Global soil saturated hydraulic conductivity map using random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution

The global Ksat map at 1 km resolution was developed by harnessing the technological advances in machine learning and availability of remotely sensed surrogate information such as terrain, climate, vegetation, and soil covariates. We merge concepts of predictive soil mapping with a large data set of Ksat measurements and local information (soil, vegetation, climate) into covariate-based “Geo Transfer Functions'' (CoGTFs) to generate global estimates of Ksat values (to highlight the impact of Geo-referenced covariates including various remote sensing maps, we use the term Geotransfer function GTF and not pedotransfer function PTF; in the latter case, typically only soil properties are used to estimate Ksat). The Ksat dataset is provided in GeoTIFF format. A total of 4 files that represent different soil depths (0, 30, 60, and 100 cm) are provided. The Ksat values are log-transformed (log10 Ksat) and cm/day was selected as a standardized unit. The Global Ksat training dataset used for this study is available here:<br> https://doi.org/10.5281/zenodo.3752721 The R code used for this study is available here:<br> https://github.com/ETHZ-repositories/Ksat_mapping_2020 For more details / to cite this dataset please use: Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., and Or, D., (2020): <strong>Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework</strong>. Journal of Advances in Modeling Earth Systems,<strong> </strong>13(4), e2020MS002242. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242 Other datasets related to this project: The Global vG training dataset is available here: 10.5281/zenodo.5547338 Examples of using this dataset to generate van Genuchten parameters maps can be found in 10.5281/zenodo.6343570. The study was supported by ETH Zurich (Grant ETH-18 18-1). We would like to thank Zhongwang Wei, Samuel Bickel and Simone Fatichi (ETH Zurich) for insightful discussions.本数据集为1公里分辨率的全球土壤饱和导水率（Ksat，soil saturated hydraulic conductivity）地图，其开发依托机器学习技术进步，以及地形、气候、植被与土壤协变量等遥感替代信息的可获取性。 我们将预测性土壤制图理念，与大规模Ksat实测数据集及局地环境信息（土壤、植被、气候）相融合，构建基于协变量的**地理传递函数（Geo Transfer Functions，CoGTFs）**，以此生成全球Ksat值估算结果。为突出包含各类遥感地图在内的地理参考协变量的影响，我们采用“地理传递函数（简称GTF）”而非**土壤传递函数（pedotransfer function，简称PTF）**这一术语；后者通常仅利用土壤属性估算Ksat。 本Ksat数据集采用GeoTIFF格式存储，共包含4个对应不同土壤深度（0、30与100厘米）的文件。 Ksat值已完成以10为底的对数变换（log10 Ksat），并选取厘米/天作为标准化计量单位。 本研究所用的全球Ksat训练数据集可通过以下链接获取： https://doi.org/10.5281/zenodo.3752721 本研究使用的R语言代码可通过以下链接获取： https://github.com/ETHZ-repositories/Ksat_mapping_2020 如需获取更多细节或引用本数据集，请使用以下著录格式：Gupta, S., Lehmann, P., Bonetti, S., Papritz, A., 与 Or, D., (2020): **Global prediction of soil saturated hydraulic conductivity using random forest in a Covariate-based Geo Transfer Functions (CoGTF) framework**，*Journal of Advances in Modeling Earth Systems*，13(4), e2020MS002242。https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002242 本项目相关的其他数据集：全球范·盖努赫滕（van Genuchten）训练数据集可通过以下链接获取：10.5281/zenodo.5547338；利用本数据集生成范·盖努赫滕参数地图的示例可在10.5281/zenodo.6343570中查阅。 本研究由苏黎世联邦理工学院（ETH Zurich）资助（项目编号：ETH-18 18-1）。 我们感谢Zhongwang Wei、Samuel Bickel与Simone Fatichi（苏黎世联邦理工学院）提供的富有启发性的学术讨论。