Sentinel-3 OLCI Water#

Keywords data used; sentinel-3,:index:datasets; sentinel-3 OL_2_WFR

Background#

The Sentinel-3 OLCI Level-2 Water Full Resolution (OL_2_WFR) product provides atmospherically corrected observations and geophysical parameters derived from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3A and Sentinel-3B satellites. OLCI was first launched aboard Sentinel-3A on 16 February 2016, followed by Sentinel-3B on 25 April 2018. The product includes water-leaving reflectance, water-quality variables and atmospheric parameters at a nominal spatial resolution of approximately 300 m. When observations from both satellites are combined, OLCI provides a revisit interval of less than two days at the equator, with more frequent coverage at higher latitudes. This makes the product valuable for monitoring inland, coastal and marine waters, including:

  • Monitoring chlorophyll-a concentration and phytoplankton dynamics

  • Detecting harmful algal blooms

  • Assessing water clarity and light attenuation

  • Monitoring suspended sediments and turbidity

  • Mapping coloured dissolved organic matter (CDOM)

  • Monitoring inland, coastal, and marine water quality

  • Supporting aquatic ecosystem and environmental monitoring

  • Supporting long-term climate and water resource studies

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 Water 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 pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
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 Resolution(x=0.003, y=-0.003)
s3_olci_l2_lfr s3_olci_l2_lfr Sentinel-3 OLCI L2 LAND CC-BY-4.0 EPSG:4326 Resolution(x=0.003, y=-0.003)
s3_olci_l2_wfr s3_olci_l2_wfr Sentinel-3 OLCI L2 WATER CC-BY-4.0 EPSG:4326 Resolution(x=0.003, y=-0.003)
s3_slstr_l2_lst s3_slstr_l2_lst Sentinel-3 LST CC-BY-4.0 EPSG:4326 Resolution(x=0.003, y=-0.003)
s3_syn_2_vg1 s3_syn_2_vg1 Sentinel-3 Level-2 VG1 SYN product CC-BY-4.0 EPSG:4326 Resolution(x=0.01, y=-0.01)

Please choose the product by specifying its name, with the product s3_olci_l2_wfr

[4]:

product = "s3_olci_l2_wfr"

List measurements#

We can further inspect the data available for the Sentinel-3 Land s3_olci_l2_wfr 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
A865 A865 int16 1 32767.0 [aerosol_angstrom_exponent] NaN 0.0 0.0001
ADG443_NN ADG443_NN int16 log10(1/m) 32767.0 [CDM_absorbtion_coefficient] NaN 0.0 0.0001
B01 B01 uint16 1 65535.0 [Oa01_reflectance] NaN 0.0 0.0001
B02 B02 uint16 1 65535.0 [Oa02_reflectance] NaN 0.0 0.0001
B03 B03 uint16 1 65535.0 [Oa03_reflectance] NaN 0.0 0.0001
B04 B04 uint16 1 65535.0 [Oa04_reflectance] NaN 0.0 0.0001
B05 B05 uint16 1 65535.0 [Oa05_reflectance] NaN 0.0 0.0001
B06 B06 uint16 1 65535.0 [Oa06_reflectance] NaN 0.0 0.0001
B07 B07 uint16 1 65535.0 [Oa07_reflectance] NaN 0.0 0.0001
B08 B08 uint16 1 65535.0 [Oa08_reflectance] NaN 0.0 0.0001
B09 B09 uint16 1 65535.0 [Oa09_reflectance] NaN 0.0 0.0001
B10 B10 uint16 1 65535.0 [Oa10_reflectance] NaN 0.0 0.0001
B11 B11 uint16 1 65535.0 [Oa11_reflectance] NaN 0.0 0.0001
B12 B12 uint16 1 65535.0 [Oa12_reflectance] NaN 0.0 0.0001
B16 B16 uint16 1 65535.0 [Oa16_reflectance] NaN 0.0 0.0001
B17 B17 uint16 1 65535.0 [Oa17_reflectance] NaN 0.0 0.0001
B18 B18 uint16 1 65535.0 [Oa18_reflectance] NaN 0.0 0.0001
B21 B21 uint16 1 65535.0 [Oa21_reflectance] NaN 0.0 0.0001
CHL_NN CHL_NN int16 log10(mg/m^3) 32767.0 [algal_pigment_complex_waters] NaN 0.0 0.0001
CHL_OC4ME CHL_OC4ME int16 log10(mg/m^3) 32767.0 [algal_pigment_open_waters] NaN 0.0 0.0001
IWV_W IWV_W uint8 kg/m^2 255.0 [integrated_water_vapour] NaN 0.0 1.0000
KD490_M07 KD490_M07 int16 log10(1/m) 32767.0 [diffuse_attenuation_coefficient] NaN 0.0 0.0001
PAR PAR uint16 microeinstein/s/m^2 65535.0 [photosynthetically_active_radiation] NaN 0.0 1.0000
T865 T865 int16 1 32767.0 [aerosol_optical_thickness] NaN 0.0 0.0001
TSM_NN TSM_NN int16 log10(g/m^3) 32767.0 [total_suspended_matter] NaN 0.0 0.0001
dataMask dataMask uint8 1 255.0 [mask] NaN 0.0 1.0000

