Sentinel-3 OLCI Land

Keywords data used; sentinel-3,:index:datasets; sentinel-3

Background

The Sentinel-3 OLCI Level-2 Land Full Resolution (OL_2_LFR) product provides atmospherically corrected observations and biophysical parameters derived from the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A and 3B satellites. The product delivers consistent measurements of vegetation condition and atmospheric properties at 300-m resolution with an approximately daily revisit frequency with both satellites combined, making it valuable for a wide range of environmental and agricultural applications, including:

  • Vegetation health and stress monitoring

  • Drought early warning and food security assessments

  • Land surface process and ecosystem modelling

  • Crop condition analysis and agricultural productivity studies

  • Phenology and seasonal dynamics monitoring

  • Climate and atmospheric studies (via integrated water vapour)

  • Long-term land cover and change mapping

The algorithms and retrieval approaches have been developed by the European Space Agency (ESA) and partners, ensuring continuity with MERIS heritage and improved spectral and radiometric performance. Validation draws on ground-based measurements and inter-comparison with other satellite products. More information on the product can be found in the OLCI Land User Guide and Algorithm Theoretical Basis Documents (ATBDs). ***

Description

This notebook will cover following topics:

  1. Inspecting the products and measurements available in the datacube

  2. Loading Sentinel-3 OLCI Data.

  3. Plotting the results

Getting started

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

Load packages

Import Python packages that are used for the analysis.

[1]:

%matplotlib inline

import datacube
import geopandas as gpd
from odc.geo.geom import Geometry

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

Connect to the datacube

Connect to the datacube so we can access DE Africa data.

[2]:

dc = datacube.Datacube(app="Sentinel_3")

List products

We can use datacube’s list_products functionality to inspect DE Africa’s products that are available in the datacube. The table below shows the product names that we will use to load the data, a brief description of the data, and the satellite instrument that acquired the data.

[3]:

dc.list_products().loc[dc.list_products()['description'].str.contains('Sentinel-3')]

[3]:
name description license default_crs default_resolution
name
s3_ol_2_wfr_nrt s3_ol_2_wfr_nrt Sentinel-3 Level 2 Water Full Resolution (WFR)... CC-BY-4.0 EPSG:4326 (-0.003, 0.003)
s3_olci_l2_lfr s3_olci_l2_lfr Sentinel-3 OLCI L2 LAND CC-BY-4.0 EPSG:4326 (-0.003, 0.003)

Please choose the product by specifying its name, with the product s3_olci_l2_lfr

[4]:

product = "s3_olci_l2_lfr"

List measurements

We can further inspect the data available for the Sentinel-3 Land s3_olci_l2_lfr product using datacube’s list_measurements functionality. The table below lists each of the measurements available in the data.

[5]:

measurements = dc.list_measurements()
measurements.loc[product]

[5]:
name dtype units nodata aliases flags_definition add_offset scale_factor
measurement
GIFAPAR GIFAPAR uint16 1 65535 [GI-FAPAR] NaN 0.0 10000.0
IWV_L IWV_L uint8 kg/m² 255 [water_vapour] NaN 0.0 1.0
OTCI OTCI uint16 1 65535 [chlorophyll] NaN 0.0 10000.0
RC681 RC681 uint16 1 65535 NaN NaN 0.0 10000.0
RC865 RC865 uint16 1 65535 NaN NaN 0.0 10000.0
LQSF LQSF float32 1 -1 NaN NaN 0.0 1.0
dataMask dataMask uint8 1 255 [mask] NaN 0.0 1.0

The Sentinel-3 Land product has seven measurements:

Measurement

Description

Application

GIFAPAR (Green Instantaneous FAPAR)

Fraction of Absorbed Photosynthetically Active Radiation (green component) at observation time. Indicates photosynthetic energy uptake.

Vegetation productivity monitoring, drought early warning, crop and forest condition assessment.

IWV_L (Integrated Water Vapour – Land)

Column-integrated water vapour content above each pixel.

Climate modelling, evapotranspiration studies, vegetation stress analysis.

OTCI (OLCI Terrestrial Chlorophyll Index)

Index sensitive to canopy chlorophyll content derived from OLCI spectral bands.

Crop stress detection, canopy functioning studies, biomass dynamics monitoring.

RC681 (Rectified Reflectance 681 nm)

Atmospherically corrected surface reflectance in the red band (681 nm).

Input for vegetation indices and biophysical parameter retrievals.

RC865 (Rectified Reflectance 865 nm)

Atmospherically corrected surface reflectance in the NIR band (865 nm).

Used with RC681 for GIFAPAR; vegetation monitoring and photosynthetic activity modelling.

LQSF (Land Quality & Science Flags)

Per-pixel quality and classification information (land/water/cloud/snow).

Masking invalid pixels, cloud/snow filtering, ensuring robust time-series analysis.

dataMask

Binary mask indicating valid vs. invalid observations.

Excluding missing/invalid values, ensuring reliable data analysis.

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:

  1. 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.

  2. 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 29559d50d5f5489f8bbcf13885620494 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 example covers a central Congo Basin belt (evergreen lowland rainforest) in Democratic Republic of the Congo (DRC), northeastern part of the country—within Tshopo Province. This region is characterised by frequent, heavy cloud cover so is often difficult to monitor effectively with optical missions that have lower revisit frequency, such as the Sentinel and Landsat missions.

To run the notebook for a different area, make sure Sentinel-3 OLCI Land data is available for the chosen area using the DEAfrica Explorer.

