WebGIS of RIICE
description
Transcript of WebGIS of RIICE
The RIICE projectyear one results and observations
Andy Nelson
Acknowledgements• IRRI
– Tri Setiyono, Emma Quicho, Aileen Maunahan, Prosperidad Abonete, Arnel Rala, Hannah Bhatti, Jen Raviz, Pongmanee Thongbai, Nel Garcia, Gene
• sarmap– Francesco Holecz, Massimo Barbieri, Francesco Collivignarelli
• GIZ– Roman Skorzus, Jimmy Loro, Antonis Malagardis, Aniruddha
Shanbhag, Jutathup Tanyaphituick,
• Allianz– Michael Anthony, Thomas Heinz
• SDC– Ninh Nguyen, Yves Guinand
• National partners– PhilRice, PCIC (Philippines), ICALRD (Indonesia), CTU, IMHEN,
NIAPP (Vietnam), CARDI (Cambodia), RD, GISTDA, DOAE (Thailand), TNAU, AICI (India)
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Contents
• RIICE project overview• Remote Sensing background
information• Example products from the Philippines
– Remote sensing of rice area– Yield estimation – Tri Setiyono– Delivery via webGIS – Arnel Rala
• Accuracy of area and yield estimates• Results & observations so far
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Frequently asked questions
• How much area was planted this season?
• What was the yield in each town or province?
• Was production more or less than last year?
• Was the harvest early or late?• Was there a storm, flood or drought?
– Where and how much area was affected?
– How many tons of rice were lost?
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
What DO we know about rice?
Total value of crop production in USD per hec (for 120 crops) Value of rice production in USD per hec
value for all cropsvalue of rice
We know that it is valuable
Rice is valued at 150 billion USD a year
Rice is grown on 160 million ha a year
Rice is grown by 200 million farmers a year
We know many depend on it
PovertyEach dot represents 250,000 people living on less than $1.25 a day, 2005
Rice ConsumptionAnnual consumption per capita
<25kg 25-50 50-75 75-100 >100kg
90% of the world’s rice is produced and consumed in AsiaOver 70% of the world’s poor are in Asia
We know where/when it is grown
Rice is grown in every country in Asia
Rice is grown in every month of the year
Rice is important; politically, culturally, economically and nutritionally
We know rice is a vulnerable crop
Most of Asia’s rice is grown in the wet season
Rice is vulnerable to storm and flood damage
Late rainfall or lack of rain causes drought damage
Opportunity to develop low cost crop insurance
Crop Insurance – terminology
• Indemnity-based ‘traditional’ insurance– Single risk assessment and individual loss
assessment where payout is based on actual loss
• Named peril (i.e. flood), or multiple peril
• Index insurance– An index is used for settlement rather than
information from the insured unit• Weather index (rainfall, temperature)• Area-yield index (average yield per geographic unit)• Remote sensing (vegetation index, crop health)
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
• Big market 25 billion USD in premiums• Underdeveloped market in developing
countries• Government support to insurance
exceeds 50% of premium value!• Quality issues with the information
required• to develop insurance products • to assess losses and make quick
payouts• Crop insurance usually runs at a loss,
but opens doors to other products/loans/credit.
Crop insurance – provider viewpoint
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
• Rice farmers are generally poor• Marginal livelihoods• Vulnerable to natural catastrophes• Low access to credit or other
income• Crop insurance often viewed as
unavailable, expensive, not trusted, too slow to payout
Crop insurance – end user viewpoint
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
• We know how to use remote sensing and crop modelling to map and monitor rice
• Need to develop better and quicker estimates of crop area, production and loss
• Need a reliable way to deliver the estimates
• Need to reduce cost of monitoring and to design more efficient sampling/fieldwork
• Need to train and educate actors
Crop insurance – R&D viewpoint
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Remote Sensing based Information and Insurance for Crops in Emerging Economies
Public Private Partnership, 3 year project
1. develop technology for rice crop monitoring and damage estimation
2. demonstrate low cost and timely insurance solutions based on that technology
3. build partnerships and networks to deliver rice crop insurance solutions, and
4. obtain government level support for food security applications
The RIICE project
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
The RIICE project
National partners in eight regions across seven countries providing export knowledge, baseline data, fieldwork and monitoring
TNAU
BARC ?
Thai RD
CTU
IMHEN
PhilRice
IRRI
CARDI
ICALRD RIICE partners. Coordinated by IRRIUniversity or government agencies with expertise in agriculture and mandates related to rice agriculture.
