background - food and agriculture organization...- advantages of earth observation by satellite -...

A new method to estimate rice crop production and outlook

using Earth Observation satellite data

Toshio Okumura, Shin-ich Sobue, Nobuhiro Tomiyama RESTEC

Kei Ohyoshi JAXA

17 Feb. 2014

Don Chan Palace, Vientiane

Background

2

Why do we research and develop

agricultural remote sensing ?

• The G20 Agriculture Ministers agreed on an

“Action Plan on food price volatility and

agriculture” in June 2011.

• The action plan was submitted at a Summit

in November 2011.

• In order to improve crop production

projections and weather forecasting, the use

of modern tools was promoted, in particular

remote sensing.

3

Background - Food security -

44. We commit to improve market information and transparency in order to make international markets for agricultural

commodities more effective. To that end, we launched:

• The “Agricultural Market Information System” (AMIS) in Rome on September 15, 2011, to improve information on

markets. It will enhance the quality, reliability, accuracy, timeliness and comparability of food market outlook information. As

a first step, AMIS will focus its work on four major crops: wheat, maize, rice and soybeans. AMIS involves G20 countries

and, at this stage, Egypt, Vietnam, Thailand, the Philippines, Nigeria, Ukraine and Kazakhstan. It will be managed by a

secretariat located in FAO

• The “Global Agricultural Geo-monitoring Initiative” (GEO GLAM) in Geneva on September 22-23, 2011. This initiative

will coordinate satellite monitoring observation systems in different regions of the world in order to enhance crop production

projections and weather forecasting data.

Part of the G20 Head of States Declaration:

2007-2008

@ Natural disaster ?@ Oil price rise ?@ Speculation ?

• The GEOGLAM serves as a useful input for the AMIS. (four

type of commodity crops – wheat, maize, rice, and

soybeans)

• Since rice is the main commodity crop in Asia, JAXA

proposes and leads the Asian Rice Crop Estimation &

Monitoring project (Asia-RiCE) for GEOGLAM.

• Asia-RiCE is a collaborative effort between a number of

Asian organizations.

4

Background - Remote sensing tec. for agriculture -

RESTEC provides the technical and administrative support for

JAXA’s lead role in Asia-RiCE.

RESTEC

• Developed a system to estimate rice crop acreage using

GIS and space-based remote sensing data for the Ministry

of Agriculture, Forestry, and Fisheries (MAFF),

• Developed software to estimate rice crop acreage and

production using space-based Synthetic Aperture Radar

(SAR), named INAHOR for JAXA,

• Additionally, implemented a satellite-based agricultural

weather information system, named JASMIN for JAXA,

• And is supporting JAXA’s activities related the food security.

5

Background - Remote sensing tec. for agriculture -

• Methodology to estimate rice crop acreage and production

using space based microwave radar (SAR), which we

developed

• Results obtained by using the method

• Rice crop outlooks using JASMIN, which is one of current

activities

6

Today’s topics;

Methodology

7

A new method to estimate rice crop acreage and production

using space based microwave radar (SAR)

8

Methodology - Advantages of Earth Observation by Satellite -

Approx. 600-800 km height from the Earth’s surface

Earth Observation satellites can collect the information:

‒ Over a broad area, even if the area is difficult to

access,

‒ Periodically,

‒ With high consistency,

‒ In near real-time,

‒ Cost-effectively.

9

Methodology - Advantages of Earth Observation by Satellite -

Space based remote sensing technology should be very

powerful tool for agriculture monitoring in national and

provincial level.

3

SARMicrowave Radiometer

RADAR Optical Sensor(Global Imager)

Optical Sensor(High Res.)

Agro-meteorologicalMonitoring

Paddy Field Mapping TopographyFlood Monitoring Crop Growth

Agricultural Stat Early Warning Damage AssessmentLand Resource Management

Agricultural Applications

Products from satellite data

Satellites/Sensors

10

Methodology - Advantage of synthetic aperture radar (SAR) -

Ear of rice

Optical sensor SAR

Sunlight Active radar

Microwave can penetrate the cloud.

Observed simultaneously

ALOS AVNIR-2 : Optical sensor can

not observe the ground under the

cloud.

ALOS PALSAR : SAR can observe the

ground under the cloud.

• For Agriculture monitoring, we need time series data

constantly.

• Since however, in Asia, many crops mainly grow in rainy

season, it is very difficult to monitor the crop situation only

using optical sensors.

Therefore, we mainly applied SAR data to estimate rice crop

area and production in Asia.

