ESA WorldCereal

Products used: WorldCereal Temporary Cropland Extent 2021 (raster 10 m), annual – version 1, WorldCereal Active Cropland 2021 (raster 10 m), seasonal – version 1, WorldCereal Maize – Main Season 2021 (raster 10 m), seasonal – version 1, WorldCereal Maize – Active Season 2021 (raster 10 m), seasonal – version 1, WorldCereal Maize – Irrigation 2021 (raster 10 m), seasonal – version 1, WorldCereal Winter Cereals 2021 (raster 10 m), seasonal – version 1, WorldCereal Winter Cereals – Irrigation 2021 (raster 10 m), seasonal – version 1

Keywordsdata used; WorldCereal, datasets; esa_worldcereal_temporarycrops, datasets; esa_worldcereal_activecropland, data used; esa_worldcereal_maize_main, datasets; esa_worldcereal_maize_active datasets; esa_worldcereal_maize_irrigation, datasets; esa_worldcereal_wintercereals

Background

The ESA WorldCereal project delivers a globally consistent, satellite-derived system for monitoring cropland extent, crop types, and irrigation practices. Designed for seasonal agricultural monitoring at 10-meter resolution, the system uses optical and radar data from Sentinel-1 SAR and Sentinel-2 MSI, enabling high-frequency, cloud-resilient observations across diverse agro-ecological zones. The initiative builds on earlier prototyping efforts and now provides a fully open-source, harmonized processing system (European Space Agency [ESA], 2025).

Key outputs include annual temporary cropland extent, seasonal crop type classifications (e.g., cereals and maize), irrigation status, and associated confidence layers. These products are available globally but are particularly relevant for regions with food security challenges, such as sub-Saharan Africa and South Asia. Products are mapped using agro-ecological zones to account for regional planting calendars, and the system supports the co-creation of customized models using user-supplied in-situ reference data.

All maps are provided in standardized spatial grids (EPSG:4326), with versioned outputs and quality metrics validated against ground observations. The platform also includes a global reference data module, enabling the sharing, curation, and retrieval of harmonized crop ground-truth data. Ongoing improvements in model training, algorithm optimization, and atmospheric correction enhance product accuracy and usability over time.

Digital Earth Africa (DE Africa) hosts these datasets for the African region, providing free and open access to the data. This notebook demonstrates how to load the WorldCereal datasets into the DE Africa Sandbox environment.

Summary Table

Product Code	Spatial Resolution	Time Period
esa_worldcereal_temporarycrops	10 m	2021
esa_worldcereal_activecropland	10 m	2021
esa_worldcereal_maize_main	10 m	2021
esa_worldcereal_maize_active	10 m	2021
esa_worldcereal_maize_irrigation	10 m	2021
esa_worldcereal_wintercereals	10 m	2021
esa_worldcereal_wintercereals_irrigation	10 m	2021

Measurement

Measurement	Temporary Cropland (`temporarycrops`)	Active Cropland (`activecropland`)	Maize Main (`maize_main`)	Maize Irrigation (`maize_irrigation`)	Winter Cereals (`wintercereals`)	Winter Cereals Irrigation (`wintercereals_irrigation`)
Binary Classification	Yes	Yes	Yes	Yes	Yes	Yes
Crop Type (Maize/Winter Cereal)	No	No	Yes	Yes	Yes	Yes
Irrigation Status	No	No	No	Yes	No	Yes
Confidence Layer	Yes	Yes	Yes	Yes	Yes	Yes
Agro-Ecological Zone (AEZ) Mask	Yes	Yes	Yes	Yes	Yes	Yes

Description

In this notebook we will load esa_worldcereal_**** data using dc.load()

Topics covered include:

Inspecting the esa_worldcereal_*** product available in the datacube
Using the dc.load() function to load in each esa_worldcereal_**** data
Plotting esa_worldcereal_***

Getting started

To run this analysis, run all the cells in the notebook, starting with the “Load packages” cell.

Load packages

[1]:

%matplotlib inline



import numpy as np
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
from matplotlib.colors import ListedColormap, BoundaryNorm
import geopandas as gpd
from odc.geo.geom import Geometry

import datacube
from deafrica_tools.plotting import display_map
from deafrica_tools.areaofinterest import define_area

Connect to the datacube

[2]:

dc = datacube.Datacube(app="WorldCereal")

Analysis parameters

The following cell sets the parameters, which define the area of interest to conduct the analysis over.

