Japan Aerospace Exploration Agency (JAXA)
Published March 2017

JAXA’sUseful Earth Observation
Technologies
Research and Development Results
on Satellite Data Utilization

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Use and Application of EORC’s Research and Development Results
by Government Ministries and Agencies and the Industrial WorldThis pamphlet summarizes the results of research and development on the usage of satellite
data provided by the Earth Observation Research Center (EORC). The aim is to utilize JAXA’searth observation technologies to resolve issues experienced by government ministries andagencies and the industrial world, and also to improve business value.

This pamphlet includes overviews of products and datasets analyzed and studied with various
types of satellite observation data, algorithms for producing these, and more.

We hope this pamphlet will inspire you to use JAXA’s earth observation technologies in yourbusiness, and also provide hints for new business creation and problem resolution.

EORC Technology Usage Policies
1. Datasets and products(1) For medium- or low-resolution satellite data (data with a resolution of more than 15m), data designated by
JAXA as “JAXA Standard Products” can be freely re-distributed or used at no cost even for commercial
purposes. However, when using the released data as-is, the user must add a credit stating, “© JAXA.” If theuser re-processes the released data before usage, a credit stating, “Original data provided by JAXA” is required.
(2) For data other than JAXA Standard Products, the handling differs by data type including the usage ofintellectual property (fees apply), usage for research or educational purposes and individual usage (free ofcharge), usage through joint research, etc. Refer to each page for individual usage policies.

2. Programs, algorithms, and other analysis technologies(1) Technologies are released at no cost for research and educational purposes or individual usage. For commercial purposes, etc., technologies can be used by paying fees based on JAXA’s Intellectual Property
Utilization License Agreement.
(2) After screening, other possible usages include contracted research by, advice from, and customization by
JAXA; joint research with JAXA; etc.

Application inbusiness

EORC

Industrial world

Sensortechnologies

ModelsObservationdata

1

Disaster prevention, weather,environment, ocean,
agriculture, forestry, fisheries
industry, construction, civilengineering, security,
insurance, logistics, etc.

Products

Composition/
assimilation
Algorithms

Government ministries
and agencies

Datasets

Discoveringneeds

The basic usage policies for each technology are listed on each page.
For details about the usage policies or inquiries about usage, please contact JAXA directly by e-mail.
E-mail: sapc-info@jaxa.jp

Promotion ofearth sciences

2

Dataset

Global PALSAR-2/PALSAR/JERS-1 Mosaic
and Forest/Non-forest Map

Information

JICA-JAXA Forest Early Warning Systemin the Tropics (JJ-FAST )Product

Information
Global mosaic SAR image data by PALSAR-2/PALSAR/JERS-1 (resolution 10m: 2007, 2009;
25m: 1996, 2007, 2008, 2009, 2010, 2014, 2015)
Global forest/non-forest mosaic data by PALSAR-2/PALSAR (resolution 10m: 2007, 2009; 25m:
2007, 2008, 2009, 2010, 2014, 2015)
High-accuracy SAR processing technologies
Forest/non-forest analysis and processing technologies
URL http://www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm

Deliverables

Quality (accuracy andother information)

Utilizations

Spatial resolution: 10m, 25m
Radiometric accuracy: 1dB or less
Geometric accuracy: 10m or less
Forest/non-forest classification accuracy: Approximately 90% (forest classification standard:
natural forest with an area larger than 0.5ha and forest cover over 10%)
Identifying areas of forest decrease or increase, reference for estimating forest biomass
quantities, educational materials

Past usage in businessand other fields

Applications to measurement, reporting, and verification (MRV) systems for REDD+ (scheduled)
Applications to global maps by the International Steering Committee for Global Mapping(ISCGM) (scheduled)

Usage policy

Dataset with 10m spatial resolution is provided according to a license agreement for usage(fees apply)
Dataset with 25m spatial resolution conforms to JAXA Standard Products (no cost)

Availability of customization

Display ranges can be customized (contact JAXA regarding forest classification analysis andprocessing customization)

Joint research institution(s)—Deliverables

Near-real-time monitoring system for illegal logging using observation data from ALOS and ALOS-2
The user can download tile image data for forest change regions and KML and shapefile datafor change regions
URL http://www.eorc.jaxa.jp/jjfast/

Quality (accuracy andother information)

Minimum area of detection: 5ha* Will support areas smaller than 5ha in the future
Detection accuracy: Currently being evaluatedUpdate frequency: Once every 1.5 months
Supported regions：South America, Africa* Supported areas will be gradually expanded to Central America and Asia

Utilizations

Early-stage detection of illegal forest logging
Understanding of changes in forest resources

