Saturated hydraulic conductivity (Ksat) is a key soil hydraulic parameter for representing infiltration and drainage in land surface models. For large scale applications, Ksat is often estimated from pedotransfer functions (PTFs) based on easy‐to‐measure soil properties like soil texture and bulk density. The reliance of PTFs on data from uniform arable lands and the omission of soil structure limits the applicability of texture‐based predictions of Ksat in vegetated lands. To include effects of terrain, climate, and vegetation in the derivation of a new global Ksat map at 1 km resolution, we harness technological advances in machine learning and availability of remotely sensed surrogate information. For model training and testing, a global compilation of 6,814 geo‐referenced Ksat measurements from the literature was used. The accuracy assessment based on spatial cross‐validation shows a concordance correlation coefficient (CCC) of 0.16 and a root mean square error (RMSE) of 1.18 for log10 Ksat values in cm/day (CCC = 0.79 and RMSE = 0.72 for non‐spatial cross‐validation). The generated maps of Ksat represent spatial patterns of soil formation processes more distinctly than previous global maps of Ksat based on easy‐to‐measure soil properties. The validation of the model indicates that Ksat could be modeled without bias using Covariate‐based GeoTransfer Functions (CoGTFs) that harness spatially distributed surface and climate attributes, compared to soil information based PTFs. The relatively poor performance of all models in the validation (low CCC and high RMSE) highlights the need for the collection of additional Ksat values to train the model for regions with sparse data.

中文翻译：

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基于协变量的GeoTransfer Function（CoGTF）框架中使用随机森林的土壤饱和导水率的全球预测

饱和导水率（Ksat）是代表土地表面模型入渗和排水的关键土壤水力参数。对于大规模应用，通常根据易于测量的土壤特性（如土壤质地和堆积密度），根据pedotransfer函数（PTF）估算Ksat。PTF对均匀耕地数据的依赖以及土壤结构的遗漏，限制了基于植被的Ksat的基于纹理的预测的适用性。为了在1 km分辨率的新全球Ksat地图的推导中包括地形，气候和植被的影响，我们利用了机器学习和遥感替代信息的可用性方面的技术进步。为了进行模型训练和测试，使用了来自文献的6,814个地理参考Ksat测量值的全球汇编。基于空间交叉验证的准确性评估显示，log10 Ksat值的以厘米/天为单位的一致性相关系数（CCC）为0.16，均方根误差（RMSE）为1.18（对于非空间，CCC = 0.79和RMSE = 0.72交叉验证）。基于易于测量的土壤特性，生成的Ksat图比以前的Ksat全局图更清楚地表示了土壤形成过程的空间格局。该模型的验证表明，与基于土壤信息的PTF相比，可以利用基于空间分布的表面和气候属性的基于协变量的GeoTransfer函数（CoGTF）对Ksat进行建模而不会产生偏差。