11

Methodology - Advantage of synthetic aperture radar (SAR) -

12

Methodology - Basic approach to estimate paddy area using SAR -

[Toan et al., 1997]Life Cycle of Rice

FloodingSowing/

Transplanting Mature

SpecularReflection

WeakBackscatter

StrongBackscatter

dark bright brighter

110-120 days (in Tropics)

Vegetative

stage

We can estimate the paddy area by detecting the dark areas in flooding / planting stage and by detecting

the bright areas in vegetative stage from SAR image data.

little dark

Planting

stage Flooding

stage

bright

Planting

Minimum

Range(Max-Min)

Maximum

FloodingPhenological

StageVegetative

Backscatter

13

Methodology - Basic approach to estimate paddy area using SAR -

If (Minimum < Threshold1) and (Range > Threshold2)

Paddy Area (Flooding / Planting stage) (Vegetative stage)

Paddy area has “Flooding” and “Vegetative” stages.

SAR Imageover Paddy

Results

14

The estimation result of rice crop acreage and/or production

using the new method in Thailand

• From 2011 to 2012, a collaborative project of ALOS series

and THEOS series was conducted by JAXA and Geo-

Informatics and Space Technology Development Agency

(GISTDA) of Thailand.

• We verified the new method with SAR data, in the rice crop

working group of the project under contract to JAXA.

• Target area were

‒ Khon kaen province,

‒ Suphan buri province,

‒ and Thailand whole country.

• Target season is rainy season.

15

Results - JAXA & GISTDA Cooperation project -

Khon kaen

Suphan buri

• Khon Kaen is located in the northeastern part of Thailand.

• The feature is that most of the paddy fields are rainfed.

16

Results - In Khon kaen -

Khon kaen

We conducted field survey for about 200 fields for validation.

17


Automatic data collection by field router

• meteorological sensor

‒ Air Temperature,

‒ Soil Temperature,

‒ Precipitation (rain gauge),

‒ Radiation,

• Image sensor (CMOS camera)

18


19 Apr 2011 20 May 2011 20 Jun 2011

14 Jul 2011 5 Aug 2011 7 Aug 2011 31 Aug 2011

5 Aug 2011

We periodically checked water

situation and type of planting.

19


We measured crop yield (production per area) by cutting rice plant in

harvest stage.

20


Cutting Drying in the sun Drying in the machine

Threshing Measuring

TD

pondpond

TTTTTTTTTTTTDTD

D+TDTT

D+TD+TDDDDDDTTTDDDD

D+TDDDDTDD

D+TD+TD+TD+TDDDD

pondDDDDDDDDDD

pondDDDDDDDDDD

pondDDDDDDDDD

pondDDD

pondDD

D+TDTT

D+TD

D+TD+TD

D+TD

pondDDTTTTTDTTTTDDTTTTTDDDDDDDDDDDDDDTTTT

pondTTTTTTTDDDTDTTDDDTTTTTTTTTTTTTTTTTTTTTTTTTDTTTTTTDDTDDDDDTDTDDDDDDD

DDDDDDDTTTTTT

kk_2011-

Yield [kg]

0

0 - 200

200 - 400

400 - 600

600 - more

Acreage & Production

21


Zoom

Minimum image during flooding / planting stage Maximum image during vegetation stage Detected paddy area

22


TD

pondpond

TTTTTTTTTTTTDTD

D+TDTT

D+TD+TDDDDDDTTTDDDD

D+TDDDDTDD

D+TD+TD+TD+TDDDD

pondDDDDDDDDDD

pondDDDDDDDDDD

pondDDDDDDDDD

pondDDD

pondDD

D+TDTT

D+TD

D+TD+TD

D+TD

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pondTTTTTTTDDDTDTTDDDTTTTTTTTTTTTTTTTTTTTTTTTTDTTTTTTDDTDDDDDTDTDDDDDDD

DDDDDDDTTTTTT

Acreage [m2] Yield [g/m2] Production [ton]

Estimation value 164,405.99 (203.96)*1 33.53

Validation value 166,766.39 2.47 – 750.08 40.96

Accuracy of

estimation 98.58% - 81.87%

*1: The statistic information was used to estimate the production.

Estimated result Validation data from field survey

23


• The accuracy of acreage estimation was about 98%, because the

flooding situations of paddy fields were gotten well from SAR image

data during the planting stage.

• The accuracy of production estimation was about 82%, because the

variation in the yield of direct seeding fields was large at the verification

site, and varied substantially from the statistical information which was

used to estimate the production.

• To estimate rice crop production with high accuracy, yield for each field

type is require. Studies are underway to get yield from SAR data by

using a correlation between biomass and the SAR backscatter.

• Suphan buri is located in the central part of Thailand.

• The feature is that most of the paddy fields are

irrigated.

• An accuracy of acreage estimation was about 76%.

• Some sugarcane fields were misjudged as rice.

• To improve accuracy of estimation, studies are being

undertaken to better distinguish rice from sugarcane.

