Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units

Version 1 : Received: 20 September 2022 / Approved: 22 September 2022 / Online: 22 September 2022 (15:08:05 CEST)

How to cite: Ließ, M. Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units. Preprints 2022, 2022090347 (doi: 10.20944/preprints202209.0347.v1). Ließ, M. Modelling the Agricultural Soil Landscape of Germany – A Data Science Approach Involving Spatially Allocated Functional Soil Process Units. Preprints 2022, 2022090347 (doi: 10.20944/preprints202209.0347.v1).

Abstract

The national-scale evaluation and modelling of the impact of agricultural management and cli-mate change on soils, crop growth, and the environment require soil information at a spatial res-olution addressing individual agricultural fields. This manuscript presents a data science ap-proach which agglomerates the soil parameter space into a limited number of functional soil pro-cess units (SPUs) which may be used to run agricultural process models. In fact, two unsupervised classification methods were developed to generate a multivariate 3D data product consisting of SPUs, each being defined by a multivariate parameter distribution along the depth profile from 0 to 100 cm. The two methods account for differences in variable types and distributions and in-volve genetic algorithm optimization to identify those SPUs with the lowest internal variability and maximum inter-unit difference with regards to both, their soil characteristics and landscape setting. The high potential of the methods was demonstrated by applying them to the agricultural German soil landscape. The resulting data product consists of twenty SPUs. It has a 100 m raster resolution in the 2D mapping space, and its resolution along the depth profile is 1 cm. It includes the soil properties texture, stone content, bulk density, hydromorphic properties, total organic carbon content, and pH.

Keywords

digital soil mapping; soil process units; soil parameter space; machine learning; unsupervised classification

Subject

EARTH SCIENCES, Geoinformatics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.

We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.