大模型作为人工智能发展的新浪潮，对于科研范式、生产方式、产业模式的革命性影响不可低估，投入大模型研究已是必然选择。在地理智能计算领域，大模型的科学设计与应用实践还相去甚远。本文秉承“解构复杂地表系统，求解精准土地参数”宗旨，提出在多源多模态观测数据支撑下开展土地空间对象化建模。在此基础上，梳理了土地利用、土地覆盖变化、土壤、土地资源、土地类型/应用等五“土”合一的土地空间参数体系，并针对参数的大规模求解设计了集“符号系统-感知系统-控制系统”三个核心系统于一体的遥感大模型。以农业生产空间的土地利用参数求解为应用案例开展初步实验，实践表明所提框架思路在提升土地空间大规模参数精准解算方面具有较大潜力，有助于服务精细化土地信息产品的智能定制，深化对土地空间的认知。最后，从模型的适应性/稳健性、结果的可解释性/可信度等方面对土地空间参数计算的大模型研究进行了展望。

As a new trend in the development of artificial intelligence (AI), the revolutionary impact of large models (LMs) on scientific research paradigms, production methods, and industrial models cannot be underestimated. Investing in LM research is an inevitable choice. In the field of geographic intelligent computing, there is still a long way to go between the scientific design and practical application of LMs. This article adheres to the principle of deconstructing complex land surface systems and solving precise land parameters. It proposes to carry out land spatial object-oriented modeling supported by multi-source and multimodal observation data. On this basis, we outline the land spatial parameter system and the solution framework via the integration of five-land-parameters from land use, land cover change, land soil, land resource, land type/application. Furthermore, an intelligent computing remote sensing LM is designed for large-scale parameter solving via integrating three core systems, namely symbol system, perception system, and control system. A preliminary experiment is conducted using the solution of land use parameters in agricultural production spaces as an application case. The practice showed that the proposed framework has great potential in improving the accuracy of large-scale parameter calculation in land space. The proposed model helps to serve the intelligent customization of refined land information products and deepen the understanding of land space. Finally, prospects for LM research on land spatial parameter calculation are presented from the perspectives of model adaptability/robustness, and interpretability/credibility of results.