24

Results - In Suphan buri -

Minimum image

during planting

stage

Deference image

between planting

and vegetation

stage

Estimated area of

rice crop

Maximum image

during vegetation

stage

Suphan buri

• We estimated wall-to-wall of the acreage

and production of Thailand’s rainy

season rice crops.

• The satellite data used was ScanSAR

from ALOS PALSAR provided by JAXA,

which provides a 100m spatial resolution,

and a 350km observation swath width.

• Although ScanSAR data is coarser,

monthly updates for the entirety of

Thailand can be achieved.

• And, it is useful to estimate rice crop

area in provincial level.

25

Results - Wall-to-wall estimation of Thailand -

300km

Imagery collected in 2009 was used, with the planting stage

defined as April to August, and the vegetative stage was taken

to be August to November.

26


< RSP Path >

RSP118

RSP121

RSP124

RSP127

RSP130Planting stage Vegetative stage

There were some data gaps.

27


Images during planting stage Images during vegetation stage

Minimum pixel values

image

during planting

stage

Deference image

between planting

and vegetation

stage

Estimated area of

rice crop

Maximum pixel values

image

during vegetation

stage

Considerations

• By comparing with statistical information provided by

Thailand’s Office of Agricultural Economics (OAE), an

accuracy of more than 70% was confirmed in 37 provinces

in 76 provinces.

• Error factors estimation are as follows;

‒ The some data gaps might be origin.

‒ The provinces which main crop was rice were good.

‒ The other crops might be misjudged as rice.

‒ The crop calendar might be different with our assumed.

• We need rural information such as crop calendar and so on.

• In the future, it is suggested that the methodology be

improved by conducting field surveys in areas that the

accuracy of less than 70% was confirmed.

28


• To improve accuracy of estimation, studies are being

undertaken to better distinguish rice from other crops by

‒ analysing biomass in the well-grown stage,

‒ analysing the period from planting to harvest,

‒ paying attention to the characteristics of cross

polarization,

‒ and analysing complex images with full polarizations.

29


Rice crop outlooks

30

Rice crop outlooks for GEO GLAM & AMIS

• Asia-RiCE, led by JAXA, has also started to provide rice crop outlooks

in Thailand, Vietnam and Indonesia for FAO AMIS by using JASMIN.

• RESTEC developed JASMIN to provide satellite weather information to

statistical experts.

• JASMIN displays information in maps and graphs, and the information

includes information on current conditions and anomalies (deviations

from past normal years). Data is updated twice a month.

31

Rice crop outlooks - JASMIN -

32

Parameters Interval Spatial Resolution

Data Period (anomaly calc.)

Satellite Data Source

Precipitation

Cumulative (15-day)

10 km 2002-

(2002-2012)

GSMaP (GCOM-W1, TRMM, MTSAT etc.)

Solar Radiation

15-day Average 5 km 2007-

(2007-2012) MODIS

Land Surface Temperature


(2002-2012) MODIS

Soil Moisture


(2002-2012) AMSR-E, WindSat

Drought Index

15th /31[30]th day of month

10 km 2003-

(2003-2012) GSMaP, MTSAT

Vegetation Index

15th /31[30]th day of month

5 km 2009-

(2009-2012) MODIS

JASMIN provides 6 parameters.

Rice crop outlooks - JASMIN -

33

Provide satellite derived information on the

WWW

Develop Rice Outlook by AFSIS and

Agricultural Statistician in each country

Review and Post “Asia-RiCE Outlook”

Develop “Crop Monitor” report for AMIS

“Market Monitor”

Publish “Market Monitor”

(Monthly)

Rice crop outlooks - Work-flow -

• Rice crop outlooks using the satellite weather information system

JASMIN were started from last October in three countries, Indonesia,

Vietnam and Thailand.

• It is expected that more knowledge will be accumulated. In the future,

JASMIN will be expanded to support FAO AMIS outlook reporting for

other ASEAN countries in cooperation with AFSIS.

34

Rice crop outlooks - Current status -

Conclusions

35

• RESTEC developed INAHOR and JASMIN under contract

to JAXA.

• INAHOR is software that is used to estimate rice crop

acreage and production using SAR data.

• JASMIN is a web-based system that provides satellite

agricultural weather information to statistical experts for the

purpose of making crop outlooks.

• JAXA and RESTEC are working to verify their methods of

rice crop monitoring using EO satellite data for GEO

GLAM/Asia-RiCE.

• In addition, we also preparing to apply new SAR sources

such as ALOS-2 and Sentinel-1.

36

Conclusions

Thank you for your kind attention

37

Appendix

38

RESTEC overview

39

Remote Sensing Technology Center of Japan

RESTEC overview - Main business -

40

• Earth Observation : Reception, Processing and Provision of data acquired both by domestic and foreign satellites.

Development and Operations of ground stations.