The Sentinel-3 water product has 26 measurements:

Measurement

Description

Application

A865 (Aerosol Ångström Exponent)

Aerosol parameter describing the size distribution of atmospheric particles.

Atmospheric correction, aerosol characterisation, and climate studies.

ADG443_NN (Coloured Dissolved and Detrital Matter Absorption Coefficient)

Absorption coefficient of coloured dissolved organic matter (CDOM) and detrital material at 443 nm, estimated using a neural network algorithm.

Monitoring dissolved organic matter, freshwater inputs, and water quality.

B01 (Oa01)

Sensitive to aerosol correction and improved water constituent retrieval.

Atmospheric correction and water constituent retrieval.

B02 (Oa02)

Sensitive to yellow substances, detrital pigments, and turbidity.

CDOM monitoring, turbidity assessment, and water quality analysis.

B03 (Oa03)

Corresponds to the chlorophyll absorption maximum and supports biogeochemistry and vegetation studies.

Chlorophyll retrieval, ocean colour analysis, biogeochemistry, and vegetation monitoring.

B04 (Oa04)

Sensitive to chlorophyll.

Chlorophyll retrieval and water quality monitoring.

B05 (Oa05)

Sensitive to chlorophyll, sediment, turbidity, and red tide.

Chlorophyll retrieval, sediment monitoring, turbidity assessment, and harmful algal bloom detection.

B06 (Oa06)

Chlorophyll reference (minimum) band.

Chlorophyll retrieval and water quality analysis.

B07 (Oa07)

Sensitive to sediment loading.

Sediment transport and turbidity monitoring.

B08 (Oa08)

Corresponds to the second chlorophyll absorption maximum and is sensitive to sediment, yellow substances, and vegetation.

Chlorophyll retrieval, sediment monitoring, CDOM analysis, and vegetation monitoring.

B09 (Oa09)

Optimized for improved chlorophyll fluorescence retrieval.

Chlorophyll fluorescence retrieval and ocean colour analysis.

B10 (Oa10)

Corresponds to the chlorophyll fluorescence peak and red edge.

Chlorophyll fluorescence monitoring and vegetation analysis.

B11 (Oa11)

Represents the chlorophyll fluorescence baseline and red-edge transition.

Chlorophyll fluorescence monitoring and vegetation analysis.

B12 (Oa12)

Sensitive to oxygen absorption, clouds, and vegetation.

Cloud detection, atmospheric correction, and vegetation monitoring.

B16 (Oa16)

Used for atmospheric and aerosol correction.

Atmospheric correction and aerosol retrieval.

B17 (Oa17)

Supports atmospheric and aerosol correction, cloud detection, and pixel co-registration.

Atmospheric correction, cloud detection, and image co-registration.

B18 (Oa18)

Water vapour absorption reference band and common reference band with SLSTR for vegetation monitoring.

