This presentation was given by Mr. Ishan Tomar, Satsure during IoTForum's AgriTech Day 2019 on February 9, 2019 at NIANP-ICAR, Bangaluru

1. Satellite Enabled
Agri Risk Mgmt
SatSure
Bangalore | London | St. Gallen | Dubai

2. Large Area Analytics
Satellite
Data
+ + =
Artificial
Intelligence
Big Data
Analytics
Realtime
Insights
!2
What? How much?When?Where? How risky?

3. Solution
FARMER
PROFILE
DATA
AGRONOMIC
DATA
WEATHER
DATA
ECONOMIC
DATA
SATELLITE
DATA
FARM INCOME
CREDIT WORTHINESS
DECISION INTELLIGENCE FRAMEWORK
CROP RISK
AGRI INSURANCE
POLICY
DECISION MAKING
CROP MONITORING
MARKET LINKAGES
!3

4. !4
Satellite Technology

5. How Satellites Work
!5

6. Discriminating Signals
!6

7. Discriminating Signals
!7

8. Crop Phenology
!8

9. Crop Phenology - Analysis
!9
Lower Level of Red Curve
Smaller Area
under
Red Curve

10. Radar Satellites
!10Cloudy Area
Optical Satellite SAR Satellite

11. Radar Satellites
!11
Synthetic Aperture Radar (SAR)
Satellite
Rice Monitoring using SAR

12. Spatial Resolution
!12

13. !13
Satellites & Agriculture
• Crop Classification
• Crop Health Index
• Crop Water Stress Index
• Sowing Intelligence
• Sowing Progress
• Harvest Intelligence
• Harvest Progress
• Relative Yield
• Crop Risk Zoning
• Crop Cutting Experiment Optimization
• Assessment of Crop Damage due to :
• Flood
• Frost
• Disease, etc
• etc

14. Area Covered
!14
90 Districts in India
• Governments
• Banks
• Insurers
• Re-Insurers
Outside India
• Ivory Coast
• Tanzania
• Nigeria
• Bolivia
• Australia
• Japan
• UAE
• Ghana
1 Million Sq Km Weekly
500 Million Sq Km Analysed Globally
Sectors
• Agriculture
• Infrastructure
• Renewables
“… an Indian bank was able to increase
its book size by 2% and achieve a
reduction of 1.5% in nonperforming
assets during a single season.”

15. Farm Level Performance
!15!15
Crop : Mustard
Sate : Rajasthan
District : Sawaimadhopur
• Taluk : Bonli
• Village : Banholi
• Lat / Long : 26.162 / 76.407
04-Nov-2016 20-Nov-2016 06-Dec-2016 22-Dec-2016 07-Jan–2017 23-Jan–2017

16. !16
SatSure Dashboard
Summary PageDistrict - Crop AcreageMandal - Crop AcreageMandal - Crop HealthMandal - Crop MoistureMandal - Relative YieldMandal - Harvesting

17. !17
Ex-VP, Goldman Sachs
23 yrs industry exp
UK, India & U.S.
Amardeep Sibia
CEO
Scientist, ISRO
Consultant NSR
7 yrs space industry exp
India, U.S. & EU
Prateep Basu
CSO & Founder
Ex-Petrofac Risk
Manager
11 yrs Oil & Gas
GCC
Samuel John
COO
Serial Entrepreneur
12 yrs Insurance & Consulting
EU & Asia
Abhishek Raju
Director, Partnerships & Founder
CFA
17 years Finance
Managing Partner Silver
Crescent Capital
M. Gopinath
CFO
Analyst, Political
Quotient
3 yrs Political Consulting
UN Youth Ambassador
India
Naga Sravan
VP, Policy & Govt Relations
Scientist, ISRO
7 yrs astrophysics
India
Ishan Tomar
CTO & Founder
Scientist, ISRO
7 yrs satellite remote
sensing
India
Vivek Gautam
Remote Sensing & Founder
Scientist, ISRO
5 yrs GIS
India & Canada
Rashmit Singh
Global Head Product MGMT
Scientist, NCFC
6 yrs Crop Modelling
India
Pradeep Bisen
Agriculture Lead
The Team

