Global maps of soil water characteristics parameters developed using the random forest in a Covariate-based GeoTransfer Functions (CoGTF) framework at 1 km resolution
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The global soil water characteristics parameters (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) 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 (<em>α, n, θ<sub>r</sub>, and θ<sub>s</sub></em>) 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 <em>α</em> Inverse air entry pressure Log<sub>10</sub><em>α (m<sup>-1</sup>)</em> <em>n</em> Shape parameter Log<sub>10</sub><em>n </em>(dimensionless) <em>θ<sub>r</sub></em> Residual water content m<sup>3</sup>/m<sup>3</sup> <em>θ<sub>s</sub></em> Saturated water content m<sup>3</sup>/m<sup>3</sup> 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 <strong>Remote Sensing.</strong> 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.