Past usage in businessand other fields

This system is operated as one facet of the JICA-JAXA Initiative for Improvement of Forest
Governance

Usage policy

Free for research and educational purposes only
A credit stating “©JICA/©JAXA” is required when using the data as-is or after re-processing

Availability of customization—

Joint research institution(s)JICA (research contracted by JICA to JAXA)Specific examples

Specific examples

JJ-FAST top page
Global forest/non-forest map (2009)

ForestNon-forest* User registration/download
http://www.eorc.jaxa.jp/ALOS/
en/palsar_fnf/fnf_index.htm* JAXA Let’s SAR (free PALSAR
analysis software)
http://www.eorc.jaxa.jp/
ALOS-2/en/doc/pal2_tool.htm

Near Japan (forest/non-forest)

3

Near Tokyo (forest/non-forest)

Near Tokyo(SAR mosaic/HH-polarization)

Example change map

4

Dataset

ALOS World 3D (AW3D)

DatasetHigh Resolution Land-Use and Land-Cover Map Products

Information

Information
Deliverables

Digital surface model for the global land surface with optional ortho-rectified images utilizing
approximately three million data images observed by PRISM onboard ALOS
* Using PRISM archive data
Free version
URL http://www.eorc.jaxa.jp/ALOS/en/aw3d30/index.htm

Quality (accuracy andother information)[Paid version]
Spatial resolution: DSM equivalent of 5m,
ortho image equivalent of 2.5m
Target accuracy: Horizontal 5m,
vertical 3.5m (RMSE)
Coverage: Nearly all land areas across the globe

Utilizations

Map production and updating (up to scale of roughly 1:12,500), infrastructure improvement,
disaster prevention, resource surveying, transportation field, communications field, etc.

[Free version DSM]
Spatial resolution: DSM equivalent of 30m
Target accuracy: Horizontal 5m,
vertical 4.4m (RMSE)
Coverage: Nearly all land areas across the
globe

Past usage in businessand other fields

National Spatial Data Infrastructure (NSDI) information in various countries, overseas expansion
and overseas aid projects by domestic private enterprises

Usage policy

The paid version is sold for commercial usage by business operators(e-mail: aw3d@kits.nttdata.co.jp or data@restec.or.jp)
The free version conforms to JAXA Standard Products (e-mail: aproject@jaxa.jp)

Availability of customization

Not available (can be customized by the user)

Joint research institution(s)NTT DATA Corporation, Remote Sensing Technology Center of Japan

Deliverables

Land-use and land-cover maps of all areas in Japan produced using observation data mainly
from AVNIR-2 (onboard ALOS) from 2006 to 2011Land-use and land-cover maps of northern Vietnam produced using data from multiple satellites
in 2007 and 2015 (Landsat, ASTER, PALSAR, and PALSAR-2 mosaics, etc.)
URL http://www.eorc.jaxa.jp/ALOS/en/lulc/lulc_index.htm

Quality (accuracy andother information)[All areas in Japan: Version 16.09]
Spatial resolution: 10m
Classification categories: 1) water, 2) urban,
3) paddy, 4) crop field, 5) grass land,6) deciduous broad-leaved tree, 7) deciduous
coniferous tree, 8) evergreen broad-leaved
tree, 9) evergreen coniferous tree, 10) bareland, 11) snow and ice
Classification accuracy: 78.0%

Utilizations

Research and practical utilization for land management, vegetation surveys, forest management, landslide disaster surveys, food security, ecosystems, and other fields; education

Past usage in businessand other fields

Disaster simulations by private enterprises, textbooks, publications, etc.

Usage policy

JAXA products: Free for research and educational purposes and individual usage

Availability of customization

Not available (can be customized by the user)

Joint research institution(s)Tsukuba University[Northern Vietnam: Version 16.09]
Spatial resolution: 10m
Classification categories: 1) water, 2) urban,
3) paddy, 4) crop field, 5) grass land,6) orchard, 7) bare land, 8) forest,9) mangrove
Classification accuracy: 81.3% (2007),
89.1% (2015)

Specific examples
Specific examples

Released dataset thumbnails(resolution equivalent of 180m, the blackcolor is a cloud mask)
http://www.eorc.jaxa.jp/ALOS/
en/aw3d30/index.htm
Digital 3D map image example: Mt. Fuji
Example of ALOS World 3D (AW3D)(Left: 5m resolution, center and right: simulated DSMs with
30m and 90m resolutions for reference)

Category
1 Water
2 Urban
3 Paddy
4 Crop field
5 Grass land
6 Deciduous
broad-leaved tree