18. www.satsure.co
info@satsure.co

19. !19
Appendix

20. !20
1. Ichchapuram
2. Kanchili
3. Kaviti
4. Mandasa
5. Bhamini
6. Sompeta
7. Palasa
8. Kothuru
9. Meliaputti
10. Seethampeta
11. Pathapatnam
12. Vajrapukothuru
13. Hiramandalam
14. Veeraghattam
15. Nandigam
16. Saravakota
17. Vangara
18. Tekkali
19. Palakonda
19. Palakonda
20. Sarubujjili
21. Santhabommali
22. Jalumuru
23. Regidiamadalavalas
24. Burja
25. Kotabommal
26. Santhakaviti
27. Rajam
28. Narasannapeta
29. Polaki
30. Amadalavalasa
31. Ganguvari Singadam
32. Srikakulam
33. Ponduru
34. Gara
35. Etcherla
36. Laveru
37. Ranastalam
Crops Area (in Ha)
Maize 22,997.48
Paddy 8,701.85
Ragi 4,197.50
Pulses 93542.06
Crop Acreage

21. 19th Dec 2017 29th Dec 2017
03rd Jan 2018
08th Jan 2018 18th Jan 2018
23rd Jan 2018
!21
Crop Health

22. Cropping Intelligence
!22!22
Sowing Progress
Harvesting Progress
22K Ha 109K Ha 201K Ha
40K Ha 81K Ha 102K Ha

23. Crop Risk - Yield Estimation
!23!23
• Higher accuracy of yield estimates
• Reduce number of CCEs by 70%
SMART SAMPLING OF CROP
CUTTING EXPERIMENTS
• Reducing the cost of insurance
administration
• Setting threshold crop yield with
better accuracy
SRIKAKULAM
In-Season Crop State

24. Crop Risk Zoning
!24
Risk rating of crops :
• Time Series Rainfall Data
• % irrigation
• Crop Yield
• Crop Prices
• Crop Feasibility Analysis

25. www.bestppt.com
CCE Optimisation
!25
Stratified multi-stage random sampling technique
Create crop-wise CCE plans
Remove bias in selected points.

26. www.bestppt.com
Damage Assessment
!26
Assess intensity of crop damage for insurance claims settlement
• Mid-Season Calamity
• Post-Harvest Loss

27. !27
Cyclone Titli Impact
Pre-Cyclone Flooding
Highlights
• 11 Oct : Cyclone Landfall
• Sentinel-1 radar satellite
data used due to clouds
Timelines
• 10 Oct : Satellite imagery
- Inundation due to leading
cyclone clouds
• 17 Oct : Satellite imagery
- Post cyclone inundation
assessment
• 18 Oct : Portal Live
• 25 Oct : Sentinel-2 Optical
data
- Final assessment based
on yield loss
Post-Cyclone FloodingCropped AreaDamage

28. !28
Flood Response

29. Dispute Resolution
!29
Objective
1. Validate reported CCE historical data
2. Create alternate adjudication data for missing CCE data
Proposed Methodology
Use satellite based yield esitmates
Plan
1. Estimate yield by using the satellite data based yield model from
CCE Optimisation.
2. Average yield for the village can be calculated after removing
outliers. This yield can be checked against threshold yield agreed
to in the contract
3. If the yield for a particular CCE is disputed, accept reported yield
a. within the upper & lower limits after removing outliers or
b. within 3 SD of average yield calculated above

30. Srikakulam Land Classification
Kharif 2017
!30
Class Area (ha) Class %
Crop land 3,18,675.15 54.91
Deciduous Forest 89,920.89 15.49
Scrub Land 52,167.06 8.99
Plantation/ Orchard 36,995.22 6.37
Fallow land 34,085.61 5.87
Water Bodies 22,676.58 3.91
Other Waste Land 12,493.35 2.15
Built-up + Roads 11,232.18 1.94
Riverbed 1,886.76 0.33
Shrubs 202.5 0.03
Crop land
55%
Built-up
15%
Water Body
9%
Fallow land
6%
Deciduous Forest
6%
Plantation/ Orchard
4%
Other Waste Land
2%
Riverbed
2%