to establish real-time communication channels between satellites and IoT users, thereby optimizing resource allocation and significantly improving responsiveness to evolving user demands [14]. As user applications and data demands expand, the volume and diversity of data generated by remote sensing satellites have surged, outstripping the load capacity of existing data transmission systems [15]. This disparity has made it increasingly challenging for users to promptly access relevant remote sensing resources from the vast datasets available, thereby underscoring the need for real-time data transmission and intelligent push capabilities. To tackle this issue, an intelligent push strategy that proactively delivers potentially relevant remote sensing data from satellite networks to IoT users can significantly reduce content request delays. Such a strategy is crucial for maximizing the utilization of satellite and user cache resources and enhancing the overall performance of integrated satellite-terrestrial IoT networks. Deep reinforcement learning (DRL) has proven effective in addressing optimization challenges within dynamic network environments, as it leverages its perception and decision-making capabilities to discover optimal solutions through agent-environment interactions. Recently, several studies have applied DRL to optimize resource allocation in satellite networks [16; 17]. In this work, we focus on the integration of large-scale remote sensing constellations with communication satellite networks and propose a multi-modal multi-agent proximal policy optimization (MAPPO) algorithm, termed multi-modal-MAPPO, to achieve the real-time intelligent push of multi-modal remote sensing data. The primary contributions of this paper are as follows:  
* Firstly, to address the distinctive challenges of link intermittency and resource heterogeneity in satellite-terrestrial integrated networks, we introduce a link state-aware proactive delivery algorithm tailored for satellite networks. This algorithm innovatively constructs a multi-granularity network state monitoring model to dynamically capture the real-time fluctuations and outage characteristics of both satellite/user cache resources and link states. By framing the dynamic delivery problem as a Markov decision process (MDP), we provide a robust theoretical foundation for intelligent decision making. * Secondly, our research comprehensively incorporates the spatio-temporal characteristics of remote sensing data that are vital for IoT applications. These characteristics encompass content generation time, geographic parameters (such as longitude and latitude ranges), payload type, resolution, and product level. To the best of our knowledge, this study represents the first application of multi-agent DRL to optimize an in-orbit proactive push for remote sensing data within a large-scale integrated satellite-terrestrial IoT network. This novel approach enhances data availability and access efficiency for a wide range of satellite applications. * Thirdly, to achieve the active delivery of multi-modal remote sensing data in ISTNs, we propose a multi-modal collaborative multi-agent deep reinforcement learning framework. This framework is the first application of MAPPO to in-orbit remote sensing data delivery scenarios.