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


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?


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)



• 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


• 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


• 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

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


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

19


Optical remote sensing

20


Rice from optical satellites

21


Clouds are a problem

22


Radar remote sensing

23


Rice from optical and radar RS

24

What the satellite is „seeing“.


How does radar work?• Water does not reflect any signal back to

the satellite – the image is black

25



How does radar work?• If there is a young rice crop, then some

signal is reflected back – the image is gray

26



How does radar work?• As the crop grows, more signal is reflected

and the image become light gray/white

27

time

Rada

r bac

ksca

tter


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

28

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


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


Late June, most areas have just established the rice crop [Blue].

Some areas still in land preparation phase [Brown].


Rice monitoring examples

Crop Status in Leyte West – on 12 July 2012

waterbare soil

flooding


scenescence

harvest


Mid July, area is a mixture crop in tillering stage [green] and recently established crop [blue].




waterbare soil

flooding


scenescence

harvest


End July, most of the cropped area is in peak vegetation or flowering [dark green] and some still in tillering stage [green].



Crop Status in Leyte West – on 13 August 2012

waterbare soil

flooding


scenescence

harvest


Mid August, most of the cropped area is in peak vegetation [green] and tending to grain filling [yellow].



Crop Status in Leyte West – on 21 September 2012

waterbare soil

flooding


scenescence

harvest


Mid September, most areas in grain filling stage [yellow], some still in peak vegetation [green] and some already harvested [brown]



waterbare soil

flooding


scenescence

harvest



End September, mixture of grain filling stage [yellow], peak vegetation [green] harvested [brown]

A lot of variation in crop!



Leyte West – Date of Start of Season, 3m

June

September


Most areas planted in June, some in July and some even in August



Leyte West – Date of Peak, 3m

June

September


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.



39

time

Rada

r bac

ksca

tter


What about flood/drought• Flood and drought are detected when the

signal changes in an unexpected way

grain filling

harvesting


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


Example 1: Early season flood

water

bare soil

flooding

tillering – stem extension

peak

scenescence

harvest


© Cosmo-SkyMed data ASI distributed by e-GEOS, processed using MAPscape-Rice


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

42


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


Ground observations are vital

Field 20

Satellite Pass

Pre-flooding Flooding Establishment Pre-flowering Flowering Grain filling Harvest Post-harvest


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


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


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


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


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


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.


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


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


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


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


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


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


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.


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!


thank youwww.riice.org

Documents

WebGIS of RIICE