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Improving food security

through satellite information

http://www.riicevn.org

Philippines

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 accurate 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.

Government seeks

better information on rice growth

through satellite imagery ...

Insurers offer crop insurance …

… to protect farmers

in case of natural catastrophes.

… and plan for expected yields

or shortcomings thereof.

Large scale, high resolution is processed

by using Mapscape software

Remote sensing enhances the power of crop modelling

Smartphone based rapid data collection

This presentation had been designed for the RIICE team by Thomas Maxeiner from CreativeRepublic

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.

RIICE prefers to use radar-based satellites for cloud-free results

Radar backscatter

Grain filling

Time

Harvesting

Tillering

Flowering

Sowing

Stem extension

Vietnam

Our Products:

RIICE Maps & Yield Estimates

Implementing Partners

http://philippinericeinfo.ph

02/2012

Phase I: Test phase

Technical proof of concept;

«dry-test» of satellite-supported

insurance products.

Bangladesh

India

Thailand

Vietnam

Cambodia

Indonesia

Philippines

Digitalising the crop information value chain

Better performance through calibrating by fieldwork

Scientific data collection delivers a permanent better accuracy rate

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.

Sentinel-1

  • 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

http://www.esa.int/spaceinimages/Images/2014/01/Sentinel-1_radar_vision

RIICE supports its partners

in all stages of the risk management cycle

Relevance

Description of fieldwork activity

Next season change

Rice area validations should always be performed

Rice Area

and location

validation

Once established, further LAI collection is optional

Rice/non-rice classification (RnR); accuracy assessment through confusion matrix

Sample method: 10 rice + 10 non-rice points per sub-district unit

Timing: RnR is taken towards the end of the season, retrospective sampling possible

Standard LAI protocol using smart-phones

Sample method: same as above

Evaluation: 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

Leaf Area

Index

validation

RIICE participates AIC in “witnessing” government CCEs

Crop

Cutting

Experiments

National Partners

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.

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.”

Optical

remote

sensing

Synthetic

aperture

radar (SAR)

Active sensors are weather and sunlight independent: artificial microwave radiation can penetrate clouds, light rain and snow.

Passive sensors cannot be operated in the night and in the case of cloud coverage, often during cropping season.

Main backbone of RIICE is the Sentinel Mission of ESA

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, development, and water balance of lowland rice under conditions of potential production and water and/or nitrogen limitations.

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.

Our results

and key achievements

}

Cloud platforms

scalable reliable

and always on

Sentinel is key to ORYZA crop growth model

More information about the input parameters and their spatial and temporal resolution

05/2015

12/2019

Phase II: Scale-up phase

Nation-wide upscaling

of yield monitoring in collaboration

with governments and application

in insurance programmes.

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

Crop calender & practice input

Different stages of crop development can be detected when images are taken throughout the season

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.

Cambodia

IRRI‘s crop modeling tool ORYZA

RIICE works with the experts and the knowledege of the world’s premier research organization

India/Odisha

Copy here

Thailand

Better forecast – better management – better results

An enhanced approach using remote sensing data delivers much more precise results

Satellite derived information

Area info

Biomass info

Time info

Spatial info

Spatial information locates field.

Output is rice location. Checked with field work.

All satellite-derived information is available at 20m resolution (per pixel) if based on Sentinel satellites. Output is rice area.

Information on key dates within the cropping season.

Most important output is start of season date.

Leaf area index (derived from satellite) measures green leaf per unit surface. Output is LAI (not yield!); initially calibrated through field work.

Weather data

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.

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.)

How the technology works

Here is the Headline

Here is the Subheadline

Since Rice is mostly grown during the rainy season: Radar data is particularly needed in Asia.

Crop Growth Simulation Model (CGSM)

CGSM + SAR

Better identification

and classification of

yield range

Our Products:

RIICE Dashboards

Producing a better accuracy rate

Three years of RIICE-fieldwork created well working data base

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.

17 million hectare rice area monitored each year since 2013

Commitments by governments and/or insurance companies in India and Vietnam to use the technology in their respective crop insurance programmes

Considerable investments

from the Philippines and

Odisha governments to scale

to a state-wide operational system; increasing in-kind and financial investments from all other countries

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

India/Tamil Nadu

Project Timeline

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