7 Deciduous coniferous tree
8 Evergreen broad-leaved tree
9 Evergreen coniferous tree
10 Bare land
11 Snow and ice

Land cover near Tsukuba and Tsuchiura Cities

High Resolution Land-Use and Land-Cover Map Productsof all areas in Japan : Version 16.09
http://www.eorc.jaxa.jp/ALOS/en/lulc/lulc_index.htm (Japanese only)

http://www.eorc.jaxa.jp/ALOS/en/aw3d/index_e.htm
Category

1 Water
2 Urban
3 Paddy

4 Crop field
5 Grass land
6 Orchard

7 Bare land
8 Forest
9 MangroveHigh Resolution Land-Use and Land-Cover Map Products of Northern Vietnam: Version 16.09

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Product

Reference Ground Dataset for Land-Cover Maps
(SACLAJ Web)

Algorithm

L-band SAR Interferometry

Information

Information

Deliverables

SAR interferometry images and SAR interferometry algorithms for L-band Synthetic Aperture Radar

Quality (accuracy andother information)

Accuracy: The target is around 3cm for crustal displacement amount,
excluding atmospheric and ionospheric conditions
8m height accuracy (DEM generation) of ALOS/PALSAR data has been verified
Spatial resolution: Depends on the average sample number, but generally 30m

Utilizations

Infrastructure management, disaster prevention, investigating crustal deformation and land
subsidence (including time series analysis), DEM generation, deforestation research

Past usage in businessand other fields

Must participate in the JAXA Land-Cover Community or complete user registration (fornon-commercial purposes)
E-mail: aproject@jaxa.jp

Provision of analysis software for research purposes (Meteorological Research Institute/Japan
Meteorological Agency, National Institute for Land and Infrastructure Management [Japan],
PIXEL researchers)

Usage policy

Can be used through contracted or joint research

Availability of customization—

Availability of customization

Contact JAXA

Joint research institution(s)University of Tsukuba

Joint research institution(s)—Deliverables

4D reference data (training data/verified information) dataset for land-cover maps
Reference data posting and sharing system

Quality (accuracy andother information)

SACLAJ̲Gref (Ground photos): On-site photograph-based reference data with clear shooting
dates for each image
SACLAJ̲Rref (Remote sensing data): Satellite image interpretation-based reference data thatenables wide-area coverage including hard-to-access areas, overseas locations, etc.

Utilizations

Sharing of reference data for producing land-cover maps, satellite product verification

Past usage in businessand other fields—

Usage policy

Specific examples

Specific examples
PALSAR differential interferogram(crustal deformation)
http://www.eorc.jaxa.jp/ALOS/en/img_up/
dis_pal_tohokueq_110401-06.htm* The color changes from blue to red, yellow, green, and back to blue
indicate an extension (and an opposite pattern indicates a shortening)of the satellite-ground distance. One color cycle is equal to 11.8cm.

SACLAJ Web data search screen (list display)

Interferogram obtained by the analysis of the ALOS-2
PALSAR-2 data acquired before (Feb. 21) and after(May 2) the earthquake in Nepal

SACLAJ Web on-site data registration screen example

7

http://www.eorc.jaxa.jp/ALOS-2/en/img_up/
dis_pal2_npl-eq_20150502.htm

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Algorithm

Time-series Interferometric SAR

Product

Information

Time-series Interferometric SAR Automatic Analysis Tool

Information

Deliverables

This algorithm utilizes SAR interferometry time-series analysis to measure small-scale displacement of land surface items and infrastructure over a wide area

Deliverables

This product performs automatic SAR interferometry time-series analysis to output small-scale
displacement of land surface items and infrastructure

Quality (accuracy andother information)

Deformation speed accuracy: Around 10 – 100mm/year *1
Spatial resolution: Around 3m – 50m *1
Measurement targets: Small changes to the ground surface during the observation period *2

Quality (accuracy andother information)

The output result accuracy depends on the the SAR data and analysis subject used.
Operating environment: Commercially manufactured PC (Windows, 8GB or greater memory)
Differences compared to existing tools:
Full automation of the following settings required for processing settings*1 Depends on SAR data and measurement target used.
The accuracy error for the runway surface example below is 17mm/year.
*2 Measurement is suspended due to ground level raising, ground surface improvement work,and new building construction during the observation period.

Utilizations

Interference pair (satellite, sensor, mode) selection, data selection/adjustment for target analysis scope,
downloading of required external data, various parameter settings, selection/conversion of target area
scope in images

Detecting small-scale displacement of infrastructure(infrastructure deterioration changes, building subsidence, etc.)
Understanding ground deformation(oil fields, landslide disasters, urban development, crustal deformation, etc.)