Select location

To define the area of interest, there are two methods available:

By specifying the latitude, longitude, and buffer, or separate latitude and longitude buffers, this method allows you to define an area of interest around a central point. You can input the central latitude, central longitude, and a buffer value in degrees to create a square area around the center point. For example, lat = 10.338, lon = -1.055, and buffer = 0.1 will select an area with a radius of 0.1 square degrees around the point with coordinates (10.338, -1.055).

Alternatively, you can provide separate buffer values for latitude and longitude for a rectangular area. For example, lat = 10.338, lon = -1.055, and lat_buffer = 0.1 andlon_buffer = 0.08 will select a rectangular area extending 0.1 degrees north and south, and 0.08 degrees east and west from the point (10.338, -1.055).

For reasonable loading times, set the buffer as 0.1 or lower.
By uploading a polygon as a GeoJSON or Esri Shapefile. If you choose this option, you will need to upload the geojson or ESRI shapefile into the Sandbox using Upload Files button in the top left corner of the Jupyter Notebook interface. ESRI shapefiles must be uploaded with all the related files (.cpg, .dbf, .shp, .shx). Once uploaded, you can use the shapefile or geojson to define the area of interest. Remember to update the code to call the file you have uploaded.

To use one of these methods, you can uncomment the relevant line of code and comment out the other one. To comment out a line, add the "#" symbol before the code you want to comment out. By default, the first option which defines the location using latitude, longitude, and buffer is being used.

If running the notebook for the first time, keep the default settings below. This will demonstrate how the analysis works and provide meaningful results. The default location is an area with various land use and land cover types covering Mozambique and Malawi.

[3]:

# Method 1: Specify the latitude, longitude, and buffer)
aoi = define_area(lat=-15.7764, lon=35.8923, buffer=0.5)

# Method 2: Use a polygon as a GeoJSON or Esri Shapefile.
# aoi = define_area(vector_path='aoi.shp')

#Create a geopolygon and geodataframe of the area of interest
geopolygon = Geometry(aoi["features"][0]["geometry"], crs="epsg:4326")
geopolygon_gdf = gpd.GeoDataFrame(geometry=[geopolygon], crs=geopolygon.crs)

# Get the latitude and longitude range of the geopolygon
lats = (geopolygon_gdf.total_bounds[1], geopolygon_gdf.total_bounds[3])
lons = (geopolygon_gdf.total_bounds[0], geopolygon_gdf.total_bounds[2])

# Define the query
query = {
    'x': lons,
    'y': lats,
    'output_crs': 'epsg:6933',
}

[4]:

# The code below renders a map that can be used to view the region.
display_map(lons, lats)

[4]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Accessing WorldCereal data through DEAfrica

Visualizing the results

The cell below shows the classification and confidence layers for the area of interest. Comparing it to the reference image produced by the display_map() function above, we can see that areas covered by Lake Chilwa and the Mulanje Mountain Forest Reserve are classified as other land cover classes (non-crop) with high confidence and cropland is dispersed through other areas with varying confidence.

[6]:

classification = esa_worldcereal_temporarycrops.isel(time=-1)['classification']
confidence = esa_worldcereal_temporarycrops.isel(time=-1)['confidence']

# --- Plotting ---
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

# Plot 1: Binary Map
cmap_binary = mcolors.ListedColormap(['red', 'green', 'lightgray'])
bounds_binary = [-50, 50, 150, 300]  # Class boundaries
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im1 = classification.plot.imshow(
    ax=ax1, cmap=cmap_binary, norm=norm_binary, add_colorbar=False
)
ax1.set_title('Classification')
ax1.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Other Land Cover/Use', markerfacecolor='red', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Temporary Crops', markerfacecolor='green', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

# Plot 2: Confidence Map
im2 = confidence.plot.imshow(
    ax=ax2, cmap='viridis', vmin=0, vmax=100, cbar_kwargs={'label': 'Confidence (%)'}
)
ax2.set_title('Confidence (0–100)')
ax2.set_ylabel('')

fig.suptitle('Temporary Cropland Extent 2021', fontsize=16, fontweight='bold')
plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_18_0.png

Visualizing the results

The distribution of cropland in the default area of interest matches that seen above. In July 2021 (winter in the default area of interest), the majority of cropland is classified as inactive.

[8]:

fig, ax = plt.subplots(figsize=(8, 6))

cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', '#DEEDCF', 'lightgray'])
bounds_binary = [-0.5, 50, 150, 254.5, 300]
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im = esa_worldcereal_activecropland.isel(time=-1)['classification'].plot.imshow(
    ax=ax,
    cmap=cmap_binary,
    norm=norm_binary,
    add_colorbar=False
)

ax.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Inactive Cropland', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Active Cropland', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Crop', markerfacecolor='#DEEDCF', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')


ax.set_title('Active Cropland Extent')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_22_0.png

Visualizing the results

In January 2021, it appears that much of the cropland was active.

