DOI: 10.5281/zenodo.7569685
title: Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

description: <strong>Data Description</strong>: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: ├── available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]<br> ├── available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]<br> ├── caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]<br> ├── caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]<br> ├── cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]<br> ├── cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]<br> ├── clay_30arc_30cm_us.tif [30 cm depth soil clay content]<br> ├── clay_30arc_100cm_us.tif [100 cm depth soil clay content]<br> ├── depthWT_30arc_us.tif [depth to water table]<br> ├── kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]<br> ├── kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]<br> ├── ph_30arc_100cm_us.tif [100 cm depth soil pH]<br> ├── ph_30arc_100cm_us.tif [30 cm depth soil pH]<br> ├── pondingFre_30arc_us.tif [ponding frequency]<br> ├── sand_30arc_30cm_us.tif [30 cm depth soil sand content]<br> ├── sand_30arc_100cm_us.tif [100 cm depth soil sand content]<br> ├── silt_30arc_30cm_us.tif [30 cm depth soil silt content]<br> ├── silt_30arc_100cm_us.tif [100 cm depth soil silt content]<br> ├── water_content_30arc_30cm_us.tif [30 cm depth soil water content]<br> └── water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: ├──30cm SOC mean.tif [30 cm depth soil SOC]<br> ├──100cm SOC mean.tif [100 cm depth soil SOC]<br> ├──30cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]<br> └──100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data <br> <strong>Data format</strong>: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. <strong>Geospatial projection</strong>: <pre><code>GEOGCS["GCS_WGS_1984", DATUM["D_WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["Degree",0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS["wgs84", DATUM["WGS_1984", SPHEROID["WGS_1984",6378137,298.257223563]], PRIMEM["Greenwich",0], UNIT["degree",0.0174532925199433]] </code></pre> Paper describing data and methods: Wang, Z., Sihi, D., Kumar, J., Weintraub-Leff, S. R., Todd-Brown, K., and Mishra, U.,<strong> </strong>(2022)<strong> "</strong>Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning<strong>", (in review)</strong>