• Research and Development : Conducting calibration and validation of remote sensing data, development of algorithm

and software in remote sensing. Developing processor and observation platform.

• Capacity Building : Providing both domestic and international personnel with remote sensing training. Capacity building

for developing countries including technology transfer.

• Think tank/Consulting : Conducting consulting and research works related to the earth observation and remote sensing.

Assessment and analysis of remote sensing needs in emerging and developing countries.

• Solution Businesses : Offering value added services including consultation in remote sensing technologies. Monitoring

agricultural crop production related to food security. Monitoring natural disasters.

More Details : http://www.restec.or.jp

41

http://www.jaxa.jp

Planetary exploration

Space Transportation Systems

Aerospace exploration

Aeronautical Technology

Research

Human Space

Activities

Satellites & Spacecraft

RESTEC overview - Main customer -

• Main customer is JAXA (Japan Aerospace eXploration Agency)

• JAXA develops and researches the space technologies and its utilization.

• More than 70% of our business is from JAXA.

Methodology

42

-

43

Framework for crop yield estimation

44

Flow chart of INAHOR software

Noise reduction

Select images for planting stage and well-growing stage

Open satellite image data

Detect flooding area

Detect well-growing area

Mapping rice crop area

Check the result of rice crop area mapping

Manual Automatic

Set two threshold parameters to detect flooding and well-growing area

Rice crop area

OK

NG

Finally, the rice crop production is calculated by result of estimated rice crop area and yield

per unit from statistic information or field survey.

Mask the other than farmland (if LULC from optical sensor data is available)

45

Operation of INAHOR • INAHOR runs on Linux OS Ubuntu.

• INAHOR is developed by open sources. (Qt, PostgreSQL, PostGIS, Shapefile C Library, LibGeoTIFF, PROJ.4, GEOS,GDAL／OGR, netCDF,

HDF, etc. Ubuntu is also open source.)

Current Activities

46

Monitoring rice crop situation and Outlook of rice crop in Asia

for GEO GLAM / Asia-RiCE

47

Current Activities – Activity for ALOS-2 PALSAR-2 -

• For ALOS-2 PALSAR-2, a

study to estimate biomass

using SAR data is going in

Indonesia.

• The classification to four

stages of paddy field has

done .

• The verify of result is going.

Planting

Harvesting

Vegetated 2

Vegetated 1

• Asia-RiCE was launched by JAXA after the agricultural ministers’ 2011

G20 meeting which it was decided to launch AMIS and GEOGLAM.

• Technical demonstration sites (TDS) were selected in each country.

• These sites are being used to verify the rice crop monitoring

methodology using statistical information, ground (in-situ) observations,

computer modelling, SAR, and other space-based observation data

48

Current Activities - GEO GLAM/Asia-RiCE activity-

India

Thailand

Indonesia

Philippines

China

Japan

Laos

Vietnam

Malaysia

Phase 1B: Apr 2014 - Mar 2015Phase 1A: Jun 2013 - Nov 2014

Taiwan

Technical Demonstration Sites for Asia-RiCE

• RESTEC supports that JAXA is developing ground (in-situ) observation

devise and analyzing method to estimate biomass, crop calendar and so

on, at Japanese Technical demonstration site where is Tsuruoka,

Yamagata Prefecture, with Professor from Tsuruoka college of

technology and Aizu university.

• The studies are going to start April, next crop season, by combining the

ground observation data and satellite data.

49

Current Activities - Asia-RiCE activity in Japan-

50

Much

Less

Much

Less

Current Condition Anomaly

• Precipitation :

Few precipitation can causes drought and

too much precipitation can causes flooding.

Clear

Cloudy

Clear

Cloudy


• Solar radiation :

Solar radiation is one of the key factors for

rice growth.

High solar radiation means there is few

cloud and a lot of solar radiation comes to

land surface.

Wet

Dry

Wet

Dry


• Soil moisture :

Available water in the soil is a significant

factor for rice growth.

High soil moisture means available water in

the soil is enough.

Low soil moisture means at the risk of

drought.

Current Activities - JASMIN -

51

• Drought index :

Drought index shows the degree of drought.

High index means that there are few

available water (drought).

• Vegetation index:

NDVI is not agro-meteorological parameter,

but the index to indicate the amount of

leaves.

High NDVI means much vegetative and less

NDVI means less vegetative.

Wet

Dry


Wet

Dry

Much

Less

Much

Less


Current Activities - JASMIN -

RESTEC is determined to

• contribute to capacity building for remote sensing utilization

in the world.

• work with agencies and institutions for better utilization of

remote sensing technologies.

• provide technical services of remote sensing to

organizations engaged in resource management, disaster

control and others.

52

Conclusions

background - food and agriculture organization...- advantages of earth observation by satellite -...

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