[10]:

# Plot the xarray DataArray with custom colormap
fig, ax = plt.subplots(figsize=(8, 6))

cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', '#DEEDCF', 'lightgray'])
bounds_binary = [-0.5, 50, 150, 254.5, 300]
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im = esa_worldcereal_maize_active.isel(time=-1)['classification'].plot.imshow(
    ax=ax,
    cmap=cmap_binary,
    norm=norm_binary,
    add_colorbar=False
)

# Custom colorbar setup with boundaries
ax.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Inactive Cropland', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Active Cropland', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Crop', markerfacecolor='#DEEDCF', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

ax.set_title('Extent of Active Cropland during the Maize Season')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_26_0.png

4. Main-season maize cropland

The cell below demonstrates how to load the dataset using the esa_worldcereal_maize_main product code. This product refers to a maize crop map for the main maize season defined in a region. We can therefore interpret the map as relating to maize crops only. Metadata information can be found here.

0: inactive cropland non-maize
100: active cropland maize
254: no crop
255: no data

The most recent imagery from January 2021 is used in this example.

[11]:

#load the data
esa_worldcereal_maize_main = dc.load(product='esa_worldcereal_maize_main',
                                measurements=['classification','confidence'],
                                resolution = (-10, 10),
                                time = ('2021'),
                                **query)

esa_worldcereal_maize_main

Visualizing the results

We can see that maize makes up a large proportion of active cropland, shown above, for the main maize season. We might assume that the remainder of active cropland that is not maize could be planted to other summer crops. We could combine this esa_worldcereal_maize_main product with the esa_worldcereal_maize_active product to determine which areas are active and not maize.

[12]:

classification = esa_worldcereal_maize_main.isel(time=-1)['classification']
confidence = esa_worldcereal_maize_main.isel(time=-1)['confidence']

# --- Plotting ---
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

# Plot 1: Binary Map
cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', 'lightgray'])
bounds_binary = [-50, 50, 150, 255]  # Class boundaries
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im1 = classification.plot.imshow(
    ax=ax1, cmap=cmap_binary, norm=norm_binary, add_colorbar=False
)
ax1.set_title('Classification')
ax1.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Non-maize Cropland', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Maize Cropland', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

# Plot 2: Confidence Map
im2 = confidence.plot.imshow(
    ax=ax2, cmap='viridis', vmin=0, vmax=100, cbar_kwargs={'label': 'Confidence (%)'}
)
ax2.set_title('Confidence (0–100)')
ax2.set_ylabel('')

fig.suptitle('Main Maize Season Extent', fontsize=16, fontweight='bold')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_30_0.png

Visualizing the results

The image below shows active cropland that was irrigated in the maize season as well as those that were rainfed. The confidence map provides assurance to justify the output, based on a scale ranging from 0 to 100%

[14]:

classification = esa_worldcereal_maize_irrigation.isel(time=-1)['classification']
confidence = esa_worldcereal_maize_irrigation.isel(time=-1)['confidence']

# --- Plotting ---
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

# Plot 1: Binary Map
cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', '#DEEDCF', 'lightgray'])
bounds_binary = [-0.5, 50, 150, 254.5, 300]
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im1 = classification.plot.imshow(
    ax=ax1, cmap=cmap_binary, norm=norm_binary, add_colorbar=False
)
ax1.set_title('Classification')
ax1.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Rainfed', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Irrigated', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Crop', markerfacecolor='#DEEDCF', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

# Plot 2: Confidence Map
im2 = confidence.plot.imshow(
    ax=ax2, cmap='viridis', vmin=0, vmax=100, cbar_kwargs={'label': 'Confidence (%)'}
)
ax2.set_title('Confidence (0–100)')
ax2.set_ylabel('')

fig.suptitle('Irrigated Cropland Extent during the Maize Season', fontsize=16, fontweight='bold')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_34_0.png

Visualizing the results

The image below shows the winter cereals crops and other temporary crops. The confidence map provides assurance to justify the output, based on a scale ranging from 0 to 100%

[16]:

classification = esa_worldcereal_wintercereals.isel(time=-1)['classification']
confidence = esa_worldcereal_wintercereals.isel(time=-1)['confidence']

