background - food and agriculture organization space based microwave radar (sar), which we developed...
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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
Results - In Khon kaen -
Automatic data collection by field router
• meteorological sensor
‒ Air Temperature,
‒ Soil Temperature,
‒ Precipitation (rain gauge),
‒ Radiation,
• Image sensor (CMOS camera)
18
Results - In Khon kaen -
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
Results - In Khon kaen -
We measured crop yield (production per area) by cutting rice plant in
harvest stage.
20
Results - In Khon kaen -
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
Results - In Khon kaen -
Zoom
Minimum image during flooding / planting stage Maximum image during vegetation stage Detected paddy area
22
Results - In Khon kaen -
TD
pondpond
TTTTTTTTTTTTDTD
D+TDTT
D+TD+TDDDDDDTTTDDDD
D+TDDDDTDD
D+TD+TD+TD+TDDDD
pondDDDDDDDDDD
pondDDDDDDDDDD
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pondDDD
pondDD
D+TDTT
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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
Results - In Khon kaen -
• 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
Results - Wall-to-wall estimation of Thailand -
< RSP Path >
RSP118
RSP121
RSP124
RSP127
RSP130Planting stage Vegetative stage
There were some data gaps.
27
Results - Wall-to-wall estimation of Thailand -
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
Results - Wall-to-wall estimation of Thailand -
• 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
Results - Wall-to-wall estimation of Thailand -
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
15-day Average 5 km 2002-
(2002-2012) MODIS
Soil Moisture
15-day Average 50 km 2002-
(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
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
Current Condition Anomaly
• 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
Current Condition Anomaly
• 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
Current Condition Anomaly
Wet
Dry
Much
Less
Much
Less
Current Condition Anomaly
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
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