[6]:

# Method 1: Specify the latitude, longitude, and buffer)
aoi = define_area(lat=1.066, lon=26.455, buffer=0.9)

# 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
lat_range = (geopolygon_gdf.total_bounds[1], geopolygon_gdf.total_bounds[3])
lon_range = (geopolygon_gdf.total_bounds[0], geopolygon_gdf.total_bounds[2])

[7]:

display_map(x=lon_range, y=lat_range)

[7]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Load Sentinel-3 dataset using dc.load()

Now that we know what products and measurements are available for the product, we can load data from the datacube using dc.load. We will load data from spectral satellite bands. By specifying output_crs='EPSG:4326' and resolution=(-0.003, -0.003), we request that datacube reproject our data to the global geographic coordinate reference system (CRS), with 300 x 300 m pixels. Finally, group_by='solar_day' ensures that overlapping images taken within seconds of each other as the satellite passes over are combined into a single time step in the data. We can see that the query returns eight time steps as we have queried the first eight days of April 2025.

[8]:

query = {
    'x': (lon_range),
    'y': (lat_range),
    'time':('2025-04-01', '2025-04-30'),
    'output_crs': 'EPSG:4326',
    'resolution': (-0.003, 0.003)}

ds_S3 = dc.load(product=product,
                group_by="solar_day",
                **query)

ds_S3

[8]:

<xarray.Dataset> Size: 152MB
Dimensions:      (time: 30, latitude: 601, longitude: 601)
Coordinates:
  * time         (time) datetime64[ns] 240B 2025-04-01T04:00:49 ... 2025-04-3...
  * latitude     (latitude) float64 5kB 1.966 1.964 1.96 ... 0.1695 0.1665
  * longitude    (longitude) float64 5kB 25.56 25.56 25.56 ... 27.35 27.35 27.36
    spatial_ref  int32 4B 4326
Data variables:
    GIFAPAR      (time, latitude, longitude) uint16 22MB 0 6732 6969 ... 0 0 0
    IWV_L        (time, latitude, longitude) uint8 11MB 0 44 43 43 ... 0 0 0 0
    OTCI         (time, latitude, longitude) uint16 22MB 0 24311 24823 ... 0 0 0
    RC681        (time, latitude, longitude) uint16 22MB 0 383 372 376 ... 0 0 0
    RC865        (time, latitude, longitude) uint16 22MB 0 3549 3667 ... 0 0 0
    LQSF         (time, latitude, longitude) float32 43MB 8.0 1.678e+07 ... 8.0
    dataMask     (time, latitude, longitude) uint8 11MB 0 1 1 1 1 ... 1 0 0 0 0
Attributes:
    crs:           EPSG:4326
    grid_mapping:  spatial_ref

⚠️ Note: Some Sentinel-3 OLCI Land bands are stored as integers with a scale factor (e.g., 0.0001) or offset (e.g., 1000) applied. This scaling is used to efficiently store values in integer format. To obtain the correct surface values, the scale or offset must be reversed. The example below demonstrates how to apply this correction. In this case, a scale factor of 10,000 applies to several Sentinel-3 measurements as shown in the output of the List Measurements section above. We can either divide values by 10,000 or multiply by 0.0001, as below.

Masking

Before visualisation, we use the dataMask band to mask values affected by cloud or other issues. The code below keeps data for pixels where the data mask value is 1.

[9]:

ds_S3 = ds_S3.where(ds_S3.dataMask == 1)

The cell below visualizes the Fraction of Absorbed Photosynthetically Active Radiation (green component) at the time of observation, which represents photosynthetic energy uptake. Values range from 0 to 1.

[10]:

(ds_S3['GIFAPAR'].isel(time=[0, 2, 9, 15])*0.0001).plot(robust=True, col="time", col_wrap=4);
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Land_27_0.png

The cell below visualizes the OLCI Terrestrial Chlorophyll Index (OTCI), an index sensitive to canopy chlorophyll content derived from OLCI spectral bands. It provides information on vegetation health and photosynthetic activit

[11]:

(ds_S3.isel(time=[0, 2, 9, 15])['OTCI']*0.0001).plot(robust=True, col="time", col_wrap=4)

[11]:

<xarray.plot.facetgrid.FacetGrid at 0x7fa00b306d80>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Land_29_1.png

The cell below visualizes the atmospherically corrected surface reflectance in the red band (681 nm). This measurement is used in vegetation indices and biophysical parameter retrievals,

[12]:

(ds_S3.isel(time=[0, 2, 9, 15])['RC681']*0.0001).plot(robust=True, col="time", col_wrap=4)

[12]:

<xarray.plot.facetgrid.FacetGrid at 0x7fa0011924e0>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Land_31_1.png

The cell below visualizes the atmospherically corrected surface reflectance in the near-infrared band (865 nm). This measurement, used alongside the red band (681 nm), supports vegetation monitoring and photosynthetic activity modelling,

[13]:

(ds_S3.isel(time=[0, 2, 9, 15])['RC865']*0.0001).plot(robust=True, col="time", col_wrap=4)

[13]:

<xarray.plot.facetgrid.FacetGrid at 0x7fa00b349f70>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Land_33_1.png

The cell below visualizes the column-integrated water vapour content above each pixel. This parameter represents the total amount of water vapour in the atmosphere, expressed in kg/m².

[14]:

(ds_S3.isel(time=[0, 2, 9, 15])['IWV_L']).plot(robust=True, col="time", col_wrap=4)

[14]:

<xarray.plot.facetgrid.FacetGrid at 0x7fa00b4be180>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Land_35_1.png

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

[15]:

print(datacube.__version__)

1.8.20

Last Tested:

[16]:

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

[16]:

'2025-09-16'