# --- Plotting ---
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

# Plot 1: Binary Map
cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', '#DEEDCF', 'lightgray'])
bounds_binary = [-0.5, 50, 150, 254.5, 300]
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im1 = classification.plot.imshow(
    ax=ax1, cmap=cmap_binary, norm=norm_binary, add_colorbar=False
)
ax1.set_title('Classification')
ax1.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Other Temporary Crop', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Winter Cereals', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Crop', markerfacecolor='#DEEDCF', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

# Plot 2: Confidence Map
im2 = confidence.plot.imshow(
    ax=ax2, cmap='viridis', vmin=0, vmax=100, cbar_kwargs={'label': 'Confidence (%)'}
)
ax2.set_title('Confidence (0–100)')
ax2.set_ylabel('')

fig.suptitle('Winter Cereals Extent', fontsize=16, fontweight='bold')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_38_0.png

7. Irrigated main cereal cropland

The cell below demonstrates how to load the dataset using the esa_worldcereal_wintercereals_irrigation product code. This product refers to a irrigated cereal map for the irrigated main cereal season defined in a region. We can therefore interpret the map as relating to cereal crops only. Metadata information can be found here..

0: Rainfed
100: Irrigated
254: No crop
255: No data

The most recent imagery from January 2021 is used in this example.

[17]:

#load the data
esa_worldcereal_wintercereals_irrigation = dc.load(product='esa_worldcereal_wintercereals_irrigation',
                                measurements=['classification','confidence'],
                                resolution = (-10, 10),
                                time = ('2021'),
                                **query)

esa_worldcereal_wintercereals_irrigation

Visualizing the results

The image below shows the cereal farms that were irrigated as well as those that were rainfed. The confidence map provides assurance to justify the output, based on a scale ranging from 0 to 100%

[18]:

classification = esa_worldcereal_wintercereals_irrigation.isel(time=-1)['classification']
confidence = esa_worldcereal_wintercereals_irrigation.isel(time=-1)['confidence']

# --- Plotting ---
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6), sharey=True)

# Plot 1: Binary Map
cmap_binary = mcolors.ListedColormap(['#FF7F7F', '#255F38', '#DEEDCF', 'lightgray'])
bounds_binary = [-0.5, 50, 150, 254.5, 300]
norm_binary = mcolors.BoundaryNorm(bounds_binary, cmap_binary.N)

im1 = classification.plot.imshow(
    ax=ax1, cmap=cmap_binary, norm=norm_binary, add_colorbar=False
)
ax1.set_title('Classification')
ax1.legend(handles=[
    plt.Line2D([0], [0], marker='o', color='w', label='Rainfed', markerfacecolor='#FF7F7F', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='Irrigated', markerfacecolor='#255F38', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Crop', markerfacecolor='#DEEDCF', markersize=10),
    plt.Line2D([0], [0], marker='o', color='w', label='No Data', markerfacecolor='lightgray', markersize=10)
], loc='lower right')

# Plot 2: Confidence Map
im2 = confidence.plot.imshow(
    ax=ax2, cmap='viridis', vmin=0, vmax=100, cbar_kwargs={'label': 'Confidence (%)'}
)
ax2.set_title('Confidence (0–100)')
ax2.set_ylabel('')

fig.suptitle('Irrigated Cereal Extent', fontsize=16, fontweight='bold')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_42_0.png

Identifying cropland classes

In this section, we use the merged WorldCereal dataset (merge_data) to separate cropland into meaningful categories. By combining information from the maize classification, irrigation classification, and active cropland classification layers, we can distinguish between:

Active Irrigated Maize: Pixels classified as maize (maize_classification = 100) and irrigated (irrigation_classification = 100).
Active Rainfed Maize: Pixels classified as maize (maize_classification = 100) and rainfed (irrigation_classification = 0).
Active Irrigated Non-Maize: Pixels classified as non-maize (maize_classification = 0) but irrigated (irrigation_classification = 100).
Active Rainfed Non-Maize: Pixels classified as non-maize (maize_classification = 0) and rainfed (irrigation_classification = 0).
Inactive Cropland: Pixels where cropland was identified (active_classification = 0), but not active in the season.

Each of these layers is extracted by applying logical conditions with .where() to the merged dataset.

