AFSIS crop productivity prediction AFSIS crop productivity prediction - - PowerPoint PPT Presentation

AFSIS crop productivity prediction based on satellite observation AFSIS crop productivity prediction based on satellite observation

Shinich Sobue1,2, Kei Ohyoshi1 Toshio Okumura2, Kobayashi Kazufumi2 On behalf of the Asia‐RiCE Team

1Japan Aerospace Exploration Agency 2Remote Sensing Technology Center of Japan

Overview Overview

 Asia‐Rice led by JAXA, and AFSIS have started to provide rice growing outlooks in Thailand, Vietnam and Indonesia for FAO AMIS through GEOGLAM, using agricultural meteorological information based on satellite observations and local information based on ground observations by statistics

• rganization.

 AFSIS is tackling to make rice growing outlooks using satellite agro‐met information, in addition to local information which they have used until now.  To provide satellite agro‐met information to AFSIS statistical experts, “JASMIN” was developed by RESTEC under the contract

• f JAXA.

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Advantages of Earth Observation by Satellite Advantages of Earth Observation by Satellite

 Earth Observation satellites can collect the information:  1) Over a broad area, even if the area is difficult to access, 2) Periodically, 3) With high consistency, 4) In near real‐time, 5) Cost‐effectively.

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• Approx. 600‐800 km height

from the Earth’s surface

more efficiently and more exactly

• 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

• f modern tools was promoted, in particular

remote sensing.

FAO AMIS & GEO GLAM FAO AMIS & GEO GLAM

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• 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:

• The GEO GLAM 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, Japan

Aerospace Exploration Agency (JAXA) proposes and leads the Asian Rice Crop Estimation & Monitoring project (Asia- RiCE) for GEO GLAM.

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

Asian organizations.

Asia‐RiCE Asia‐RiCE

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AFSIS AFSIS

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http://www.afsisnc.org/

 ASEAN Food Security Information System  The Project is implemented under ASEAN + 3 Cooperation and led by Thailand. Japan is the donor through ASEAN Trust Funds.  AFSIS collect the agricultural‐statistics information of each country and aim at constructing the structure which grasps and monitors the situation of the food security of an overall community.  AFSIS publishes Early Warning Information (EWI) Report and Agricultural Commodity Outlook (ACO) Report, twice a year.

⇒ AFSIS is good organization

to make a rice growing outlook around Asia with Asia‐Rice.

Work flow Work flow

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Provide satellite agro-met information on the WWW by “JASMIN” Make rice growing outlooks with agricultural statisticians in each country Review and submit the outlooks to GEOGLAM Develop “Crop Monitor” report for AMIS “Market Monitor” Publish “Market Monitor” Those works are conducted every month.

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JASMIN JASMIN

 JAxa's Satellite based MonItoring Network system for FAO AMIS

• utlook

 JASMIN displays information in images and time series graphs on the web browser.

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JASMIN’s parameter JASMIN’s parameter

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 2009‐ (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 2002‐ (2009‐2012) MODIS

 JASMIN provides 6 parameters, and they are updated twice a month. The interval is 1st to 15th and 16th to end of month.

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Image information Image information

 JASMIN provides 2 images for each parameter, which are current condition and anomaly (the anomaly is deviation from past normal years).

Much Less Much Less

Current Condition Anomaly

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Image information Image information

• Precipitation :

Few precipitation can causes drought and too much precipitation can causes flooding.

• 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.

• 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.

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Image information Image information

• 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.

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Time series graph information Time series graph information

 The time series graph shows 3 lines.

1) Red line is time series of selected year 2) Blue line is time series of previous year 3) Grey line is time series of climate value (average of the past data)

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Time series graph information Time series graph information

 Time series graph in only Asia‐RiCE TDS are provided.

• Subang in Indonesia
• Suphan buri in Thailand
• An Giang and Thai Binh in Vietnam

 JASMIN will be improved to add the graph in principal production areas in each country.

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Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

 AFSIS and JAXA had a GEO GLAM consultation meeting for making rice growing outlook,

• n 18th Oct. 2013 in Thailand.

 Summary :

• Target country : GEO GLAM/Asia‐Rice phase 1A countries,

Indonesia, Thailand, Vietnam

• Work assignment :
• Satellite agro‐met information is provided by JAXA twice a month, to use as

evidence and data for outlook.

• Monthly rice growing outlook is reported by target countries statistic
• rganizations, and those reports are adjusted by AFSIS and sent to Asia‐RiCE

until 20th every month.

Notice : Outlook is not directly connected with official statistic information from statistic organizations because of temporal requirement and administrative issue.

• The rice growing outlooks are reviewed by Asia‐Rice and submit to GEO GLAM

until 23rd every month.

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 The example of rice growing

• utlook

Reference : AFSIS releases monthly rice growing

• utlook on their web log too.

http://www.afsisnc.org/blog

Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

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Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

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Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

 Asia‐RiCE team check the relationship between the outlooks and satellite agro‐met information, and submit to GEO GLAM.

Indonesia : The rainfall in April 2014 is mostly moderate in most regions of Indonesia. While the rainfall is expected to be very ideal and sufficient to support the growth of rice plants, particularly to be

• ptimum on reproductive phase.

Precipitation anomaly in the second half of March, and the first half of April

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Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

Thailand : In this month, Thailand is expected to harvest the second rice to be about 47 percent of the country production. Yield is not so good due to the cold weather in the beginning of the year which is impact on the rice condition. To this condition, there are some areas which harvested later than normal. Surface Temperature anomaly in the second half of Dec., and the second half of Jan.

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Making Outlook ‐ collaboration with AFSIS ‐ Making Outlook ‐ collaboration with AFSIS ‐

Vietnam : The Mekong River Delta region has harvested nearly 800 thousand hectares, however only accounting for approximately 60% of the last-year area. The main reasons of the lower sowed area and slower speed of the harvest are the flood in January 2014, and the cold weather.

Precipitation anomaly from Nov. to Jan. Surface Temperature anomaly from Dec. to Mar.. Dec. Dec. Jan. Jan. Feb. Feb. Mar. Mar.

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“Market Monitor” is Published by FAO AMIS “Market Monitor” is Published by FAO AMIS

 The example of AMIS Market Monitor No18 – May 2014

*Crop Monitor is developed for AMIS by GEOGLAM. It summarizes latest conditions for AMIS crops based on regional expertise and analysis of satellite data, ground observations, and meteorological data.

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“Market Monitor” is Published by FAO AMIS “Market Monitor” is Published by FAO AMIS

 Summary of rice growing outlook is written as Highlights

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“Market Monitor” is Published by FAO AMIS “Market Monitor” is Published by FAO AMIS

 Three countries outlooks are inserted at crop monitor page

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Summary Summary

 Asia‐Rice team and AFSIS have started to provide rice growing

• utlooks in Thailand, Vietnam and Indonesia for FAO AMIS through

GEOGLAM, using agricultural metrological information based on satellite observation and local information based on ground

• bservations by statistics organization.

 Now we are accumulating knowledge which is relationship between satellite agro‐met information and rice growing outlook, making outlook from local information and satellite agro‐met information.  Now JAXA and RESTEC are developing flood parameter such as a MODIS NDWI, because the flood influence is frequently indicated in the outlook.  We are also considering improvement of JASMIN, because the addition of the graph information in principal production areas is demanded by AFSIS strongly.

Thank You for Your Attention

@Tsukuba, Japan