Hengl Tomislav, Mendes de Jesus Jorge, Heuvelink Gerard B M, Ruiperez Gonzalez Maria, Kilibarda Milan, Blagotić Aleksandar, Shangguan Wei, Wright Marvin N, Geng Xiaoyuan, Bauer-Marschallinger Bernhard, Guevara Mario Antonio, Vargas Rodrigo, MacMillan Robert A, Batjes Niels H, Leenaars Johan G B, Ribeiro Eloi, Wheeler Ichsani, Mantel Stephan, Kempen Bas
ISRIC - World Soil Information, Wageningen, the Netherlands.
Faculty of Civil Engineering, University of Belgrade, Belgrade, Serbia.
PLoS One. 2017 Feb 16;12(2):e0169748. doi: 10.1371/journal.pone.0169748. eCollection 2017.
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.
本文介绍了土壤网格系统（SoilGrids）最新改进版本（2016年6月更新）在250米分辨率下的技术发展及精度评估。土壤网格系统可对七个标准深度（0、5、15、30、60、100和200厘米）的标准数字土壤属性（有机碳、容重、阳离子交换容量（CEC）、pH值、土壤质地组分和粗碎屑）进行全球预测，此外还能预测基岩深度以及基于世界土壤资源参考基础（WRB）和美国农业部（USDA）分类系统的土壤类别分布（总共约280个栅格图层）。预测基于约150,000个用于训练的土壤剖面以及由158个基于遥感的土壤协变量组成的数据集（主要源自MODIS陆地产品、SRTM数字高程模型衍生物、气候图像以及全球地形和岩性地图），这些协变量被用于拟合一系列机器学习方法——随机森林、梯度提升和/或多项式逻辑回归，具体通过R软件包ranger、xgboost、nnet和caret来实现。十折交叉验证结果表明，这些集成模型对变异的解释率在56%（粗碎屑）至83%（pH值）之间，总体平均为61%。与之前1公里空间分辨率的土壤网格系统版本相比，考虑到变异解释量的相对精度提升幅度在60%至230%之间。精度提升可归因于：（1）使用机器学习而非线性回归；（2）在准备更高分辨率协变量图层方面的大量投入；（3）增加了土壤剖面。土壤网格系统的进一步发展可能包括完善方法以纳入输入不确定性并推导后验概率分布（逐像素），以及进一步实现空间建模自动化，从而能够生成潜在数百种土壤变量的土壤图。未来研究的另一个领域是开发将土壤网格系统预测与本地和/或国家栅格土壤产品（例如高达50米空间分辨率）进行多尺度合并的方法，以便生成更准确、完整和一致的全球土壤信息。土壤网格系统依据开放数据库许可提供。
