Use of Satellite Remote Sensing for Crop Insurance

Shibendu Shankar Ray

Pradhan Mantri Fasal Bima Yojana (PMFBY), the Government of India’s new crop insurance programme, which was launched during Kharif- 2016, is unique in many senses. One of that is promoting the use of technology for better implementation of the crop insurance. The technologies, promoted by PMFBY, are mostly in 3 domains:  i) Information Technology (e.g. development of web-based insurance portal for better management of the crop insurance), ii) Smartphone Applications (Mobile Apps for information about crop insurance and for crop cutting experiment data collection), and iii) Satellite and UAV/drone based Remote Sensing. This article deals with satellite remote sensing applications in crops insurance.

Satellite remote sensing (SRS) is the technique of using various cameras (optical and microwave), on-board earth observation satellites, to get information about various targets, such as land ocean and atmosphere. Use of SRS for agricultural applications has been a major activity for Indian Remote Sensing Programme. Since, the early remote sensing experiment of the country in 1968/69, for assessing the spread of the coconut root wilt disease in Kerala, agriculture has been a major driver of Indian Space Programme. Many satellites have been specifically designed, keeping requirement of agriculture in view. Ministry of Agriculture has launched many national level programmes for use of satellite remote sensing. These include FASAL (Forecasting Agricultural output using Space, Agro-meteorology and Land based observations), NADAMS (National Agricultural Drought Assessment and Monitoring System), CHAMAN (Coordinated Horticulture Assessment and Management using geo-informatics), KISAN (Crop Insurance using Space technology And geo-informatics). The technologies developed in these programmes are being used for better implementation of the crop insurance programme.

Satellite Remote Sensing (SRS) for Crop Insurance

There are many possibilities of use of satellite remote sensing for crop insurance, specifically PMFBY.  These include:

1. Smart Sampling
2. Yield Estimation

• Area Discrepancy

1. Yield Discrepancy and Quality Checking
2. Loss Assessment and On Account Payment
3. Prevented Sowing

• Risk Zone Mapping

Smart Sampling

PMFBY is a crop yield based insurance. Crop yield is assessed by carrying out crop cutting experiments (CCE). The Guidelines has suggested carrying 4 CCEs per village/village panchayat, for major crops. Considering we have more than 2.38 lakh village panchayats in the country and in a season states are notifying 1-6 crops for insurance, the number of required CCEs becomes very huge and difficult to manage. Additionally, as CCE locations are randomly selected, they may not be very representative of the diverse crop conditions in the village. The Vegetation Index, which is derived from SRS and is representative of crop condition, can be used for designing better sampling plan for CCEs, which is also known as smart sampling. The studies carried out under KISAN project has shown that a combination of two Vegetation Indices (Normalized Difference Vegetation Index and Normalized Difference Wetness Index) have significantly improved the efficiency of CCE planning.

Yield Estimation

Large number of models (empirical/physical) have been developed to relate various parameters (crop, soil and agro-meteorology) derived from SRS to estimate yield. With the availability of high resolution data, there is a possibility of getting crop yield or their proxies using SRS, at village level. However, for many crops, the use of SRS for yield estimation is still a scientific challenge. There is a need to include many other agricultural information, along with SRS, to develop a good yield models which can work at farm or village level. Research & development work is being carried out in this regard.

Area Discrepancy

It has been seen that there are many cases, where the area insured is much more than actual area sown, which results in reduction of sum insured and consequently reduction in claims of insurance. There has been also cases, where area is insured for a particular crop, but actually sown with a different crop. Since, SRS has been operationally used for crop area estimation (under FASAL project), this can be used for checking area discrepancy, especially for major crops of the country.

Yield Discrepancy/Quality Checking

Many times, there has been issues regarding the quality of the yield data derived from crop cutting experiments, i.e. yield is abnormally low or high, compared to general crop condition. The PMFBY guidelines mentions that in these cases satellite image data and other technologies can be used to check the quality of yield data.  Mahalanobsis National Crop Forecast Centre (MNCFC) has developed a protocol of checking yield data through statistical analysis, weather analysis, SRS based vegetation indices and other collateral information.

Loss Assessment & On Account Payment

PMFBY proposes to provide immediate relief to insured farmers, in case of adverse seasonal conditions during the crop seasons, e.g. floods, prolonged dry spells, severe drought etc., where expected yield is likely to be 50% of the threshold yield. Since, this payment has to be given mid-seasons, it has to be assessed in absence of CCE data. In these cases, weather data and SRS can be very useful. Various applications have shown that it is possible to assess the severity of impact of drought, hailstorm and flood using satellite data. The NADAMS project regularly assesses the severity of drought at district/sub-district level using an integrated approach. Similarly, microwave remote sensing data are useful to assess the flood inundation of crops.

Prevented Sowing

Due to unfavourable weather conditions (deficiency or delay in rainfall, unfavourable temperature conditions, etc.), farmers may not be able to sow the crops. PMFBY covers this risk. A comparison of High resolution SRS data of two years (normal year and the current year) can identify the areas with prevented sowing. This can be overlaid with digitized cadastral maps to identify the farms with prevented sowing condition.  

Risk Zone Mapping

Under PMFBY, in order to diversify/spread the risk and cover high risk/low risk area equally, the districts/sub-districts are grouped in such a way that, each group contains mix of districts/sub-districts with different risk profile. Generally, risk is assessed using long term yield data. In absence of high quality long-term yield data at block/taluka level, one can use long term satellite based vegetation indices values for risk zoning. These can be combined with long-term weather data, disaster frequency, etc. for better risk analysis.

Conclusion

The above discussion has shown that satellite remote sensing can play a major role for the success of the Crop Insurance programme of the country. However, there are still a lot of limitations of using satellite remote sensing for operational purposes: i) Limited availability of cloud free data during Kharif season and iii) Use of remote sensing for quantitative estimation of crop loss or crop yield at farm level is still a challenge. Hence, there is a need to integrate remote sensing with other inputs and models for catering to the requirements of operational applications. With the frequent availability of high resolution satellite data from Indian and International satellites and also the increasing use of UAV (Unmanned Aerial Vehicles)/ Drones for crop insurance programme, the remote sensing can prove to have a strong role in successful implementation of Pradhan Mantri Fasal Bima Yojana.

(The author is the Director at Mahalanobis national Crop Forecast Centre, DAC&FW, New Delhi. Views expressed are personal. The author could be reached at shibendu.ncfc@nic.in )