How to determine the acres devoted for crop type

I did crop classification, corn and soybeans, from sentinel 1 data for study case and I would like to determine the acres devoted to corn and what is for soybeans. Could I do this using SNAP or QGIS?


If you have a vector layer ( shapefile) for corn and soybeans areas, then its very easy to calculate the area of those shapefile in QGIS

Have a look at this video:

Hi Ghazal,

The work you’re doing sounds very interesting. May I ask which country is this classification being done?

I’m trying to do a similar classification for India (Issues such as very small farms, mixed cropping, multi-cropping, etc.) using SNAPPy and Sentinel-1 and would like to know more about the methods of classification you’re using.

Thank you.


I used Random forest for the classification of my case study in USA, Kossuth county. Furthermore since you are from India, I would like to help me to have polygon for Kerala in order to use it as study area to extract data from sentinel 1 if possible

bu the output that I have from SNAP, the classification product, is raster and I want to do calculation of the area for every crop type.

convert your raster to polygons and calculate the size of the polygons in a new attribute column.

In order to calculate the area of some parts on your raster, you can follow two ways:

  • Vectorize your raster as mentioned by ABraun
  • Digitize those parts of the raster you want to calculate the area from. Once you have the digitized polygons, you can calculate the area.

IDRISI has a tool to directly assess the size of pixel aggregates of the same value but I don’t see this in any free GIS

Hi Ghazal!

Thank you.

You could download shapefiles of various states and districts in India from this website:

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Hi adhithya

I would like to do crop classification over India and I want to know if possible how to get vector training for any area in India.

Thanks in advance.

It’s extremely hard doing crop classification in India. Especially South India. The reasons being:

  1. Mixed cropping
  2. Multi cropping
  3. Small farm sizes (Most marginal farmers have holdings of less than 30 m x 30 m)
  4. Finally, It’s nearly impossible to find training data online (I haven’t come across any till now). Without actually being on a field trip to collect the ground data points I was never able to do classification.

Hope this answers your question. Apologies for being overly negative.

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have you manged to do the crop classification despite the heterogeneity of pixels? how did you manage to do it. I’m interested because i’m doing a project on the same area facing challenges of mixed cropping and small farm sizes