[20]:

active_irrigated_maize = merge_data.isel(time=-1)['irrigation_classification'].where(
    (merge_data['irrigation_classification'] == 100) & (merge_data['maize_classification'] == 100)
)

active_rainfed_maize = merge_data.isel(time=-1)['irrigation_classification'].where(
    (merge_data['irrigation_classification'] == 0) & (merge_data['maize_classification'] == 100)
)

active_irrigated_non_maize = merge_data.isel(time=-1)['irrigation_classification'].where(
    (merge_data['irrigation_classification'] == 100) & (merge_data['maize_classification'] == 0)
)

active_rainfed_non_maize = merge_data.isel(time=-1)['irrigation_classification'].where(
    (merge_data['irrigation_classification'] == 0) & (merge_data['maize_classification'] == 0)
)

active_inactive_cropland = merge_data.isel(time=-1)['irrigation_classification'].where(
    (merge_data['active_classification'] == 0)
)

Cropland category visualization

In this step, we combine the five derived cropland layers into a single composite map. Each pixel is assigned a numeric code based on which category it belongs to:

0 → Active Irrigated Maize
1 → Active Rainfed Maize
2 → Active Irrigated Non-Maize
3 → Active Rainfed Non-Maize
4 → Inactive Cropland

We then apply a custom colormap to display each class in a distinct color:

Red → Active Irrigated Maize
Green → Active Rainfed Maize
Blue → Active Irrigated Non-Maize
Orange → Active Rainfed Non-Maize
Purple → Inactive Cropland

Finally, we overlay these categories on a single plot with a legend, providing a clear visual representation of cropland status and water management practices for maize and other crops.

[21]:

# Create a base canvas using the first layer's shape
shape = active_irrigated_maize.shape
composite = np.full(shape, np.nan)

# Assign numeric codes for each class
composite = np.where(~np.isnan(active_irrigated_maize), 0, composite)
composite = np.where(~np.isnan(active_rainfed_maize), 1, composite)
composite = np.where(~np.isnan(active_irrigated_non_maize), 2, composite)
composite = np.where(~np.isnan(active_rainfed_non_maize), 3, composite)
composite = np.where(~np.isnan(active_inactive_cropland), 4, composite)

# Define colors and labels
colors = ['red', 'green', 'blue', 'orange', 'purple']
labels = [
    'Active Irrigated Maize',
    'Active Rainfed Maize',
    'Active Irrigated Non-Maize',
    'Active Rainfed Non-Maize',
    'Inactive Cropland'
]

cmap = ListedColormap(colors)

# Plot
fig, ax = plt.subplots(figsize=(6, 6))
im = ax.imshow(composite, cmap=cmap)

# Build a custom legend
handles = [plt.matplotlib.patches.Patch(color=colors[i], label=labels[i]) for i in range(len(colors))]
ax.legend(handles=handles, bbox_to_anchor=(1.05, 1), loc='upper left')

ax.set_title("Active and Inactive Cropland: Maize Irrigation and Rainfed Patterns")
ax.axis('off')

plt.tight_layout()
plt.show()

../../../_images/sandbox_notebooks_Datasets_World_Cereal_48_0.png

Conclusion

This notebook demonstrates how to load the WorldCereal dataset in the DE Africa Sandbox. The data can be used to monitor cropland dynamics, detect seasonal changes, and compare agricultural patterns across regions. It can also support the creation of derived datasets to identify cereal and maize crops, helping track productivity trends and inform food security planning.

References

European Space Agency. (2025). Introducing WorldCereal. ESA. https://www.esa.int/Applications/Observing_the_Earth/Introducing_World_Cereal

Additional information

License: The code in this notebook is licensed under the Apache License, Version 2.0. Digital Earth Africa data is licensed under the Creative Commons by Attribution 4.0 license.

Contact: If you need assistance, please post a question on the DE Africa Slack channel or on the GIS Stack Exchange using the open-data-cube tag (you can view previously asked questions here). If you would like to report an issue with this notebook, you can file one on Github.

Compatible datacube version:

[22]:

print(datacube.__version__)

1.8.20

Last tested:

[23]:

from datetime import datetime
datetime.today().strftime('%Y-%m-%d')

[23]:

'2025-09-23'

ESA WorldCereal

Background

Summary Table

Description

Getting started

Load packages

Connect to the datacube

Analysis parameters

Select location

Accessing WorldCereal data through DEAfrica

1. Temporary cropland extent

Visualizing the results

2. Active cropland

Visualizing the results

3. Active cropland during the maize season

Visualizing the results

4. Main-season maize cropland

Visualizing the results

5. Spatial extent of irrigated cropland during the maize season

Visualizing the results

6. Winter cereal cropland

Visualizing the results

7. Irrigated main cereal cropland

Visualizing the results

Active and Inactive cropland: Maize irrigation and rainfed Patterns

Identifying cropland classes

Cropland category visualization

Conclusion

References

Additional information