Water vapour correction and vegetation monitoring.

B21 (Oa21)

Sensitive to water vapour absorption and atmospheric/aerosol correction.

Water vapour retrieval and atmospheric correction.

CHL_NN (Algal Pigment – Complex Waters)

Chlorophyll-a concentration estimated using a neural network algorithm for optically complex coastal and inland waters.

Water quality monitoring, harmful algal bloom detection, and ecosystem health assessment.

CHL_OC4ME (Algal Pigment – Open Waters)

Chlorophyll-a concentration estimated using the OC4ME algorithm for open ocean waters.

Ocean productivity studies, phytoplankton monitoring, and marine ecosystem assessment.

IWV_W (Integrated Water Vapour)

Column-integrated atmospheric water vapour above each observation.

Atmospheric correction, climate studies, and environmental monitoring.

KD490_M07 (Diffuse Attenuation Coefficient at 490 nm)

Indicates how quickly light at 490 nm is attenuated within the water column, providing a measure of water clarity.

Water clarity assessment, aquatic habitat monitoring, and primary productivity studies.

PAR (Photosynthetically Active Radiation)

Amount of sunlight available for photosynthesis reaching the Earth’s surface.

Marine primary productivity studies, phytoplankton growth, and ecosystem monitoring.

T865 (Aerosol Optical Thickness)

Measure of the amount of aerosols in the atmosphere above each observation.

Atmospheric correction, air quality studies, and climate monitoring.

TSM_NN (Total Suspended Matter)

Concentration of suspended particles in the water estimated using a neural network algorithm.

Sediment transport studies, turbidity monitoring, and coastal and inland water management.

dataMask (Data Mask)

Binary mask indicating valid and invalid observations.

Excluding missing or invalid observations and 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 68b73a009f2c4494891a4b027d6a030d 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. f 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 uses Lake Mweru, a large freshwater lake located on the border between the Democratic Republic of the Congo (DRC) and Zambia. Lake Mweru supports important fisheries and surrounding communities, making it well suited for demonstrating Sentinel-3 OLCI water quality products such as chlorophyll-a, suspended matter, and water clarity.

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= -8.9793, lon= 28.7650, buffer=0.55)

# 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 approximately 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.

[8]:

query = {
    'x': (lon_range),
    'y': (lat_range),
    'time':('2026-05-01', '2026-05-30'),
    '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: 176MB
Dimensions:      (time: 26, latitude: 368, longitude: 367)
Coordinates:
  * time         (time) datetime64[ns] 208B 2026-05-01T04:01:15.500000 ... 20...
  * latitude     (latitude) float64 3kB -8.428 -8.431 -8.434 ... -9.527 -9.529
  * longitude    (longitude) float64 3kB 28.22 28.22 28.22 ... 29.31 29.31 29.31
    spatial_ref  int32 4B 4326
Data variables: (12/26)
    A865         (time, latitude, longitude) int16 7MB 0 0 0 ... 32767 32767
    ADG443_NN    (time, latitude, longitude) int16 7MB 0 0 0 ... 32767 32767
    B01          (time, latitude, longitude) uint16 7MB 0 0 0 ... 65535 65535
    B02          (time, latitude, longitude) uint16 7MB 0 0 0 ... 65535 65535
    B03          (time, latitude, longitude) uint16 7MB 0 0 0 ... 65535 65535
    B04          (time, latitude, longitude) uint16 7MB 0 0 0 ... 65535 65535
    ...           ...
    IWV_W        (time, latitude, longitude) uint8 4MB 34 34 36 ... 255 255 255
    KD490_M07    (time, latitude, longitude) int16 7MB 0 0 0 ... 32767 32767
    PAR          (time, latitude, longitude) uint16 7MB 0 0 0 ... 65535 65535
    T865         (time, latitude, longitude) int16 7MB 0 0 0 ... 32767 32767
    TSM_NN       (time, latitude, longitude) int16 7MB 0 0 0 ... 32767 32767
    dataMask     (time, latitude, longitude) uint8 4MB 0 0 0 0 ... 255 255 255
Attributes:
    crs:           EPSG:4326
    grid_mapping:  spatial_ref

Applying Scale Factors and Offsets to Sentinel-3 OLCI Measurements#

⚠️ Note: Many measurements in the Sentinel-3 OLCI Level-2 Water Full Resolution product are stored as scaled integers to reduce file size while retaining the required measurement precision. To convert a stored integer value into its physical value, apply the scale_factor and add_offset attributes provided in the variable metadata. The scale_factor changes the magnitude of the stored value, while the add_offset shifts it to the correct physical range.

