MONet Data Synthesis and Comparison

What is MONet?

The Molecular Observation Network (https://www.emsl.pnnl.gov/monet) is an ongoing project developed by the Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory, with aims to create a continential scale database of standardized soil molecular properties to advance the understanding of soil biogeochemistry. The MONet Data Synthesis Project aims to systematically compare and integrate soil data from MONet with established publicly available datasets, such as SoilGrids and the Soil Respiration Database (SRDB). This repository contains scripts to analyze and compare key soil properties (e.g., soil respiration, pH, and clay content) across known drivers of variability, with an emphasis on addressing spatial scale differences between global datasets and national-scale efforts like MONet.

Goals of the Analysis

1. Understand Soil Properties Relative to Environmental Drivers:
   • Explore soil respiration variations with mean annual precipitation (MAP) and mean annual temperature (MAT).
   • Assess the spatial distribution and variability in pH and clay content at national and sub-national levels.
   • Compare trends across subsamples of larger datasets (e.g., SoilGrids) with regional/national observations (e.g., MONet).
2. Enhance Data Accessibility:
   • Provide processed data, visualizations, and detailed scripts to the soil science, biogeochemistry, and environmental science communities.

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Repository Overview

Folder	Description
data/	Directory for storing raw and publicly available datasets.
R_data/	Directory for saving intermediate processed data
figures/	Directory for saving plots and visual outputs from the analyses.
MONetSynthesis.Rmd	R markdown file containing analysis of MONet data product.
MONetDataPreprocessing.R	R script for downloading and preprocessing data
README.md	Overview of the repository, datasets, and instructions for reproducing the analyses.

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Data Sources

This project integrates multiple high-quality datasets. A summary of their sources and attributes is given below:

Dataset	Description	Source
MONet Soil Respiration	Processed soil respiration data collected in the United States.	MONet Soil Respiration - Zenodo
MONet Clay and pH	Clay content and pH of X number of soil samples in the United States	MONet
SoilGrids	Global gridded clay content and pH at 10km resolution.	SoilGrids
SRDB	Soil respiration observations aggregated from published journal articles globally.	SRDB GitHub Repository
Global Gridded 1-km Rh	Soil heterotrophic respiration upsacled globally at 1-km reslution.	NASA Earthdata
GCAM Basin Clay Content	Clay content and summary statistics of GCAM's 232 basins	GCAM
GCAM Basin Shapefiles	Spatial boundaries of GCAM's 232 basins at 0.5 arc mins resolution in crs: EPSG:4326 WGS84 - World Geodetic System 1984	GCAM basin boundaries from moirai
Climate Zones	Koppen-Geiger classification shapefile used for grouping comparisons by climatic similarities.	North American Climate Atlas
USA Shapefiles	Spatial reference shapefiles for delineating site boundaries in the continental U.S.	NOAA GIS US States Shapefiles

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Instructions to Gather and Prepare Data

1. Download Raw Data:

   • Clone this repository and navigate to the data/ folder.
   • Download the MONet data, Climate Zones, and USA shapefiles from the sources provided above and store them in the organized subdirectories.
   • For SoilGrids and SRDB, run the get_SGdata.R and get_SRDBdata.R

2. Install Required R Packages:

   • Install all necessary R libraries used in this analysis

3. Run Data Processing Scripts

   • Open and run provided R script for data download and processing (MONetDataProcessing.R)
   • This script will take some time to run and saves a Rdata file to the directory

4. Run Analysis Scripts:

   • Open the provided R markdown (MONetSynthesis.Rmd) script.
   • Run the script to:
     • Conduct exploratory analyses comparing respiration trends, MAP/MAT relationships, etc.
     • Perform spatial and statistical comparisons on pH and clay content.

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File structure

Below is the file structure that should exist after downloading required data and running MONetDataProcessing.R

├── MONetSynthesis.Rproj
├── Morris-Wiens_MONetSynthesis.Rmd
├── R/
│   └── MONetDataPreprocessing.R
├── data/
│   ├── GCAM/
│   │   └── mapped_clay_KN.csv
│   ├── MONet/
│   │   ├── 1000S_processed_L2_summary.csv
│   │   ├── 1000Soils_Metadata_Site_Mastersheet_v1.csv
│   │   ├── pH/
│   │   │   └── processed_data/
│   │   │       ├── Column_Descriptions.xlsx
│   │   │       ├── Coordinates.csv
│   │   │       └── Soil_BioChemical_properties.csv
│   │   └── clay/
│   │       └── processed_data/
│   │           ├── Column_Descroptions.xlsx
│   │           ├── Coordinates.csv
│   │           └── Soil_BioChemical_properties.csv
│   ├── shapefiles/
│   │   ├── gcam_boundaries_moirai_3p1_0p5arcmin_wgs84/
│   │   │   └── main_outputs/
│   │   │       └── glu_boundaries_moirai_landcells_3p1_0p5arcmin.shp
│   │   ├── s_18_mr25/
│   │   │   └── s_18mr25.shp
│   │   └── na_climatezones_shapefile/
│   │       └── climatezones_shapefile/
│   │           └── NA_ClimateZones/
│   │               └── data/
│   │                   └── NorthAmerica_Climate_Zones.shp    
│   ├── soilgrids*/
│   │   ├── crop_roi_igh_clay_0-5cm.tif
│   │   ├── crop_roi_igh_clay_15-30cm.tif
│   │   ├── crop_roi_igh_ph_0-5cm.tif
│   │   └── crop_roi_igh_ph_15-30cm.tif
│   ├── SoilResp_HeterotrophicResp_1928/
│   │   └── data/
│   │       └── soil_Rh_mean.tif
│   ├── srdb*/
│   │   └── srdb-20250503a/
│   │       └── srdb-data.csv
│   └── worldclim_data*/
│       └── climate/
│           └── wc2.1_10m/
│               └── wc2.1_10m_prec_xx.tif
├── R_data*/
│   ├── processed_Rs.RData
│   ├── processed_pH.RData
│   └── processed_clay.RData
├── figures
└── README.md

*These folders contain data that is generated in MONetDataPreprocessing.R

Statistical and Comparative Focus Areas

The analysis pipeline includes:

1. Soil Respiration Analysis:

   • Relating soil carbon flux (respiration) to MAP and MAT across the U.S.
   • Identifying climatic influences on observed trends using regression.

2. pH and Clay Content Comparison:

   • Cross-referencing MONet-observed properties with SoilGrids' datasets spatially and within climatic zones.

3. Data Visualization:

   • Generating maps of sample coverage and plots comparing different datasets.