Recent progress and future prospect of digital soil mapping： A review

To deal with the global and regional issues including food security, climate change, land degradation, biodiversity loss, water resource management, and ecosystem health, detailed accurate spatial soil information is urgently needed. This drives the worldwide development of digital soil mapping. In recent years, significant progresses have been made in different aspects of digital soil mapping. The main purpose of this paper is to provide a review for the major progresses of digital soil mapping in the last decade. First, we briefly described the rise of digital soil mapping and outlined important milestones and their influence, and main paradigms in digital soil mapping. Then, we reviewed the progresses in legacy soil data, environmental covariates, soil sampling, predictive models and the applications of digital soil mapping products. Finally, we summarized the main trends and future prospect as revealed by studies up to now. We concluded that although the digital soil mapping is now moving towards mature to meet various demands of soil information, challenges including new theories, methodologies and applications of digital soil mapping, especially for highly heterogeneous and human-affected environments, still exist and need to be addressed in the future.

Predicting soil depth in a large and complex area using machine learning and environmental correlations

Soil depth is critical for eco-hydrological modeling,carbon storage calculation and land evaluation.However,its spatial variation is poorly understood and rarely mapped.With a limited number of sparse samples,how to predict soil depth in a large area of complex landscapes is still an issue.This study constructed an ensemble machine learning model,i.e.,quantile regression forest,to quantify the relationship between soil depth and environmental conditions.The model was then combined with a rich set of environmental covariates to predict spatial variation of soil depth and straightforwardly estimate the associated predictive uncertainty in the 140000 km^(2)Heihe River basin of northwestern China.A total of 275 soil depth observation points and 26 covariates were used.The results showed a model predictive accuracy with coefficient of determination(R)of 0.587 and root mean square error(RMSE)of 2.98 cm(square root scale),i.e.,almost 60% of soil depth variation explained.The resulting soil depth map clearly exhibited regional patterns as well as local details.Relatively deep soils occurred in low lying landscape positions such as valley bottoms and plains while shallow soils occurred in high and steep landscape positions such as hillslopes,ridges and terraces.The oases had much deeper soils than outside semi-desert areas,the middle of an alluvial plain had deeper soils than its margins,and the middle of a lacustrine plain had shallower soils than its margins.Large predictive uncertainty mainly occurred in areas with a lack of soil survey points.Both pedogenic and geomorphic processes contributed to the shaping of soil depth pattern of this basin but the latter was dominant.This findings may be applicable to other similar basins in cold and arid regions around the world.

基于不同模型的区域尺度耕地表层土壤有机质空间分布预测

研究不同模型对土壤有机质空间预测的性能差异对制定更加科学合理的采样策略、提升采样效率和提高土壤空间预测精度有着重要的指导意义。本研究将6496个土壤样点按8∶2的比例分层随机分成训练集与验证集,应用普通克里格、随机森林以及随机森林-回归克里格三种有代表性的数字化土壤制图(Digital Soil Mapping,DSM)模型,对河南省许昌市耕地表层土壤有机质含量及空间分布进行预测,对三种模型性能表现进行综合评价。三种模型输出的预测结果显示:研究区耕地表层土壤有机质含量水平一般,均值为18.70~18.81 g kg^(-1),变异系数0.15~0.17,属中等强度变异;空间分布总体格局为西北与西南部分山地褐土区、东南部砂姜黑土区表层有机质含量高,中北部脱潮土、石灰性潮土区表层有机质含量低。验证结果表明:三种模型性能表现无明显差距,预测精度基本一致,输出结果对研究区耕地表层土壤有机质变异解释百分比在33%~34%之间,在相同和相近尺度土壤有机质空间预测案例研究里属中等水平。在协变量有限且样点分布较为均匀的情况下,普通克里格模型便于快速获得研究区目标变量的空间分布;如果协变量比较丰富且易于收集利用,或是进行空间预测的同时还需要甄别不同因素对目标变量的影响大小,则建议采用随机森林模型;协变量有限,但样点密度较大时,随机森林-回归克里格模型可能是对目标变量进行空间预测的不错选择。

高分辨率中国国家土壤信息格网

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.