Easier operation of necessary settings
Analysis target selection (latitude and longitude) by searching in natural language
Intuitive operation with KML data for making minute adjustments to the target range

Past usage in businessand other fields

Empirical research with private enterprises

Usage policy

Can be used with the automatic analysis tool through contracted or joint research

Availability of customization

To be determined (empirical research is underway)

Joint research institution(s)JAXA researched and developed the algorithm.
Usage research is conducted with the Infrastructure Development Institute-Japan;
Penta-Ocean Construction Co. Ltd.; and Nippon Koei Co., Ltd.

+100

Empirical research is underway with private enterprises

Usage policy

Contact JAXA

Availability of customization

To be determined (empirical research is underway)

Joint research institution(s)Developed by: JAXA / Verified by: JAXA, Kyoto University, private enterprises

Three steps to output

Automatic analysis tool
2
3

Satellite measurement
value (mm/year)

Put purchased SAR data in the folder
Language search for the target
Click “Run”

-100

Small
Large
subsidence ⇔ subsidence

Ground measurement
value (mm/year)

-100

+100

Deformation amount image

Purchased data
-0.03
-0.03

-0.02

-0.02

-0.01

-0.01

0

0

0.01

0.01

0.02

0.02

0.03

0.03

0.04
600

0.04
600

500
400

500

300

400

200

300

3D visualization of
secular change

200
100
0

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Past usage in businessand other fields

1Comparison of ground measurements and satellite analysis (thered lines are true values)

Ground surface and infrastructure displacement canbe spatially visualized from satellite observation data.
(Background image is optical data © Google)

Support for displacement amount analysis by non-specialist users

Specific examples

Specific examples

Secular change of airport ground surface height

Utilizations

700

600

500

400

300

200

100

0

100
0
700

600

500

400

300

200

100

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INAHOR Software for Estimating Rice Planted Areas

ProgramInformation

Agrometeorological Monitoring System

DatasetInformation

Deliverables

Synthetic Aperture Radar (ALOS PALSAR, ALOS-2 PALSAR-2) data from multiple time periods
is used for easy operations to identify rice planting locations and estimate areas

Deliverables

Quality (accuracy andother information)

Detection accuracy of 80 – 90% at verification sites in Southeast Asia and Japan(Verification in Southeast Asia has been completed in Indonesia, Vietnam, Laos, the Philippines,
and Thailand)

Quality (accuracy andother information)

Conforms to GSMaP and JASMES

Utilizations

Supplementary information for judging principal grain crop situations

Utilizations

Producing agricultural statistics (rice crop planted area, production)* Input of yield data (harvest amount per area unit) is separately required for the estimation of production

Past usage in businessand other fields

Overseas Food Supply and Demand Reports by the Ministry of Agriculture, Forestry and Fisheries
Agriculture Market Information System (AMIS) Market Monitor(Food and Agriculture Organization [FAO])

Past usage in businessand other fields

Used in Innovative Data Collection Methods for Agricultural and Rural Statistics, a technicalcooperation project by the Asian Development Bank

Usage policy

Conforms to JAXA Standard Products

Usage policy

Can be used through contracted or joint research. The Asian Development Bank will release
online training materials including the results of the aforementioned project.

Availability of customization

Available (such as local customization) within joint research frameworks

Joint research institution(s)Drought indexes are joint research results with the University of Tokyo

Availability of customization

Available (such as local customization) within joint research frameworks

Joint research institution(s)Joint verification work is underway with government agencies in Indonesia, Thailand, Vietnam,
Cambodia, Myanmar, etc.

Agricultural weather data (soil moisture, solar radiation, precipitation, etc.) and crop growth
status can be viewed online in near real time (updated every 15 days)

Specific examples

Specific examples

Choose the ALOS-2 images to use

Estimated rice-paddy planting locations (blue)
Example: Agrometeorological information display

Drought index deviation (Kingdom of Thailand)(Kingdom of Thailand)

Output is available in a format (KMZ) that can be displayedin Google Earth (red is rice-paddy planting locations)Temporal change in soil moisture in Suphan Buri Province (Kingdom of Thailand)

System customized for producing rice crop growing outlooks in Asia
http://suzaku.eorc.jaxa.jp/GCOM_W/JASM/index.html

1112

Dataset

Environmental Information Provision System
For Public Health

Information
Deliverables

Quality (accuracy andother information)

DatasetGlobal Satellite Mapping of Precipitation(GSMaP) (JAXA Global Rainfall Watch)

