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:
https://doi.org/10.5281/zenodo.3752721

The R code used for this study is available here:
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): 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

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.