Analysis Tools

In this section scripts to generate the maps of the gases over the region of the India with a focus on the Indo-Gangetic Plain are introduced.

The codes are created based on the data stored by the satellite Sentinel-5p by the use of Google Earth Engine platform. Following parameters can be measured and their time dependent plots can be further investigated:

  • Methane (CH4)
  • Carbon Monoxide (CO)
  • Formaldehyde (HCHO)
  • Nitrogen Dioxide (NO2)
  • Ozone (O3)
  • Sulphur Dioxide (SO2)
  • UV Aerosols (UVA)

    You do not need to have a programming skills to use them and generate your own maps, although deeper understanding of the code may be an advantage. Short tutorial, all together with links to the script and description of the code can be found on the page.

Description of the code

The geometry is imported as 8 polygons: Rajasthan, West Bengal, Punjab, Bihar, Delhi, Haryana, UP, IGP. Additional, 9th entry is the whole region of India obtained from the LSIB dataset (Large Scale International Boundary Polygons, Simplified).

In the first lines of the scripts, the user can specify their parameters. The region of the interests as a variable region is specified in common with the start and finish date of the observations (start and finish variables). The format of the provided data is YYYY-MM-DD. In the same sector, the variable scales is defined as an integer which indicates the number of meters per pixels. The cloudFraction variable indicates allowed in percents cloud contents on the pictures (value between 0-1) Note: setting the custom cloud content is not available for each species due to the lack of the detection of the cloudiness for the particular gases.  The regionborders variable is created to store the actual borders of the region to generate the KMZ file.

In the next part of the script, if the region of the interests is set as India, UP, IGP (i.e. huge region with the complicated geometry, their geometry is simplified to the rectangle. And the scale is chosen arbitrarily by changing the variable scales. However,  the generated KMZ file contains the original geometry of the region.

The function masking clouds is created, and a suitable collection of the Sentinel-5p images is filtered by the date. Additionally, the bands are chosen. Then, It is mapped over the cloud mask (if apply).  The variable with prefix band_viz is set the maximum and minimum values (what can be customized by advancer user by the analyse of the minimum and maximum values of the chart created in the later part of the script) and colour palette necessary to display the map and the legend. 

The mean value of the gas is calculated using reducers what let to operate on the bigger sets of the data and clipped to the region of the interests.

The chart is generated based on the image collection, region of the interest, band of the interest over the time sourced from the first satellite picture made in the choose start date.

The generation of the layer is similar to the generation of the chart, however, this time instead of direct referencing to the band by the original band name, the band is chosen as 0 (after reducing the size of the collection, the image lost their primary band names which are now substituted by the numbers and can be viewed by printing the collection i.e. print(XXmeanclipped)).

In the next part of the code, the legend is generated. It is a long-winded process, although not described here since it is created nearly only based on the setting adequate visual parameters such as the font size, pagination, margins etc..

Finally, the last part of the code includes the data to export the image to the drive: colour pallette (same as before), min and max values. The variable toexport is introduced as the merged collection with choose band. The instruction .visualize() with a visualized parameter inside let the image to obtain the colours after the export.

Finally, the map and the KMZ file is exported. The crs instruction inside the function which is exporting the map indicates that the picture is fitted in whole on the one picture (to avoid dividing the picture into the parts by the program).

What you will need...

Despite using the tool is free, you will need the valid google account and to register yourself on the Google Earth Engine platform.

google earth engine console gui

Available scripts

Methane (CH4)

Methane (CH4) is, after carbon dioxide (CO2), the most important contributor to the anthropogenically enhanced greenhouse effect. Roughly three-quarters of methane emissions are anthropogenic and as such it is important to continue the record of satellite based measurements.

Click on the panel to generate your map

Nitrogen Dioxide (NO2)

Nitrogen oxides (NO2 and NO) are important trace gases in the Earth’s atmosphere, present in both the troposphere and the stratosphere. They enter the atmosphere as a result of anthropogenic activities (notably fossil fuel combustion and biomass burning) and natural processes (wildfires, lightning, and microbiological processes in soils).