Missing or fill values should be masked before applying the scaling.

\[\text{Physical value} = (\text{Stored value} \times \text{Scale factor}) + \text{Add offset}\]

For example, For example, a stored value of 2449 for the B01 reflectance band, with a scale_factor of 0.0001 and an add_offset of 0, corresponds to a physical value of 0.2449.

The scale factor varies between measurements. Most OLCI reflectance bands use a scale factor of 0.0001, while IWV_W (Integrated Water Vapour) uses a scale factor of 1.0, meaning its stored values are already expressed in physical values.

The following example displays the scale factor, add offset and units for all the measurements.

[9]:

metadata = pd.DataFrame(
    [
        {
            "Measurement": measurement,
            "Scale factor": ds_S3[measurement].attrs.get("scale_factor"),
            "Add offset": ds_S3[measurement].attrs.get("add_offset"),
            "Units": ds_S3[measurement].attrs.get("units"),
        }
        for measurement in ds_S3.data_vars
    ]
)

metadata

[9]:
Measurement Scale factor Add offset Units
0 A865 0.0001 0.0 1
1 ADG443_NN 0.0001 0.0 log10(1/m)
2 B01 0.0001 0.0 1
3 B02 0.0001 0.0 1
4 B03 0.0001 0.0 1
5 B04 0.0001 0.0 1
6 B05 0.0001 0.0 1
7 B06 0.0001 0.0 1
8 B07 0.0001 0.0 1
9 B08 0.0001 0.0 1
10 B09 0.0001 0.0 1
11 B10 0.0001 0.0 1
12 B11 0.0001 0.0 1
13 B12 0.0001 0.0 1
14 B16 0.0001 0.0 1
15 B17 0.0001 0.0 1
16 B18 0.0001 0.0 1
17 B21 0.0001 0.0 1
18 CHL_NN 0.0001 0.0 log10(mg/m^3)
19 CHL_OC4ME 0.0001 0.0 log10(mg/m^3)
20 IWV_W 1.0000 0.0 kg/m^2
21 KD490_M07 0.0001 0.0 log10(1/m)
22 PAR 1.0000 0.0 microeinstein/s/m^2
23 T865 0.0001 0.0 1
24 TSM_NN 0.0001 0.0 log10(g/m^3)
25 dataMask 1.0000 0.0 1

Masking#

Before visualisation, we use the dataMask band to mask values affected by cloud or other quality issues. The dataMask is a binary quality layer, where a value of 1 indicates a valid observation and 0 indicates an invalid observation.

The comparison below illustrates the effect of applying the mask using the Band 03 (B03) surface reflectance measurement. The left image shows the original data, while the right panel shows the same observation after retaining only pixels where dataMask = 1. This demonstrates how the mask removes observations over non-target areas (i.e. non-water) and unreliable observations, producing a cleaner dataset for visualisation and subsequent analysis.

[10]:

# Select a single observation for comparison
time_idx = 8

# Unmasked
b03 = (ds_S3["B03"].isel(time=time_idx)* ds_S3["B03"].attrs["scale_factor"])

# Masked
b03_masked = (ds_S3["B03"].where(ds_S3.dataMask == 1).isel(time=time_idx)* ds_S3["B03"].attrs["scale_factor"])

# Plot comparison
fig, ax = plt.subplots(1, 2, figsize=(12,5))

b03.plot(ax=ax[0], robust=True)
ax[0].set_title("Without dataMask")

b03_masked.plot(ax=ax[1], robust=True)
ax[1].set_title("With dataMask")

plt.tight_layout()
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_26_0.png

The comparison demonstrates the effect of applying the dataMask. Without masking (left), invalid observations are retained, including pixels affected by cloud, land, or other quality issues. Applying the dataMask (right) removes these unreliable pixels by keeping only observations where dataMask = 1, resulting in a cleaner image that is more appropriate for visual interpretation and further analysis.

