土壤谱学感测与数字制图研究进展

Advances in soil spectroscopic sensing and digital mapping

关键词:  土壤信息测绘  数字土壤制图  人工智能  遥感  农业可持续发展

Key words:  soil information surveying and mapping  digital soil mapping  artificial intelligence  remote sensing  sustainable agricultural development

摘要:

【目的】 土壤作为农业生产的核心载体，受气候变化与人类活动双重影响，其属性呈现高度的时空异质性，为精准化的土壤信息获取与精细化的土地管理带来了全新挑战。传统土壤调查方式在信息获取效率与空间表达精度方面仍存在局限，亟需借助新技术实现信息增益与价值提升。【方法】 文章系统梳理了人工智能驱动下土壤信息“测”与“绘”的相关研究进展，围绕表征成土因素及过程的地理大数据构建分析框架，从土壤属性高效感知、土壤空间分布智能建模及模型可解释性等方面对现有研究进行综合归纳与评述。【结果】 新型遥感观测、光谱测量及算法模型的快速发展，有效推动了土壤属性信息的高效获取与空间表达。随着数据驱动推断与过程机理表征的深度融合，土壤信息获取正从经验统计向可解释、可推理的知识表达演进。【结论】 未来土壤信息测绘将围绕智能融合、知识驱动与开放共享等方向构建计算体系，推动土壤信息从静态制图向过程认知和智能决策拓展，为发展智慧土地管理与保障生态安全提供有力支撑。

Abstract:

[Purpose] Soil，as the fundamental medium for agricultural production，is jointly influenced by climate change and human activities，which results in pronounced spatiotemporal heterogeneity of soil properties and poses new challenges to precise soil information acquisition and refined land management. Conventional soil survey approaches remain limited in terms of information acquisition efficiency and spatial representation accuracy，highlighting the urgent need for emerging technologies to enhance information gain and application value.[Method] Against this background，this study systematically reviewed recent advances in artificial intelligence-driven soil information "measurement" and "mapping". Focusing on geospatial big data representing soil-forming factors and processes，an analytical framework was constructed to comprehensively synthesize and evaluate existing studies from the perspectives of efficient soil property sensing，intelligent modeling of soil spatial distribution，and model interpretability.[Result] Rapid developments in novel remote sensing observations，spectroscopic measurements，and algorithmic models effectively promoted the efficient acquisition of soil property information and improved the accuracy of spatial representation. With the deep integration of data-driven inference and process-based understanding，soil information acquisition has gradually evolved from empirical statistical approaches toward interpretable and inferable knowledge representations.[Conclusion] Looking forward，soil information mapping will focus on building computational frameworks characterized by intelligent data integration，knowledge-driven modeling，and open data sharing. This transformation supports the transition of soil information from static mapping toward process-oriented understanding and intelligent decision-making，providing a solid foundation for the development of smart land management and ecological security.