The rice crop is observed by remote sensing and fieldwork through the season, resulting in rice crop status maps
The remote sensing data is linked to a crop growth model to estimate and forecast rice crop yield by district or village
Area, and yield information are used to develop insurance products that cover the farmer´s shortfall in production due to natural disasters.
Distribution channels (rural lending banks, cooperatives, rice mills) are being identified and trained to roll out the insurance product. Local insurers sell the
product through a distributor and reinsure the risk through an Allianz-led reinsurance pool.
From technology to deliveryREMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
we’ve come a long way since someone had the idea of strapping a camera to a pigeon
just some of the earth observing satellites in operation now
Remote Sensing basicsREMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Optical remote sensing
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice from optical satellites
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Clouds are a problem
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Radar remote sensing
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice from optical and radar RS
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What the satellite is „seeing“.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
How does radar work?• Water does not reflect any signal back to
the satellite – the image is black
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What the satellite is „seeing“.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
How does radar work?• If there is a young rice crop, then some
signal is reflected back – the image is gray
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What the satellite is „seeing“.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
How does radar work?• As the crop grows, more signal is reflected
and the image become light gray/white
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time
Rada
r bac
ksca
tter
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
How does radar work?• Different stages of the crop can be
detected if images are taken through the season = rice crop monitoring
grain filling
harvesting
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MAPscape-Rice & Oryza2000REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
• Crop calendar• Crop practices• Administrative units
Oryza2000 rice growth simulation model
• Meteo data• Soil data• Varietal data• Management data• Crop Cuts for yield validation
Yield estimation and yield forecasts Production and loss estimates
• Rice area map• Start and peak of season date maps• Rice crop status maps• Leaf Area Index maps• Flood and drought extent (area damaged)
MAPscape-Riceprocessing & product generation
Earth Observation data
Leaf Area Index field sites for calibration
waterbare soil
floodingtillering – peakscenescenceharvest
June 26 2012
High resolution information on crop status
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
From national to local scale
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Crop Status in Leyte West – on 26 June 2012
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Late June, most areas have just established the rice crop [Blue].
Some areas still in land preparation phase [Brown].
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Crop Status in Leyte West – on 12 July 2012
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Mid July, area is a mixture crop in tillering stage [green] and recently established crop [blue].
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Crop Status in Leyte West – on 28 July 2012
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
End July, most of the cropped area is in peak vegetation or flowering [dark green] and some still in tillering stage [green].
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Crop Status in Leyte West – on 13 August 2012
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Mid August, most of the cropped area is in peak vegetation [green] and tending to grain filling [yellow].
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Crop Status in Leyte West – on 21 September 2012
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Mid September, most areas in grain filling stage [yellow], some still in peak vegetation [green] and some already harvested [brown]
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
waterbare soil
flooding
tillering – stem extensionpeak
scenescence
harvest
Crop Status in Leyte West – on 30 September 2012
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
End September, mixture of grain filling stage [yellow], peak vegetation [green] harvested [brown]
A lot of variation in crop!
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Leyte West – Date of Start of Season, 3m
June
September
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Most areas planted in June, some in July and some even in August
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
Leyte West – Date of Peak, 3m
June
September
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Peak vegetation or flowering occurred in July and August in most places but some patches were in June and some were very late in September.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Rice monitoring examples
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time
Rada
r bac
ksca
tter
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
What about flood/drought• Flood and drought are detected when the
signal changes in an unexpected way
grain filling
harvesting
Crop Status in Leyte West – on 12 July 2012
water
bare soil
flooding
tillering – stem extension
peak
scenescence
harvest
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-Rice
Flood damage?40% of the rice area affected
BUTHalf of those fields had not yet planted rice when the flood occurred
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Example 1: Early season flood
water
bare soil
flooding
tillering – stem extension
peak
scenescence
harvest
Crop Status in Leyte West – on 30 September 2012
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-Rice
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Example 2: Late season flood
Flood damage?60% of the rice area affected
BUT25% had already harvested and did not lose their crop due to the flood
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REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Importance of monitoring
A map of where rice is grown not enough!
