What are differences between S2A and S2B cameras technically?

I am not sure, what you are looking for. Technical information about Sentinel-2 can be found here.
If you would like to get reflectance value from Sentinel Hub, you need to:

• configure the layer such as “return [B02,B03,B04,B08];”
• set format to 32-bit float
  Read this note.

Hi 🙂 thank you for reply. I need exact values of sentinel 2A and 2B bands’ wavelenghts. For example Band 8 (798-852 nm) I know there are small differences between these satellite cameras.

As far as I know, the sensors are actually configured to detect a range, not exact wavelength. So the “798-852” is the best you will get. You will notice that B8A range is overlapping the B08 one, but is narrower.

Greetings

My names are Namafe Namafe. I am one of the user’s of datasets. I am a student trying to subject a sentinel 2 image to a land cover classification. However, I wanted to find out what is the difference between S2A and S2B and which of datasets require geometric or atmospheric corrections?

I look forward to hearing from you.

Regards

Hi,

S2A and S2B are both the same satellites in principle. It is obviously not possible to do an exactly the same copy so there are minor differences, but I think they should not affect machine learning principles. The differences are certainly much lesser than overall uncertainty, which comes with satellite data.
You do not need to apply geometric correction.
Atmospheric correction might be required, depending on your processes. Check this FAQ:
https://www.sentinel-hub.com/faq/what-kind-atmospheric-correction-available

Hi,

Thanks for the clarification.

In addition, if for example, I plan to subject my satellite data-set eg. sentinel 2 (in this case I have two images) to a land cover classification, and I want to use a supervised classification in the semi-automatic classification plugin. What’s best of the two options below

1. classify the individual images and then mosaic them later, or
2. mosaic the two images and then subject the composite image to a land cover classification?

If option two is possible, How does one mosaic these two images with each having the following bands, 2, 3,4,5,6,7,8,8A,1972 and 12

Hi,

I would certainly recommend you do a mosaic but limit it to one day period - this will solve problems on the border of the granules and yet ensure that the data you receive is consistent.
The best way to do this is to use our sentinelhub-py libraries or even eo-learn
If you want to do it manually using Sentinel Hub services, follow these steps:

• Create new product with configuration “return[B02,B03,B04,B05,B06,B07,B08,B8A,B11,B12];”
• Clip request per geometry
• Use TIME=YYYY-MM-DD/YYYY-MM-DD parameter, e.g. TIME=2018-08-29/2018-08-29

.

The boxcar-type spectral characteristics of the bands are listed - separately for S2A and S2B - in the Tech info link @gmilcinski gave above. If you need more details, the complete spectral response functions (SRF) are provided in this document.

Most of the times, you don’t need these details as the user and the S2A and S2B sensors can be assumed identical. However, the differences were judged significant enough that ESA’s official Level-2A processor (Sen2Cor) provides separate lookup tables for the atmospheric correction of S2A and S2B.