Information
This website provides environmental information (rain, solar radiation, land surface temperature,etc.) for locations (latitude and longitude or country/administrative district such as municipality)
and times specified by the user

Deliverables

Global rainfall dataset using multiple satellites

Quality (accuracy andother information)

Variable: Rainfall rate (mm/hr)
Grid resolution: 0.1 degree lat/lon (approximately 11km mesh near the equator)

Conforms to GSMaP and JASMES

Utilizations

Environmental epidemiology, including the analysis of correlations between environmental
conditions and illnesses

Past usage in businessand other fields

Joint research with Japanese universities (including the analysis of relationships between
environmental factors and the outbreak of infectious diseases and other illnesses in regions
such as Southeast Asia and Africa)

Usage policy

Contact JAXA

Availability of customization

Available within joint research frameworks as much as possible technically

Joint research institution(s)ーSpecific examples[Near real-time version]
Domain: Global (60N - 60S)
Temporal resolution:
1 hour (1 hour averaged rainfall)
Update frequency: Every hour (available 4hours after the end of observation; will be
replaced by the standard version later)
RMSE: 0.41mm/hr (average)
URL http://sharaku.eorc.jaxa.jp/GSMaP/
index.htm[Real-time version]
Domain: Himawari (geostationary
meteorological satellite) observation area
Temporal resolution: 1 hour(one hour averaged rainfall)
Update frequency: Every 30 minutes(available on a quasi real-time basis)
RMSE: Same as, or slightly lower
than the NRT version
URL http://sharaku.eorc.jaxa.jp/
GSMaP_NOW/index.htm

Utilizations

Weather, disasters, education, climate, environment, marine, agriculture(weather information websites, flood prediction and warning, scientific educational materials,
crop production prediction, etc.)

Past usage in businessand other fields

Japan Weather Association
Pakistan project (contracted by UNESCO)
Images are provided as content for Tangible
Earth and World Eye (Gakken)
Japan Water Agency

Usage policy

JAXA Standard Products

Availability of customization

Available (such as local customization) within joint research frameworks

Joint research institution(s)Algorithm improvement through joint research by JAXA, the Japanese Meteorological Agency,
Kyoto University, Osaka University, and the University of Tokyo

Public Works Research Institute
Infrastructure Development Institute-Japan
Many uses in TV broadcasting,including news programs
Posted on the Ogasawara Village website

Specific examples

Graph format(top image: rain, bottom image: land
surface temperature)
Example: Observation of Typhoon
No. 10 in 2015
Top page

Example: Output in Google Earth

Example: Observation of 2011 flooding in Thailand(by the Infrastructure Development Institute)
Accumulated rainfall over Chao Phraya River averaged
from July to September in each year

Text format (CSV format)

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Product

3D Precipitation Data

Product

Information“SHIZUKU” (GCOM-W ) Water Cycle Variables

Information

Deliverables

3D distribution of precipitation by GPM/DPR, radar reflectivity strength, precipitation type, brightband height, precipitation top height, etc.; extracted data from observation of typhoons (including
cyclones and hurricanes); visualization images including 3D precipitation distribution videos* GPM/DPR products became available from September 2, 2014

Deliverables

Various types of geophysical parameters that relate to water cycle variations are calculated fromAMSR2 data (onboard the GCOM-W)
URL [Standard Products] https://gcom-w1.jaxa.jp/[Research Products] http://suzaku.eorc.jaxa.jp/GCOM_W/research/resdist.html* AMSR2 products have been open to the public since January 25, 2013

Quality (accuracy andother information)

Physical quantities: 3D precipitation amount, radar reflectivity intensity, etc.
Spatial resolution: 5.2km (horizontal), 250/500m (vertical)
RMSE: Difference of 20% or less for data release request accuracy (precipitation intensity
difference of around ±50% at the earth’s surface by DPR and TRMM/PR)

Quality (accuracy andother information)

Weather, disasters, education, climate, environment, ocean(weather forecasting, climate model verification, precipitation climatology, typhoon information
monitoring, scientific educational materials, etc.)

See the above URL for details of geophysical parameters, regions, spatial resolution, and accuracy
Observation frequency depends on latitudes. The Equatorial region is mostly covered in two days.
The polar regions can be observed multiple times in one day.
The near real-time product is provided after approximately 150 minutes of observation, and thestandard product after one day.

Utilizations

Utilizations

Past usage in businessand other fields

Currently used at the Japan Meteorological Agency, universities, etc.
JAXA/EORC Real-Time Monitoring for Tropical Cyclones (frequently and popularly accessed)
provides images from July 2014. Images and movies are broadcast on TV, including NHK.