Accurate loss estimates require information on the status of the crop at the time of the calamity. These estimates are tabulated at municipal level
Flood DateRice
cultivation area
Rice cultivation
area affected
Planted rice area affected
Difference in damage estimate
12 Jul 2012 5,600 ha 40% 2,240 ha
20%1,120 ha 1,120 ha
30 Sep 2012 5,600 ha 60%
3,360 ha45%
2,520 ha 840 ha
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Ground observations are vital
Field 20
Satellite Pass
Pre-flooding Flooding Establishment Pre-flowering Flowering Grain filling Harvest Post-harvest
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Ground observations are vital
...
Field 1Field 2Field 3
...
...
Regular monitoring is key
• Monitoring from space and from the field
• Regular field visits improve the RS products and validates their accuracy
• Flood and drought can be detected with RS
• Area lost to flood/drought can only be assessed by remote sensing if images are available throughout the season
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Crop modelling basics
• ORYZA2000 crop growth simulation model, 30 years of research and development
• Requires input information on daily weather, establishment date, soil, nutrients
• Can estimate yield under water and nutrient limitations, i.e. it is a measure of ‘obtainable yield’ assuming everything else is okay (no pest/disease etc.)
• Remote sensing information used to improve the yield estimation by introducing crop establishment date and observed crop growth status on key dates
• Remote sensing can thus capture spatial variability and introduce that into the model that would otherwise be unknown
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
MAPscape-Rice & Oryza2000REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
• Crop calendar• Crop practices• Administrative units
Oryza2000 rice growth simulation model
• Meteo data• Soil data• Varietal data• Management data• Crop Cuts for yield validation
Yield estimation and yield forecasts Production and loss estimates
• Rice area map• Start and peak of season date maps• Rice crop status maps• Leaf Area Index maps• Flood and drought extent (area damaged)
MAPscape-Riceprocessing & product generation
Earth Observation data
Leaf Area Index field sites for calibration
LAI - Leaf Area Index
• LAI is area of plant leaf per unit ground area.• From 0 to 7 m2/m2 for a rice crop• LAI represents plant’s ability to absorb and
use sunlight, hence a predictor of yield• LAI is measured in the field and used to
calibrate a ‘cloud model’ to map LAI using the RS imagery
• Thus RS imagery allows us to estimate LAI at key growth stages across a wide area
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
CCE - Crop Cut Experiments
• Crop yield at the end of the season• Measured in the field • Rainfed yields are typically 2 to 4 t/h,
irrigated yields are typically 4 to 8 t/h in our study sites
• This is our validation data for yield accuracy assessment
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Mapping Leaf Area Index REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Leyte West – Leaf Area Index, 13 August 2012, 3m
Leaf Area Index
< 1 1 – 2 2 – 3 3 – 4 4 – 5 > 5
© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-RICE
Day of Year
180 200 220 240 260 280
LA
I (m
2/m
2)
1
2
3
4
5
Yie
ld (
t/h
a)
1
2
3
4
5
6
7
8
Days after transplanting
-20 -10 0 10 20 30 40 50 60 70 80 90
LAI O2K LAI O2K + RSCSK-LAIYield O2KYield O2K + RSYield Obs.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Yield: ORYZA2000+RS
• Area base yield index (ARBY) insurance in Leyte, Philippines.
• ARBY does not provide pure indemnity for each individual insured farmer.
• It is not necessary to inspect individual farms either before coverage begins or in the event of potential loss.
• No loss is paid to any farmer in an area unless and until the average yield of that area as a whole falls below the expected or ‘insured’ yield.
