Global Soil Water Characteristics Maps at 1 km Resolution

A global dataset of soil water retention parameters (α, n, θr, θs) based on the van Genuchten model. The maps were developed using a machine learning framework (Covariate-based GeoTransfer Functions) with remote sensing data for terrain, climate, vegetation, and soil. The dataset was created by researchers at ETH Zurich and published in 2022.

Use Cases

• Modeling soil water retention and hydraulic conductivity based on van Genuchten parameters.
• Analyzing global patterns of soil water availability using the provided 1 km resolution maps.
• Integrating soil hydraulic properties into climate or ecosystem models using terrain, climate, and vegetation covariates.
• Studying the relationship between soil water characteristics and environmental factors like climate and terrain.

Strengths

• Provides four key soil hydraulic parameters (α, n, θr, θs) for global coverage.
• Data is available at 1 km spatial resolution and for four soil depths (0, 30, 60, and 100 cm).
• Methodology leverages a large training dataset and machine learning (random forest) with multiple environmental covariates.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

ETH Zurich

Collection Method

Generated using a Covariate-based GeoTransfer Functions (CoGTF) framework and random forest machine learning.

Geography

Global