Weather, disasters, education, climate, environment, ocean, agriculture, weather forecast, Arctic
sea ice monitoring for the Northern Shipping Route, ocean monitoring including the Kuroshio
Current, fishing ground information, large-scale crop situations, development of educational materials

Past usage in businessand other fields

Usage policy

JAXA Standard Products

Availability of customization

Available in joint research frameworks, such as data extraction

Joint research institution(s)Sensor development by JAXA and the National Institute of Information and Communications
Technology (NICT)
Onboard satellite (GPM primary satellite) development by JAXA and NASA
Algorithm development by a joint Japan-U.S. team (JAXA, NICT, Nagasaki University, Tokai
University, NASA, Colorado State University, etc.)

Japanese Meteorological Agency(numerical weather forecasts, sea surface
water temperature, typhoons, etc.)
Japan Coast Guard (quick bulletins of sea ice
and ocean conditions, etc.)
Ministry of Agriculture, Forestry and Fisheries(Overseas Food Supply and Demand Reports)

Usage policy

JAXA Standard Products

Availability of customization

Available within joint research frameworks as much as possible technically

Joint research institution(s)Joint development, improvement, and verification of algorithms with universities and otherinstitutions for each productSpecific examples

NOAA, ECMWF (various types of
meteorological services)
WEATHERNEWS INC.
(sea ice prediction business)
Japan Fisheries Information Service Center(fishing ground and ocean condition information)

Specific examples

Integrated water vapor content

Sea surface temperature

Precipitation rate

Integrated cloud water content

Sea surface wind speed

All-weather sea surface wind speed(research product)

Sea ice concentration

Snow depth

Soil moisture content

3D precipitation distribution by DPR (JAXA/NASA)
Difference in observations with two DPR frequencies.
Left: Precipitation intensity distribution at the earth’s
surface by DPR.
Right: Vertical cross section of radar reflectivity intensity
by Ku band (13.6GHz) (top), and vertical cross section ofradar reflectivity strength by Ka band (35.5GHz) (bottom),along the black line (AB) from the left image.
The horizontal axis shows the distance from point A in
the left image, and the left axis shows the altitude (km).
The Ku band is suitable for observing strong rain, while
the Ka band has the characteristic of being able toobserve light rain and snow. By combining observations
from these two bands, it is possible to obtain 3D
distributions from strong rain to snow.

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Dataset

JASMES: Climate and Geophysical Variable Dataset

Product

MODIS Near Real-Time Data

Information

Information
Climate and geophysical variable dataset calculated from MODIS data (onboard Aqua andTerra) (the dataset will be calculated from GCOM-C data in the future)
Available variables: Amount of solar radiation at the earth’s surface (photosynthetically active
radiation [PAR]), cloudiness, snow cover extent, sea ice extent, plant water stress trend, soil
moisture content, forest fires, precipitation/precipitable water amount, ocean and land surface
temperature, land surface temperature, chlorophyll-a* Aqua and Terra were launched in 2002 and 1999, respectively, and are currently being
operated (as of March 2017)
URL http://kuroshio.eorc.jaxa.jp/JASMES/index.html

Deliverables

Quality (accuracy andother information)

Algorithms have been developed and improved by EORC (other than land surface temperature)and by joint research with other institutions (only land surface temperature at present; additions
to be made in the future)
Processing frequency: 1 day, half month, 1 month
Spatial resolution: 5km (global, polar regions), 500m/1km (Japan region)

Quality (accuracy andother information)

Provision of images and data obtained in the region of Japan by EOC and TRIC directly
Observation frequency: Around one to two times per day
Spatial resolution: 1km/500m/250m
Available within three hours after observation

Utilizations
Utilizations

Climate, environment, ocean, weather, education, disasters, agriculture
Examples: Using solar radiation data to evaluate land suitability for agriculture, using snow
cover data to research the impacts of the nuclear power station accident in Fukushima

Marine products, ocean, environment, disasters, education, agriculture
Examples: Verifying fishing condition changes, fishing and ocean condition predictions, etc.

Past usage in businessand other fields

Various types of fisheries research institutes, etc. (fishing and ocean condition information,
red tides, etc.)
Japan Coast Guard (sea ice bulletins, marine bulletins, etc.)
Japan Fisheries Information Service Center (fishing and ocean condition information)

Usage policy

Conforms to JAXA Standard ProductsContact JAXA regarding usage for commercial or business purposes

Conforms to JAXA Standard ProductsContact JAXA regarding usage for commercial or business purposes

Availability of customization

Available within joint research frameworks as much as possible technically

Availability of customization

Available within joint research frameworks as much as possible technically

Joint research institution(s)—Joint research institution(s)Algorithm for land surface temperature has been conducted as “Development of land surface
temperature estimation algorithm for GCOM-C/SGLI” with Nagasaki University, (FY25-27)

Deliverables

Various types of fisheries research institutes (fishing and ocean condition information, red tides, etc.)
Japan Atomic Emergency Agency
Private enterprises: Preliminary surveys on radiation shielding, evaluation of agricultural land
suitability level, etc.

