Abstract
ObjectivesThis data article aims to introduce the “XPolaris” R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data from the POLARIS database into traditional spreadsheet format [i.e., Comma-Separated Values (CSV)] for further data analyses.Data descriptionThe core of this publication is a code-tutorial envisioned to assist users in retrieving soil raster data within the CONUS. All data is sourced from the POLARIS database, a 30-m probabilistic map of soil series and different soil properties [Chaney et al. Geoderma 274:54, 2016, Chaney et al. Water Resour Res 55:2916, 2019]. POLARIS represents an optimization of the Soil Survey Geographic (SSURGO) database, circumventing issues of spatial disaggregation, harmonizing, and filling spatial gaps. POLARIS was constructed using a machine learning algorithm, the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART-HPC) [Odgers et al. Geoderma 214:91, 2014]. Although the data is easily accessible in a raster format, retrieving large amounts of data can be time-consuming or require advanced programming skills.
Highlights
The objective of this dataset [1] is to introduce the R-package “XPolaris”, a collection of functions for retrieving soil data from the POLARIS database [2, 3]
Due to a large volume of data in each raster image, efficient coding is necessary to match the user need with a minimum download requirement
About 50% of corn yield variability was explained by crop management and soil variables, with previous crop and soil organic matter as the most relevant features
Summary
Objective The objective of this dataset [1] is to introduce the R-package “XPolaris”, a collection of functions for retrieving soil data from the POLARIS database [2, 3]. POLARIS raster images are accessible and a client API (Application Programming Interface) has been recently released [4], programming skills are necessary to retrieve large amounts of data. The core functionalities of XPolaris will facilitate accessing soil data regardless of the number of geographical locations.
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