Integrating satellite-based mapping and field observations for improved decisions on rice

We need information for improved decision making on rice

We need information for improved decision making on rice

accurate accurate, detailed accurate, detailed, timely accurate, detailed, timely, location-specific

like remote sensing, crop modeling, smartphones and web platforms… to generate information on the where, when, and how much of rice, as well as crop health and flood/drought damage assessments.

philippinericeinfo.ph

Remote sensing based Information and Insurance for Crops in Emerging economies

www.riice.org

Leveraging on new technologies…

Field surveys • Location • Field characteristics • Crop growth • Yield (Crop cut) • Animal pests,

diseases and weeds

Smartphone-based surveys

Smartphone-based surveys

Farmer interviews • Crop

management practices

• Farmer’s estimated yield

• Main – RIICE/PRISM ODK Collect – Pocket LAI

• Support – Zxing Barcode reader – GPS Status – Aldiko Book Reader

• Optional – Camera FV Lite – GPS Essential

Applications

ODK collect

Pocket LAI

• An app that indirectly estimates leaf area index (LAI).

• Application developed by the University of Milan

© ESA/ATG medialab www.esa.int/spaceinimages/Images/2014/02/Sentinel-1

Remote sensing

Sentinel-1a • Launched 3rd/April/2014 by ESA • 12 day repeat frequency • 20m resolution • Free and open access to imagery • SAR sensor – perfect for rice • A second satellite - Sentinel-1b - will further increase monitoring capabilities, one image every 6 days

Coverage

radar images vs optical images

Settlements, built up areas

Canals/Rivers

Ponds, aquaculture

Rice area, colours indicate different

planting dates

Rice paddies and bunds

Nam Dinh, Vietnam: images on May 26, July 13 and July 29

One hectare and one square km

SAR data in RIICE are provided by ASI/e-GEOS and GISTDA from COSMO-SkyMed and by InfoTerra GmbH from TerraSAR-X.

Mapping rice from space

Changes in these images over time are used to map where rice is grown, when it is grown and how much rice is harvested

Preparation Emergence

Tillering

Flowering

Start of Season (SoS) Peak of Season (PoS) End of Season (EoS)

Ripening

Day 16 Day 32 Day 48 Day 64 Day 80 Day 96 Day 1

Inte

nsity

of t

he s

igna

l Continuous monitoring through the season

Seasonal Rice Area (RA)

Harvesting

ORYZA2000 (IRRI)

MAPscape-RICE

(sarmap)

Variety

Meteo

Variety

Management

Field results

Yield estimation

Rice-YES

Where? Rice area

Overall accuracy 88%

Central Luzon, Philippines

When? Planting dates & seasonality

0

20

40

60

80

100

120

Jun Jul Aug Sep

Area

pla

nted

to ri

ce (

000

ha)

Month

Central Luzon, Philippines

How much? Yield forecasts and estimates

Accurate, detailed and timely data on yield Our forecast: Oct 2014 = 5.40 t/ha Government forecast: Oct 2014 = 5.48 t/ha Our end-of-season estimate: Nov 2014 = 5.30 t/ha Government estimate: Mar 2015 = 5.31 t/ha Accuracy = 86%

Central Luzon, Philippines

Eastern Leyte, Philippines

Typhoon Haiyan

(Nov 2013)

Flood Map

Data on animal pest injuries

0

5

10

15

20

Inci

denc

e %

Disease Pest injury

Floridablanca, Philippines, 2014 WS

Incidence of insect pest injuries and diseases

Delivery of information

Server and Database system

Secondary data

Smartphones

Satellite

Web-based system

Delivery of information

Information are available for the following countries: Cambodia, Indonesia, Philippines, Thailand, Vietnam, and India

Bridging data and users

Satellite data

Weather, soil, etc.

Field measurements

Remote sensing

Crop modeling

GIS & WebGIS

Databases

Models

Expert knowledge

Rice area & planting dates

Actual yields & forecasts

Yield gaps & causes

Flood & drought

Pests & diseases

Data Technology Information Users

Statistical agencies

Policy makers

Disaster response team

Crop insurance providers

Researchers

Remote sensing based Information and Insurance for Crops in Emerging economies.

philippinericeinfo.ph www.riice.org