Past usage in businessand other fields

Usage policy

Specific examples

Images and data from MODIS (onboard Aqua and Terra)
Images: Chlorophyll-a concentration, sea surface temperature (SST), normalized waterleaving radiance, Rayleigh corrected reflectance, aerosol optical thickness, geometricallycorrected images
Binary data: Chlorophyll-a concentration and SST
URL http://kuroshio.eorc.jaxa.jp/ADEOS/mod_nrt_new/index.html (Japanese only)* Aqua and Terra were launched in 2002 and 1999, respectively, and are currently being
operated (as of March 2017)

Specific examples
Chlorophyll-aconcentration

Sea surface
temperature

Normalized
water leaving
radiance

Sea ofOkhotsk

Funka Bay
Global PAR

Snow cover

Japan region PAR

Snow cover

Rayleigh
corrected
reflectance

Aerosol optical
thickness
EchigoIzumo

Ocean color-related physical variables in theLand surface temperature

Water stress

Map of global PAR, snow cover, water stress,
and land surface temperature (2014)

Water stress

Chlorophyll-a

Map of PAR near Japan, snow cover,water stress, and chlorophyll-a (2014)

Ocean off
Tohokuocean area near Japan (wide area) (2014)[1km]
Tokyo Bay

Ariake Sea

Ise Bay
Seto Inland Sea

Map of polar region PAR, snow cover,vegetation index, and land/ocean surface
temperature (2014)
Polar region PAR

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Snow cover

Vegetation index

Land/ocean surface
temperature map

Tokara Islands

Images of chlorophyll-a concentrationin the ocean area near Japan (2014)[500m]

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P-Tree System: JAXA Himawari Monitor

Product

Information

Information
Himawari Standard Data (geostationary satellite) provided by the Japanese Meteorological
Agency, and physical quantity data processed from the Standard Data by JAXAURL http://www.eorc.jaxa.jp/ptree/index.html

Deliverables

Quality (accuracy andother information)

Long-term Sea Ice Climatological Dataset

Dataset[Himawari Standard Data]
Observation range: Full disk, Japan area,target area
Temporal resolution: 10 minutes, 2.5
minutes, 2.5 minutes
Spatial resolution: 0.5km (band 3), 1km(band 1, 2, 4), 2km (band 5-16)[Sea surface temperature ( °C)]
Observation range: Full disk
Temporal resolution: 10 minutes (day and night),
one hour (day and night, nighttime only)
Spatial resolution: 2km[Chlorophyll-a concentration (mg/m 3 )]Temporal resolution: 10 minutes (daytime only)
Spatial resolution: 5km[Aerosol property (optical thickness,
angstrom exponent)]
Observation range: Full disk
Temporal resolution: 10 minutes, one hour
(daytime only)
Spatial resolution: 5km[Cloud characteristics]
Observation range: Full disk
Temporal resolution: 10 minutes
Spatial resolution: 5km[Short wave radiation (photosynthetically
active radiation) ( W/m 2 )]Temporal resolution: 10 minutes (daytime only)
Spatial resolution: 5km[Wildfire]
Observation range: Full disk
Temporal resolution: 10 minutes
Spatial resolution: 2km

Utilizations

Marine, environment, disasters, education, marine products (considering fishing conditionchanges and fishing and ocean condition prediction), etc.

Past usage in businessand other fields

Meteorological Research Institute, Japan Agency for Marine-Earth Science and Technology, etc.