Click on the panel to generate your map

Ozone (O3)

In the stratosphere, the ozone layer shields the biosphere from dangerous solar ultraviolet radiation. In the troposphere, it acts as an efficient cleansing agent, but at high concentration it also becomes harmful to the health of humans, animals, and vegetation. Ozone is also an important greenhouse-gas contributor to ongoing climate change.

Click on the panel to generate your map

UV Aerosol Index (UVA)

The AAI (UVA) is based on wavelength-dependent changes in Rayleigh scattering in the UV spectral range for a pair of wavelengths. The difference between observed and modelled reflectance results in the AAI. When the AAI is positive, it indicates the presence of UV-absorbing aerosols like dust and smoke. It is useful for tracking the evolution of episodic aerosol plumes from dust outbreaks, volcanic ash, and biomass burning.

Click on the panel to generate your map

Carbon Monoxide (CO)

Carbon monoxide (CO) is an important atmospheric trace gas for understanding tropospheric chemistry. In certain urban areas, it is a major atmospheric pollutant. Main sources of CO are combustion of fossil fuels, biomass burning, and atmospheric oxidation of methane and other hydrocarbons. Whereas fossil fuel combustion is the main source of CO at northern mid-latitudes, the oxidation of isoprene and biomass burning play an important role in the tropics.

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Formaldehyde (HCHO)

Formaldehyde is an intermediate gas in almost all oxidation chains of non-methane volatile organic compounds (NMVOC), leading eventually to CO2. Non-Methane Volatile Organic Compounds (NMVOCs) are, together with NOx, CO and CH4, among the most important precursors of tropospheric O3. The major HCHO source in the remote atmosphere is CH4 oxidation. Over the continents, the oxidation of higher NMVOCs emitted from vegetation, fires, traffic and industrial sources results in important and localized enhancements of the HCHO levels.

Click on the panel to generate your map


Sulphur dioxide (SO2) enters the Earth’s atmosphere through both natural and anthropogenic processes. It plays a role in chemistry on a local and global scale and its impact ranges from short-term pollution to effects on climate. Only about 30% of the emitted SO2 comes from natural sources; the majority is of anthropogenic origin. SO2 emissions adversely affect human health and air quality. SO2 has an effect on climate through radiative forcing, via the formation of sulphate aerosols.

Click on the panel to generate your map


Other Datasets

Here is a table summarising useful remote sensing datasets for land parameters and vegetation. Table will be updated with new datasets periodically.

Temporal Resolution Spatial Resolution Description Location
ESA CCI Global Land Cover Classification

1992 to 2015
300 m
Land cover classes generated from satellite records of MERIS, AVHRR SPOT- Veg, PROBA-V
High Resolution Irrigated Area

2000 to 2015
250 m
250m normalized difference vegetation index (NDVI) data from MODIS and 56m land use/land cover data for India
LST climate data records
1998 to 2012

2016 –
0.01 degrees
Global product generated from ATSR, ATSR-2 and AATSR and Sentinel 3 SLSTR using the 11 and 12 micron BTs
ESA CCI Soil Moisture

1978 to 2010
0.25 degrees
Estimated SM from merged ACTIVE and PASSIVE sensor products using 6- 19 GHz channels
Fractional Vegetation
Every 26 days

1998 – 2017
0.001 degrees
Fraction of vegetation within a pixel estimated from SPOT Sensors with the CYCLOPS programme
Copernicus Land Services
1998 -
1 km
Estimates of Leaf Area Index, NDVI, FCOVER, FAPAR (Near Real Time at 1/3 km only for Europe)
Solar Induced Fluorescence
GOSAT (2009 -)

OCO-2 (2014- )
10 km footprint
Estimate photosynthetic activity; detection of chlorophyll Fluorescence

University of Leicester/NASA