屋顶太阳能光伏系统在全球可持续能源转型中扮演着越来越重要的角色；然而，屋顶光伏系统的空间分布零散且规模较小，这对于进行准确和精细的区域潜力评估构成了挑战。为应对这一挑战，该研究构建了综合多源遥感数据和人工智能算法的评估框架，结合静止气象卫星影像和深度学习反演模型估算逐小时地表太阳辐射，同时利用高分辨率遥感影像和图像分割模型提取建筑物轮廓，并集成几何光学模型模拟光伏发电过程。该框架能够揭示太阳能资源禀赋时空差异，调查可用屋顶资源总量，并确定米级分辨率以及小时尺度的光伏发电量。江苏省的案例研究验证了该框架在大区域内应用的有效性，展示了其在不同地理位置和多个时间尺度的可扩展性。估算结果显示，江苏省屋顶资源可支撑236.25GW的光伏装机容量，年发电量可达303.81TWh，能够满足全省41.1%的社会用电量。这项研究展示了集成多源遥感观测进行屋顶光伏潜力时空评估的可能性，为推动可持续能源转型提供了有力的工具和技术支持。

Objective: Rooftop solar photovoltaic (PV) systems are becoming increasingly critical in the global shift towards sustainable energy. Despite their importance, the fragmented and small-scale spatial distribution of rooftop PV systems poses significant challenges for accurate and detailed regional potential assessments. This study aims to tackle these challenges by developing a comprehensive assessment framework that integrates multi-source remote sensing data and advanced artificial intelligence algorithms. The objective is to provide a robust methodology for evaluating the potential of rooftop PV systems on a large scale. Method: The assessment framework developed in this study leverages a combination of geostationary meteorological satellite imagery and deep learning inversion models to estimate hourly surface solar radiation. To extract building outlines accurately, high-resolution remote sensing images are processed using advanced image segmentation models. Furthermore, the framework integrates a geometric optical model to simulate the PV generation process. This holistic approach enables the precise revelation of spatial and temporal variations in solar energy resources. It also facilitates the investigation of the total available rooftop resources and the determination of PV power generation potential at meter-level resolution and hourly scales. Result: The framework"s effectiveness was validated through a case study conducted in Jiangsu Province, China. The results demonstrated the scalability and applicability of the framework across different geographic locations and multiple temporal scales. The estimation results revealed that the rooftop resources in Jiangsu Province could support a PV installed capacity of 236.25 GW, with an annual power generation potential of 303.81 TWh. This substantial output could meet 41.1% of the province"s total electricity consumption. The case study highlights the framework"s ability to provide detailed and accurate assessments of rooftop PV potential on a large scale. Conclusion: This study illustrates the feasibility and effectiveness of integrating multi-source remote sensing observations for spatiotemporal assessment of rooftop PV potential. The developed framework offers robust tools and technical support for advancing the sustainable energy transition. By providing insights into the spatial and temporal variability of solar resources, this framework paves the way for optimized utilization of rooftop PV systems. This research contributes to the broader effort of achieving sustainable energy goals by enabling more precise and large-scale assessments of rooftop PV potential.