DSMprops: Digital Soil Mapping of numeric properties

A hybrid repository including an R package for commonly used functions and project executable scripts documenting current workflows for project implementation of digital soil mapping (DSM) models in the Soil Landscapes of the United States 100m product (SOLUS100).

SOLUS (a) and gNATSGO (b) maps of clay content at 5 cm with associated SOLUS sample density (c), upper (d) and lower (e) 95% prediction intervals (PIs) and relative prediction interval (RPI, f). ## Installation

You can install the released version of DSMprops from GitHub for R functions with:

install.packages("remotes")
remotes::install_github("naumi421/DSMprops")

To get both R package functionality and access to all exec workflows, you can clone this repository locally and then build R package. Although you can just clone the repository to get all the code and be able to build the package, devtools is needed to contribute to the repository. Note that Rtools (windows) or other pre-requisites (e.g. Ubuntu) are necessary for installing devtools.

Cloning in bash:

$ cd /path/where/repo/goes
$ git clone https://github.com/naumi421/DSMprops.git #(or alternate repo URL)

Rstudio also has options for cloning a repo with good guidance available at https://happygitwithr.com/.

Once repository has been cloned. Open the R project in Rstudio and use Build > Install and Restart to install DSMprops package which can then be used in other sessions outside of the project.

Summary

This repository documents specific steps used to create the SOLUS100 dataset. The focus of the SOLUS100m development team was to better utilize various parts of the National Cooperative Soil Survey (NCSS) datasets to inform digital soil mapping of useful soil properties. Data from the Kellog Soil Survey Laboratory National Soil Characterization Database (SCD), field classified pedons from the NCSS National Soil Information System (NASIS) catalogued from previous soil survey efforts, and the Gridded National Soil Survey Geographic Database (gNATSGO) were all utilized in a hybrid training set to increase model inference. This increases sample size by ~10-15x relative to other efforts to predict soil properties across the conterminous United States (CONUS).

Please see the associated publication for further details on methods and performance of models:

Nauman, T.W., Kienast-Brown, S, White, D., Brungard, C, Roecker, S, Philippe, J., Thompson, J.A. Soil Landscapes of the United States (SOLUS): developing predictive soil property maps of the conterminous US using hybrid training sets. In Preparation for submission to Soil Science Society of America Journal.

Properties included in SOLUS100 dataset

Twenty different soil properties were mapped for SOLUS100. All properties were defined consistently with U.S. Soil Survey Geographic Database (SSURGO) standards. These properties are listed in the table below, but more detailed descriptions can be found in SSURGO documentation.

Property	SSURGO code	Units	Notes
Clay	claytotal	% mass
Silt	silttotal	% mass
Sand	sandtotal	% mass
Very fine sand	sandvf	% mass
Fine sand	sandfine	% mass
Medium sand	sandmed	% mass
Coarse sand	sandco	% mass
Very coarse sand	sandvc	% mass
Depth to bedrock	anylithicdpt*	cm	Depth to lithic of paralithic contact
Depth to restriction	resdept	cm	Depth to layer that limits air, water, or roots
pH	ph1to1h2o	-log[H+]	1:1 method
Rock fragments	fragvol	% vol.	Particles >2mm, whole soil basis
Bulk Density	dbovendry	g cm-3	Oven dry bulk density
Calcium carbonate	caco3	% mass
Gypsum	gypsum	% mass
CEC at pH7	cec7	meq / 100g	Cation Exchange Capacity (CEC) <2mm fraction
ECEC	ecec	meq / 100g	Effective CEC
Soil organic carbon	soc	% mass
Sodium Adsorption Ratio	sar	unitless	Ratio of [Na]/([Ca]+[Mg]) in Saturated Paste
Electrical conductivity	ec	dS / m	Saturated paste

*New theme created from specified lithic or paralithic restrictions to estimate bedrock depth.

General Workflow for SOLUS100

SOLUS100 process workflow for selecting training data, model building, and model performance assessment.

SOLUS100 combines several different types of training data to increaase model inference. A selection process was used to ensure that inclusion of new tiers of data did not degrade model quality. This included both incrementally adding different types and ages of data. Both source of data and uncertainty of geolocation information were assessed in a data grid test of adding progressively more data to models starting with the highest quality and incrementally adding data with more and more uncertainty. Both out-of-bag R² and a global uncertainty summery (mean relative prediction interval) were used to rank models with various combinations of training data to select the optimal tiers of training data. Random forest models were fit for every property at every depth in this fashion. Models were created for point depths (as opposed to interval) at 0, 5, 15, 30, 60, 100, and 150 cm excepting soil depth to restriction and bedrock.

Structure of DSMprops repository

This repository is structured to include both an R package (R and man folders) and workflow documentation (exec folder). The R package portion includes common functions used extensively in workflows and includes cross-validation, spatial cross-validation, weighting schemes, global uncertainty estimation, point extraction of raster values, and validation statistical summaries. The workflow folder includes a number of subfolders with scripts documenting steps taken in preparing input data, building models, and houses other supporting files.