Soil properties dataset

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Published March 10, 2022 | Version v1
Dataset Open

Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution

Authors/Creators

  • 1. Soil and Terrestrial Environmental Physics, Department of Environmental Systems Science, ETH Zurich, Switzerland
  • 2. OpenGeoHub Foundation, Wageningen, the Netherlands
  • 3. Soil Physics and Land Management Group, Wageningen University, Wageningen, The Netherlands
  • 4. Division of Hydrologic Sciences, Desert Research Institute, Reno, NV, USA

Description

The global soil water characteristics parameters (α, n, θr, and θs) maps based on van Genuchten (vG) model 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 vG parameters and local information (soil, vegetation, climate) into "Covariate-based GeoTransfer Functions'' (CoGTFs) to generate global estimates of vG  parameters (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 predict vG parameters).

The vG parameters (α, n, θr, and θs)  dataset is provided in GeoTIFF format. A total of 16 files that represent different soil depths (0, 30, 60, and 100 cm) are provided for each parameter.

Description of vG parameters and their units
vG Parameters Description units
α Inverse air entry pressure Log10α (m-1)
n Shape parameter Log10n (dimensionless)
θr Residual water content m3/m3
θs Saturated water content m3/m3

 

The Global vG training dataset used for this study is available here:

https://doi.org/10.5281/zenodo.5547338

For more details / to cite this dataset please use:

  • Gupta, S., Papritz, A., Lehmann, P., Hengl, T., Bonetti, S., and Or, D., (2020): Global mapping of soil water characteristics parameters – fusing curated data with machine learning and environmental covariates”. Manuscript submitted to Remote Sensing.

The study was supported by ETH Zurich (Grant ETH-18 18-1). We thank Zhongwang Wei, Associate professor at Sun Yat-Sen University, for helping to collect the datasets and for insightful discussions. We would like to thank Andrea Carmintai, Professor at ETH Zurich, for the insightful discussions.

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