SoilGrids250m: Global gridded soil information based on machine learning.
SoilGrids250m: Global gridded soil information based on machine learning.
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DOI：
10.1371/journal.pone.0169748
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Kempen B
中科院分区：
文献类型：
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作者：
Hengl T;Mendes de Jesus J;Heuvelink GB;Ruiperez Gonzalez M;Kilibarda M;Blagotić A;Shangguan W;Wright MN;Geng X;Bauer-Marschallinger B;Guevara MA;Vargas R;MacMillan RA;Batjes NH;Leenaars JG;Ribeiro E;Wheeler I;Mantel S;Kempen B
本文介绍了250米分辨率（2016年6月更新）的最新改进版SoilGrids系统的技术开发和精度评估。SoilGrids为标准数值土壤属性提供全球预测（有机碳、体积密度、阳离子交换容量（CEC）、pH值、土壤质地部分和粗碎片）（0、5、15、30、60、100和200 cm），除了根据世界参考基准（WRB）和美国农业部分类系统（ca.总共280个光栅层）。预测是基于CA。用于训练的150，000个土壤剖面和158个基于遥感的土壤协变量（主要来自MODIS土地产品，SRTM DEM衍生物，气候图像以及全球地形和岩性图），用于拟合R包ranger，xgboost，nnet和caret中实施的机器学习方法-随机森林和梯度提升和/或多项式逻辑回归。10倍交叉验证的结果表明，集成模型解释56%（粗片段）和83%（pH值）之间的变化，总体平均值为61%。考虑到所解释的变化量，与1公里空间分辨率的前一版本SoilGrids相比，相对准确度的提高幅度为60%至230%。改进可归因于：（1）使用机器学习而不是线性回归，（2）在准备更高分辨率协变量层方面进行了大量投资，以及（3）插入了额外的土壤剖面。SoilGrids的进一步发展可能包括改进方法，以纳入输入不确定性和后验概率分布（每像素）的推导，以及空间建模的进一步自动化，以便可以为潜在的数百个土壤变量生成土壤图。未来研究的另一个领域是开发将SoilGrids预测与地方和/或国家网格化土壤产品（例如高达50米的空间分辨率）进行多尺度合并的方法，以便能够产生越来越准确、完整和一致的全球土壤信息。SoilGrids在开放数据库许可证下可用。
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression—as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10–fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.