What about crop insurance?REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Insured NIS in LeyteREMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Partners in ARBY
• Philippine Crop Insurance Corporation (PCIC): Insurer• DA BAS: Yield Identifier • Metro Ormoc City Credit Cooperative, Inc. (OCCCI):
Distribution Channel • GIZ: Product design, implementation and oversight • National Irrigation Administration 8 (NIA 8): yield history
• NIA/BAS provide the yield history to set the trigger yield• BAS provide the yield data for each NIS which is used to
determine payouts
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Past payouts• Result of ARBY in 2011:
NIS
2011 DA BAS ARBY kilos / hectare, wet
cropping
Average Area (NIS)
Yield in cavans
ARBY Trigger Yield in
cavans, at 80%
coverage
Difference
Payout / hectare at
80% coverage
Bao 2,953.46 65.63 63.464 2.166 0
Mainit 2,628.38 58.41 63.464 -5.054 P 799.00
Hindang-
Hilongos3,947.16 87.71 61.336 26.379 0
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
RIICE and ARBY
• 2012 Wet Season• Compare the ARBY area and yield estimation
method with the RIICE area and yield estimation method
• ARBY method used to determine payout, but RIICE used along side to build confidence in the approach (RS-ARBY)
• sarmap maps the rice area• PhilRice monitors the sites, yield data for
payout• IRRI estimates yield and puts all results on
WebGIS• GIZ, PCIC and partners assess results
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
NIS and crop cut locationREMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
ARBY CCE samplesREMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Irrigation System Barangay MunicipalityInbred Hybrid Total
MAINIT PONGSO Magsaysay Alang-Alang 5 5 10P.Barrantes Alang-Alang 3 6 9Cuta & New Road Barugo 9 8 17Bairan San Miguel 2 5 7
Sub-total 19 24 43BAO Liloan Ormoc City 3 15 18
Sabang Ba-o Ormoc City 13 4 17Matica-a Ormoc City 5 16 21
Sub-total 21 35 56HINDANG HILONGOS Magnangoy Hilongos 15 2 17
Doos del Sur Hindang 8 0 8Sub-total 23 2 25
Total 63 61 124
Number of Samples
Yield accuracy results 1REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Preliminary accuracy assessment of RS-based rice yield estimation
Yield accuracy at barangay level
Barangay Yield (ton/ha) RMSE (kg/ha)
ARBY CCE RS estimate 702
Amahit 2.96 1.94 Accuracy (%)
Cuta 3.79 4.32 85
Liloan 5.96 5.04
Matica-a 5.14 5.69
Sabang Ba-o 4.99 4.94
Yield accuracy results 2REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Preliminary accuracy assessment of RS-based rice yield estimation
Yield accuracy at municipal level
MunicipalityYield (ton/ha) RMSE (kg/ha)
ARBY CCERS
estimate 392
Barugo 4.59 4.38 Accuracy (%)
Ormoc City 5.36 4.85 92
Municipal yield accuracy assessment is based only on those barangays where we had both CCE and model data
Comparison of yields/triggers
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Comparison of ARBY yield and Remote Sensing yield for 2012 WS
How do the ARBY and RS yields compare to the triggers?
MunicipalityYield trigger
at 95%(ton/ha)
Yield (ton/ha)
ARBY RS estimate
Barugo 3.62 5.12 5.63
Ormoc City 3.74 5.31 5.56
Agreement between ARBY and RS yield estimates
No payout in WS 2012
Next Steps
• Creation of a Business Model to support use of Remote Sensing for Crop Insurance
• RS-ARBY Product approval by Insurance Commission; and PCIC Board
• Roll out of the RIICE WebGIS• Finalization of the crop model and yield
forecasting via Remote Sensing by sarmap/IRRI/PhilRice
• RS-ARBY Product development 2013-2014• RS-ARBY used for yield identification in
2014
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Observations on PPP
• Lack of clarity over respective roles of private and public sector
• Need for government support– Data infrastructure– Education, training, capacity building– Technical support on product design– Creation of enabling legal / regulatory framework
• Private partners often the innovators in distribution channels and delivery mechanisms
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Observations on PPP
• Expectations from the private sector are high Remote sensing is not a miracle solution– Monitoring from space still needs monitoring
on the ground for validation.
• Moving from demonstration to operation– Technological development is relatively easy
• Confidence building is critical• Everyone wants to know the accuracy &
cost.
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
How accurate is the method?REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Level of detailRIICE products
Area Yield Flood area
Drought area
Field 85% NA
Barangay 85%
Municipality 92%
Province 98%
Area accuracy assessment requires comparison data from BAS or another sourceProvincial yield estimate is based only on those municipalities where we ran the model
Accuracy of area, yield and damage: actual and targetted
How accurate is the method?REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
Level of detailRIICE products
Area Yield Flood area
Drought area
Field 85% NA 90% 80%
Barangay 90% 85% 95% 85%
Municipality 95% 92% 98% 90%
Province 98% 98% 99% 92%
Area accuracy assessment requires comparison data from BAS or another sourceProvincial yield estimate is based only on those municipalities where we ran the model
Accuracy of area, yield and damage: actual and targetted
Implications for insurance?
• These are the first results from the first season of RIICE.
• Accuracy of area and yield are good.• ARBY and ARBY-RS agree: no payout.• Require four seasons of testing and
development• No reported flooding in 2012. No one
wants a flood, but it’s hard to test the full method without one!
REMOTE SENSING-BASED INFORMATION AND INSURANCE FOR CROPS IN EMERGING ECONOMIES
thank youwww.riice.org