soilgrids

soilgrids provides a set of functions that allow downloading of the global gridded soil information from SoilGrids, a system for global digital soil mapping to map the spatial distribution of soil properties across the globe.

soilgrids also includes a Basic Model Interface (BMI), which converts the SoilGrids dataset into a reusable, plug-and-play data component (pymt_soilgrids) for the PyMT modeling framework developed by Community Surface Dynamics Modeling System (CSDMS).

If you have any suggestion to improve the current function, please create a github issue here.

Install package

Stable Release

The soilgrids package and its dependencies can be installed with pip

$ pip install soilgrids

or with conda.

$ conda install -c conda-forge soilgrids

From Source

After downloading the source code, run the following command from top-level folder to install soilgrids.

$ pip install -e .

Quick Start

Below shows how to use two methods to download the SoilGrids datasets.

You can learn more details from the tutorial notebook. To run this notebook, please go to the CSDMS EKT Lab and follow the instruction in the "Lab notes" section.

Example 1: use SoilGrids class to download data (Recommended method)

import matplotlib.pyplot as plt
from soilgrids import SoilGrids

# get data from SoilGrids
soil_grids = SoilGrids()
data = soil_grids.get_coverage_data(
    service_id="phh2o",
    coverage_id="phh2o_0-5cm_mean",
    west=-1784000,
    south=1356000,
    east=-1140000,
    north=1863000,
    crs="urn:ogc:def:crs:EPSG::152160",
    output="test.tif",
)

# show metadata
for key, value in soil_grids.metadata.items():
    print(f"{key}: {value}")


# plot data
data.plot(figsize=(9, 5))
plt.title("Mean pH between 0 and 5 cm soil depth in Senegal")

Example 2: use BmiSoilGrids class to download data (Demonstration of how to use BMI)

import matplotlib.pyplot as plt
import numpy as np

from soilgrids import BmiSoilGrids


# initiate a data component
data_comp = BmiSoilGrids()
data_comp.initialize("config_file.yaml")

# get variable info
var_name = data_comp.get_output_var_names()[0]
var_unit = data_comp.get_var_units(var_name)
var_location = data_comp.get_var_location(var_name)
var_type = data_comp.get_var_type(var_name)
var_grid = data_comp.get_var_grid(var_name)

print(f"{var_name=}")
print(f"{var_unit=}")
print(f"{var_location=}")
print(f"{var_type=}")
print(f"{var_grid=}")

# get variable grid info
grid_rank = data_comp.get_grid_rank(var_grid)

grid_size = data_comp.get_grid_size(var_grid)

grid_shape = np.empty(grid_rank, int)
data_comp.get_grid_shape(var_grid, grid_shape)

grid_spacing = np.empty(grid_rank)
data_comp.get_grid_spacing(var_grid, grid_spacing)

grid_origin = np.empty(grid_rank)
data_comp.get_grid_origin(var_grid, grid_origin)

print(f"{grid_rank=}")
print(f"{grid_size=}")
print(f"{grid_shape=}")
print(f"{grid_spacing=}")
print(f"{grid_origin=}")

# get variable data
data = np.empty(grid_size, var_type)
data_comp.get_value(var_name, data)
data_2D = data.reshape(grid_shape)

# get X, Y extent for plot
min_y, min_x = grid_origin
max_y = min_y + grid_spacing[0] * (grid_shape[0] - 1)
max_x = min_x + grid_spacing[1] * (grid_shape[1] - 1)
dy = grid_spacing[0] / 2
dx = grid_spacing[1] / 2
extent = [min_x - dx, max_x + dx, min_y - dy, max_y + dy]

# plot data
fig, ax = plt.subplots(1, 1, figsize=(9, 5))
im = ax.imshow(data_2D, extent=extent)
fig.colorbar(im)
plt.xlabel("X")
plt.ylabel("Y")
plt.title("Mean pH between 0 and 5 cm soil depth in Senegal")

# finalize data component
data_comp.finalize()