Having demonstrated its effect, the dataMask is now applied to the entire dataset so that all subsequent visualisations and analyses use only valid observations.

[11]:

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

The cell below visualizes the Band 03 (B03) surface reflectance at the time of observation, representing atmospherically corrected reflectance in the blue-green region of the electromagnetic spectrum. The scale factor is applied prior to visualization to convert the stored integer values to physical reflectance values.

[12]:

(ds_S3['B03'].isel(time=[0, 8, 18, 24])*ds_S3["B03"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4);
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_31_0.png

The cell below visualizes the CHL_NN chlorophyll-a product, estimated using a neural network algorithm for optically complex coastal and inland waters. The scale factor is applied prior to visualization to convert the stored integer values to physical values.

[13]:

(ds_S3.isel(time=[0, 8, 18, 24])['CHL_NN']*ds_S3["CHL_NN"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4)

[13]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae4149a990>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_33_1.png

The cell below visualizes the CHL_OC4ME chlorophyll-a product, estimated using the OC4ME algorithm for open ocean waters. The scale factor is applied prior to visualization to convert the stored integer values to physical values

[14]:

(ds_S3.isel(time=[0, 8, 18, 24])['CHL_OC4ME']*ds_S3["CHL_OC4ME"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4)

[14]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae414e2720>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_35_1.png

The cell below visualizes the Total Suspended Matter (TSM_NN) product, representing the concentration of suspended particles within the water column estimated using a neural network algorithm. The scale factor is applied prior to visualization to convert the stored integer values to physical values.

[15]:

(ds_S3.isel(time=[0, 8, 18, 24])['TSM_NN']*ds_S3["TSM_NN"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4)

[15]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae4163f380>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_37_1.png

The cell below visualizes the Diffuse Attenuation Coefficient at 490 nm (KD490_M07), representing water clarity and the rate at which light is attenuated through the water column. The scale factor is applied prior to visualization to convert the stored integer values to physical values.

[16]:

(ds_S3.isel(time=[0, 8, 18, 24])['KD490_M07']*ds_S3["KD490_M07"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4)

[16]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae347eba70>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_39_1.png

The cell below visualizes Photosynthetically Active Radiation (PAR), representing the amount of sunlight available for photosynthesis reaching the Earth’s surface. As PAR has a scale factor of 1.0, no scaling is required prior to visualization.

[17]:

(ds_S3.isel(time=[0, 8, 18, 24])['PAR']).plot(robust=True, col="time", col_wrap=4)

[17]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae42c81b80>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_41_1.png

The cell below visualizes the Aerosol Optical Thickness (T865), representing the amount of aerosols in the atmosphere above each observation. The scale factor is applied prior to visualization to convert the stored integer values to physical values.

[18]:

(ds_S3.isel(time=[0, 8, 18, 24])['T865']*ds_S3["T865"].attrs["scale_factor"]).plot(robust=True, col="time", col_wrap=4)

[18]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae34753a70>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_43_1.png

The cell below visualizes Integrated Water Vapour (IWV_W), representing the total atmospheric water vapour contained within a vertical column of the atmosphere. As IWV_W has a scale factor of 1.0, no scaling is required prior to visualization.

[22]:

(ds_S3.isel(time=[0, 8, 18, 24])['IWV_W']).plot(robust=True, col="time", col_wrap=4)

[22]:

<xarray.plot.facetgrid.FacetGrid at 0x7fae4162b530>
../../../_images/sandbox_notebooks_Datasets_Sentinel_3_Water_45_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

[20]:

print(datacube.__version__)

1.9.13

Last Tested:

[21]:

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

[21]:

'2026-09-02'