Indian Crop Insurance Market: A GIS Perspective

Insurance, as a sector not new for us and we all know the importance of incase of any eventuality to the earning members. As the scope of insurance sector is increasing so the market size. One of the notable and upcoming sector is Crop Insurance.


Insurance Sector

With change in regulation under Pradhan Mantri Fasal Bima Yojna (PMFBY), the market size has increased from 3500 Cr (USD~ 530M) to 18,000 crore (USD 3000M) (Ref Time of India), almost 18% jump.  In the earlier National Agriculture Insurance Scheme (NAIS), both premium and claims were capped for the insurer. This time, the government has freed the pricing. In exchange, insurers undertake to fully compensate the farmer for any loss. So while the government has an initial outgo in the form of a premium subsidy, it does not have to pay out anything even if there is widespread crop failure. Earlier, the exchequer would compensate for any losses above a certain limit.

Under the PMGSY, there are 18 companies which has been empanelled to settle the crop insurance, which includes all the big brands like ICICI Lombard, HDFC ERGO, New India Assurance, Agriculture Insurance Crop, IFFCO Tokyo list goes on..Every one is eyeing towards getting some share of this new market. Actually there is possibility to further expand the market to other insurances like “Cattle Insurance”. I am sure when insurance companies are reaching out to villages and knowing them then it’s easy to make the decision whether to insure other assists like Cattle or not.

Remote Sensing & GIS technique has a great role to play in crop insurance specifically in below areas:

  • Crop Yield estimation
  • Crop Damage Areas
  • Presence/Absence of crop on the ground
  • Better KYC
  • Efficient Geographic Reporting



  • Identifying new market potential

Crop Yield estimation is possible based on satellite data along with other input parameters like rain fall, soil type, ground water table, geography type. Tools like ERDAS Imagine have perfect blend of technique/models to handle the various data types. ERDAS Imagine provides the model builder which dynamically can co-relate the data and also these models can be published on-line.

GIS as a tool has the capability to stores the data related to farmer/land holdings/history of claim settlement/financial health etc, which can be represented in various forms of dashboards. These dashboard will help insurance companies in making the informed decision about marketing efforts based on history of number of claims in a region/crop failure etc.


One of the challenge in doing the yield estimation by remote sensing technique is the availability of Satellite Data and high cost associated with it. I would like to suggest here one of the alternative to this is to use open source data like MODIS (Moderate Resolution Imaging Spectroradiometer) which provides the spatial resolution of upto 250m and spectral resolution of upto 36 Channels. Which is good resolution for doing a crop estimation at District/State level (1KM X 1KM). As a strategy, I would say, we should do the regional estimates based on these data to get fair amount of idea of sowing pattern/damage areas etc. In pockets where we need to do the detailed analysis then we should use high resolution satellite data or also we can use Drone to get the quick estimates. I am sure this will provide scientific basis for providing the compensation to farmers and eliminate bogus settlement.

In this article, I am not getting into exact technique of doing the analysis, as it’s matter of detailed analysis and deliberations. For any further queries and discussion please comment here or email us: we will attempt to help you to best of our knowledge.

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