Usage policy

Limited to non-profit purposes such as research, education, and R&D

Availability of customization

Customization by JAXA is available within joint research frameworks as much as possible technically

Joint research institution(s)Japanese Meteorological Agency, National Institute for Environmental Studies

Deliverables

Long-term sea ice climatological dataset derived from multiple satellites, and data integration methods

Quality (accuracy andother information)

Joint development, improvement, and verification of algorithms with other institutions

Utilizations

Climate, environment, ocean, weather, education
Example: Research on sea ice and cloud interaction, environmental education lecture materials

Past usage in businessand other fields

Utilized as source data for sea ice monitoring on the joint IARC-JAXA website(closed in February 2015)
Japanese Meteorological Agency (being considered as sea ice climatology)

Usage policy

Conforms to JAXA Standard ProductsContact JAXA regarding usage for commercial or business purposes

Availability of customization

Available within joint research frameworks as much as possible technically

Joint research institution(s)NASA Goddard Space Flight Center

Specific examples

Change tendency for sea ice extent
over 36 years The darker the red color
is, the more the sea ice extent
decreases; the darker the blue color is,
the more the sea ice extent increases

Northern hemisphere: -177km 2/day

Bering Sea: +2.1km 2/day

Specific examples

Long-term trends for sea ice area by sea (1978 – 2014)

Second half of MarchJAXA Himawari Monitor (displaying sea surface temperature)

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Second half of June

Second half of September

Second half of December

Long-term trends for sea ice concentration (at each grid point) (1978 – 2014)20

Algorithm

Joint Simulator for Satellite Sensors

Acronyms

Information

ALOS

Advanced Land Observing Satellite

Simulated observation data of satellite sensors calculated from atmospheric data of numerical
weather models
URL https://sites.google.com/site/jointsimulator/home

AMSR

Advanced Microwave Scanning Radiometer

AMSR-E

Advanced Microwave Scanning Radiometer for EOS

Simulated sensor data from cloud/precipitation radars, lidars, broadband radiometers, visible-infrared imagers, and microwave radiometers (the resolution of output simulated sensor
data depends on the resolution of the entered numerical weather model)

AMSR2

Advanced Microwave Scanning Radiometer 2

Aqua

Earth Observing System PM-1

Utilizations

Verification of numerical weather and climate models using satellite data

AVNIR-2

Advanced Visible and Near-Infrared Radiometer-2

Past usage in businessand other fields

Japanese Meteorological Agency (numerical weather model evaluation)
Japan Agency for Marine-Earth Science and Technology (numerical weather model evaluation)
RIKEN (numerical weather model evaluation)
Universities (numerical weather model evaluation)

DEM

Digital Elevation Model

DPR

Dual-frequency Precipitation Radar

DSM

Digital Surface Model

ECMWF

European Centre for Medium-Range Weather Forecasts

Deliverables

Quality (accuracy andother information)

Usage policy

Conforms to JAXA Standard ProductsContact JAXA regarding usage for commercial or business purposes

Availability of customization

Allowed within joint research frameworks as much as possible technically

GCOM-C

Global Change Observation Mission-Climate

Joint research institution(s)The University of Tokyo

GCOM-W

Global Change Observation Mission-Water

GISTDA

Geo-Informatics and Space Technology Development Agency

GMI

GPM Microwave Imager

GPM

Global Precipitation Measurement

IARC

International Arctic Research Center

IDI

Infrastructure Development Institute-JapanJASMESJAXA Satellite Monitoring for Environmental Studies

JAXA

Japan Aerospace Exploration Agency

KaPR

Ka-band Precipitation Radar

KuPR

Ku-band Precipitation Radar

MODIS

Moderate resolution Imaging Spectroradiometer

NICAM

Nonhydrostatic ICosahedral Atmospheric Model

NICT

National Institute of Information and Communications Technology

NOAA

National Oceanic and Atmospheric Administration

PALSAR

Phased Array type L-band Synthetic Aperture Radar

PALSAR-2

Phased Array type L-band Synthetic Aperture Radar-2

PRISM

Panchromatic Remote-sensing Instrument for Stereo Mapping

REDD+

Reduction of Emission from Deforestation and forest Degradation+

RMS

Root Mean Square

Terra

Earth Observing System AM-1

Specific examples
Examples of Joint-Simulator application to NICAM 3.5km
resolution simulation data (Hashino et al. 2013)

Ka) Geostationary meteorological satellite observation (IR 10.8μm)b) Simulated satellite data applying the Joint-Simulator to a
global cloud-resolving model (NICAM)
Comparing a) and b) shows that the horizontal distribution of clouds
was skillfully reproduced by NICAM.

K

Names and details of sensors that can perform simulations

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Sensor names

Algorithm details

Visible-infrared imager

RSTAR6b (Nakajima & Tanaka 1986, 1988)

Cloud/precipitation radar

EASE (Okamoto et al. 2007, 2008; Nishizawa et al. 2008), Masunaga & Kummerow (2005)

Lidar

EASE, Matsui et al. (2009)

Microwave radiometer

Kummerow (1993)

Broadband radiometer

MSTRN-X (Sekiguchi and Nakajima 2008),
CLIRAD (Chou and Suarez 1994, 1999; Chou et al. 2001)22