Multisource Remote Sensing Data Collaboration: Technological Progress, Challenges, and Prospects for Intelligent Applications

As remote sensing technology continues to evolve rapidly, multisource data collaboration has become a research hotspot in the field of Earth observation. Compared to single-source approaches in environmental monitoring, disaster assessment, precision agriculture, and other domains, multisource remote sensing demonstrates significant advantages in monitoring reliability and complex scenario interpretation capabilities. This paper systematically reviews the theoretical advances, typical applications, and future challenges of multi-source remote sensing technology. First, we analyze the complementary advantages of multisource remote sensing data in spatial-spectral resolution and terrain characterization capabilities, including hyperspectral, multispectral, and synthetic aperture radar (SAR). Next, we explore algorithmic frameworks for pixel-level, feature-level, and decision-level fusion, as well as current trends in multi-modal remote sensing foundational models. Furthermore, this paper identifies key technical challenges, such as the heterogeneity of multisource data, complexity of cross-modal feature association, insufficient model generalization, and computational efficiency bottlenecks. Finally, we provide an in-depth analysis of future research directions to advance theoretical foundations and technical solutions in remote sensing information science.