High-resolution soil total phosphorus mapping for the conterminous USA using machine learning

Accurate estimates of soil total phosphorus (TP) concentrations are essential for sustainable nutrient management, food security, and water quality protection. This study predicts and maps the spatial distribution of TP in the top 5 cm and C horizon of soils across the conterminous USA (CONUS) using data from the Geochemical and Mineralogical Data for Soils of the Conterminous United States. We compare the performances of random forest (RF) and inverse distance weighting (IDW) to model and generate soil TP predictions. The RF incorporates 19 predictor variables, including spatial coordinates, climate, soil properties, and topography, while IDW relies solely on coordinates and interpolates between soil TP observations. Models are evaluated using five-fold cross-validation. The RF models outperform the IDW models and explain 52 % (RMSE = 0.22 log10 mg kg -1) and 56 % (RMSE = 0.26 log10 mg kg -1) of the variance in soil TP for the top 5 cm and C horizon, respectively. As expected, bot..., , ## Overview

This dataset accompanies the manuscript titled: ***High-resolution Modeling of Soil Total Phosphorus Across the Conterminous United States***

It includes processed datasets, scripts, and results related to the spatial prediction of soil total phosphorus (TP) across the CONUS at two depths (top 5 cm and C horizon) using a hybrid Random Forest and Inverse Distance Weighting (IDW) approach.

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## Dataset Description

**File**:

1. `ds01_modeling_data.xlsx`

This Excel file contains two sheets:

* **Sheet 1**: Soil TP and covariates for the top 5 cm depth
* **Sheet 2**: Soil TP and covariates for the C horizon

### Columns (both sheets)

| Column Name | Description |
| ---------------------------------------------------------------------------- | --------------------------------------- |
| `Top5_P` | Total phosphoru..., , **Changes after Jan 16, 2026:** Raster files of the soil TP predictions for the top5 and C horizon