Activefire detection using sentinel 3 slstr data


I want to develop an algorithm for activefire detection in python. My aim is to use Sentinel-3 SLSTR data of a particular region and detect forest fires in that region. The data that I am using contains separate files in netCDF format and one xml file. I tried using the snappy module but its giving me a “no module named jpy” error. So, I started using xarray python package to read the netCDF data. Right now, I am trying to convert radiance to reflectance, but not able to proceed. Any amount of help would be appreciated.

Thank you.

To pass from radiance to reflectance you have to correct atmospherically, and that is not as easy task. You can use L2 data directly:

There is already a fire product derived by ESA:

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Are you interested in currently active fires or historical records (historical studies can benefit from more elaborate processing than is possible if you need quick turnaround)?

If you successfully configured snappy when you installed ESA SNAP, this error may be due to using a different python version or venv.

Check that the you can’t use gpt for your processing needs – ESA SNAP snappy is less often required as more capabilities have been added to gpt.

If you are still having this error and decide you do need ESA SNAP snappy you should start a new thread for “no module named jpy” and provide enough detail to allow others to understand and reproduce the problem (OS version, SNAP version, SNAP update status, and the snappyutil.log file).

Thank you so much for replying,

I am currently interested in active fire detection. I am supposed to use cloud mask to avoid false alarm due to clouds. But for creating an algorithm for cloud mask I need reflectance parameter. And for that reason I am trying to convert radiance into reflectance. I am using the formula that I came across in the “Sentinel 3 SLSTR Land Handbook”. The formula goes like this:

reflectance = pi*radiance/solar_irradiance/cos(zenith_angle).

Do I directly apply the formula or consider even more processing before applying it?

Thank you so much for replying.

I looked at the FRP processing in which it explains about the derivation pixel-by-pixel module. Can you explain what is this needed constants and specific arrays it talks about that have to be processed and generated before estimating BTs and radiance.

It has been many years since I looked at active fire detection (for MODIS sensors). First study of Sentinel-3 SLSTR active fire detection and FRP retrieval: Night-time algorithm enhancements and global intercomparison to MODIS and VIIRS AF products discusses advances made since MODIS:

The results shown herein demonstrate the capabilities of the SLSTR night-time AF detection and FRP retrieval capability, with the near real-time (NRT) product produced within 3 h of data collection and available through fast-delivery routes.

Hope this is helpful. In practice, local conditions may benefit from algorithms tuned to the local fuel types, soils, and atmospheric properties. I think there are now commercial sources of high resolution imagery that may offer advantages over Sentinel 3.