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Michael Anthony

on 3 July 2018

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Transcript of RIICE

Our Products:
RIICE Maps & Yield Estimates
With the RIICE products governments are able to forecast and measure the rice production in their countries. Thus they are able to plan for expected yields or shortcomings of yields in a serious manner. Moreover governments can use RIICE products to assist the population in cases of disaster like floods.
Government seeks
better information on rice growth
through satellite imagery ...

… and plan for expected yields
or shortcomings thereof.

Insurers offer crop insurance …
… to protect farmers
in case of natural catastrophes.
Improving food security
through satellite information

National Partners

Implementing Partners
Project Timeline
Phase I: Test phase
Phase II: Scale-up phase
Technical proof of concept;
«dry-test» of satellite-supported
insurance products.

Nation-wide upscaling
of yield monitoring in collaboration
with governments and application
in insurance programmes.
RIICE supports its partners
in all stages of the risk management cycle
Digitalising the crop information value chain
Our results
and key achievements
17 million
hectare rice area monitored each year since 2013
Considerable investments

the Philippines
Odisha governments
to scale
to a state-wide operational system; increasing in-kind and financial investments from all other countries
Commitments by governments and/or insurance companies in India and Vietnam to use the technology in their respective
crop insurance programmes

Highly visible outputs, especially related to
flood and drought damage assessments
: Application of technology for better disaster assessment in Cambodia, the Philippines, Thailand, Tamil Nadu and Vietnam
Our Products:
RIICE Dashboards
Based on RIICE:
Goals and Objectives of the Philippine Rice Information System (PRiSM)
Map rice areas by season in the Philippines using
remote sensing imagery
Develop and validate methods for
estimating rice yields
using combined remote sensing/crop modeling approaches.
Characterize production situations and
quantify pest risks
in farmers’ fields in the Philippines.
Develop a web-based monitoring system that provides

estimates of rice area, rice yield, and production loss due to flood or drought
based on a combined remote sensing and crop modeling approach.
Improve the
capacity for rice crop monitoring and pest surveillance
at PhilRice and the DA by conducting training courses.
RIICE: Mekong River 2015/2016 drought less severe than thought
RIICE compared areas planted during the same season of the previous year and found out that yields were also 6% lower compared to the previous year’s season – even though salinity in the water also accounts for that.
RIICE compared areas planted during the same season of the previous year ...
... and found that yields were also 6% lower when compared to the previous year’s season though salinity in the water also accounts for that

India/Tamil Nadu
Government of Odisha invests in Odisha Rice Monitoring System. RIICE products implemented in one of the leading Indian rice states
The Indian State of Odisha is one of the leading rice producing states in India. In November 2015 the Chief Minister of Odisha decided for a rice monitoring system with Sarmap and IRRI to use its results for the crop insurance programme.
How the technology works
Large scale, high resolution is processed
by using Mapscape software
Remote sensing enhances the power of crop modelling
Smartphone based rapid data collection

Cloud platforms
scalable reliable
and always on
Main backbone of RIICE is the Sentinel Mission of ESA
RIICE prefers to use radar-based satellites for cloud-free results
Since Rice is mostly grown during the rainy season: Radar data is particularly needed in Asia.
Crop calender & practice input
Different stages of crop development can be detected when images are taken throughout the season
Radar backscatter
Stem extension
Grain filling
IRRI‘s crop modeling tool ORYZA
RIICE works with the experts and the knowledege of the world’s premier research organization
Sentinel is key to ORYZA crop growth model
More information about the input parameters and their spatial and temporal resolution
Granularity of weather data has an impact on the precision of yield results but it is not the key driver. Granularity refers to spatial resolution, temporal resolution and type of weather data.
Weather data
Rice Agronomic Management Settings
Amount and timing of fertilizer applications, rice variety use (yield potential, growth duration), crop establishment method, water management/ecosystem (fully irrigated, partially irrigated, etc.)
Information on key dates within the cropping season.
Most important output is start of season date.
All satellite-derived information is available at 20m resolution (per pixel) if based on Sentinel satellites.
Output is rice area.
Spatial information locates field.
Output is rice location.
Checked with field work.
Satellite derived information
Leaf area index (derived from satellite) measures green leaf per unit surface.
Output is LAI (not yield!); initially calibrated through field work.
Area info
Time info
Biomass info
Spatial info
Better forecast – better management – better results
An enhanced approach using remote sensing data delivers much more precise results
Better performance through calibrating by fieldwork
Scientific data collection delivers a permanent better accuracy rate
Producing a better accuracy rate
Three years of RIICE-fieldwork created well working data base
Between 2013-2015 RIICE executed field measurement at 1,300 different spots in the project region. This extensive fieldwork and research to validate the results and calibrate and improve the model led to prediction accuracy results of over 90%. Now less fieldwork is necessary.
Better results by
leveraging new technologies
Turning the value chain
of crop information digital
Combining satellite data with the scientific input of the IRRI crop model and with the fieldwork collected by rice experts, RIICE produces highly valuable results for governments, insurers and millions of
rice farmers in South East Asia.

2016 drought in Cambodia resulted in delayed the planting.
RIICE map also indicates a different irrigation system compared to Vietnam
RIICE Maps show: Compared to the previous years, farmers delayed the planting by several weeks. Some farmers missed out on planting altogether. But the impact on yield is likely to be minor and RIICE will establish expected production information shortly in the mid-season yield forecast.
Early Wet Season 2016 – April Drought
Information delivered by RIICE to MAFF, three times during the peak of the drought to observe the delayed start of the season. The information was delivered in form of an information Bulletin.
Main tool for provision of input, seeds and seedlings /
RIICE helps Government of Tamil Nadu in quickly directing flood relief measures
“After RIICE had been delivered the satellite data to the state level emergency authorities, the Government was able to send the most needed material to the flooded families: 50 metric tons of rice seed and 30,000 vegetable seedlings in the district of Cuddalore in Tamil Nadu Province.”
20m resolution, SAR sensor – perfect for rice
Free and open access to imagery
Data free of charge until 2030 secured
12 day repeat frequency
Further satellites will increase monitoring capabilities to one image every 6 days

Photo: ESA, © ESA/ATG medialab
Crop Growth Simulation Model (CGSM)
Better identification
and classification of
yield range
Passive sensors cannot be operated in the night and in the case of cloud coverage, often during cropping season.

Active sensors are weather and sunlight independent: artificial microwave radiation can penetrate clouds, light rain and snow.
radar (SAR)
ORYZA is a product of
more than a decade
of improvements and testings by various scientists and researchers. Tracing its beginnings from a simple model for potential growth and production, it has become a
comprehensive rice modeling tool
applicable for different scenario analysis. It is used to
simulate the growth
, and
water balance
of lowland rice under conditions of potential production and water and/or nitrogen limitations.
Next season change
Description of fieldwork activity
Rice Area
and location
Leaf Area

Rice/non-rice classification (RnR); accuracy assessment through confusion matrix
Sample method:
10 rice + 10 non-rice points per sub-district unit
RnR is taken towards the end of the season, retrospective sampling possible

Standard LAI protocol using smart-phones
Sample method:
same as above
RMS based on ground data vs SAR data

Still taken only in India.
Sample method:
For cost reasons, CCEs are only taken in India: 3 or 4 samples of 2.5m*2.5m in one field, 6-13 per district.
Major weakness:
the low number of sampling points and the subjectivity of the surveyor

Rice area validations should always be performed
Once established, further LAI collection is optional
RIICE participates AIC in “witnessing” government CCEs
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This presentation had been designed for the RIICE team by Thomas Maxeiner from CreativeRepublic
Full transcript