Mapping high resolution National Soil Information Grids of China

高分辨率中国国家土壤信息格网

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

现有土壤信息大多源于历史土壤调查,较为粗略、陈旧,不能满足应对粮食安全、水资源紧缺、土地退化和气候变化等全球和区域性问题的需要.中国疆域辽阔、土壤景观复杂多样,准确推测大面积复杂地区土壤变异是一大挑战.本研究基于近年我国土系调查采集的5000多个代表性土壤剖面,采用预测性土壤制图范式,研发高精度深度函数自适应拟合并集成集合式机器学习等先进方法,在高性能并行计算环境下生成了我国土壤pH、有机碳等系列关键属性90 m分辨率三维栅格分布图.量化了成土要素对土壤属性影响的相对重要性,揭示了土壤空间变异多因素综合交互的复杂性和非平稳性.研究结果大幅度提升了我国基础土壤信息水平,也对"全球土壤制图科学计划"作出了重要贡献.