The Hyperspectral Imager Suite (HISUI) is the Japanese next-generation Earth observation project, which will
be onboard ALOS-3 platform. HISUI sensor will be composed of hyperspectral imager (185 spectral bands in
VNIR-SWIR region with 30 m spatial resolution) and multispectral imager (4 spectral bands in VNIR region with
5 [m] spatial resolution), and is being developed by Japanese Ministry of Economy, Trade, and Industry (METI)
as its third spaceborne optical imager mission after JERS OPS and Terra ASTER. HISUI will provide the earth
observation data for global energy and resource issues as well as for other applications such as environmental
monitoring and forestry. This paper shows the radiometric calibration plan for HISUI long-term observation.
The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and theModerate Resolution
Imaging Spectroradiometer (MODIS) onboard Terra satellite, which have different spatial resolution, can observe
the earth surface simultaneously. Both of these sensors have been operated more than 10 years, and it is very
useful for cross-calibration because of their simultaneous observations mostly without BRDF effect. On the
other hand, the CEOS IVOS group arranges the pseudo-invariant standard test sites for cross-calibration, which
are able to evaluate the long-term stability among multiple sensors. This paper shows the TOA reflectance
comparison between ASTER and MODIS sensor over the CEOS pseudo-invariant standard test sites.
In the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) Project, two kinds of algorithms are
used for cloud assessment in Level-1 processing. The first algorithm based on the LANDSAT-5 TM Automatic Cloud
Cover Assessment (ACCA) algorithm is used for a part of daytime scenes observed with only VNIR bands and all
nighttime scenes, and the second algorithm based on the LANDSAT-7 ETM+ ACCA algorithm is used for most of
daytime scenes observed with all spectral bands. However, the first algorithm does not work well for lack of some
spectral bands sensitive to cloud detection, and the two algorithms have been less accurate over snow/ice covered areas
since April 2008 when the SWIR subsystem developed troubles. In addition, they perform less well for some
combinations of surface type and sun elevation angle. We, therefore, have developed the ASTER cloud coverage
reassessment system using MODIS cloud mask (MOD35) products, and have reassessed cloud coverage for all ASTER
archived scenes (>1.7 million scenes). All of the new cloud coverage data are included in Image Management System
(IMS) databases of the ASTER Ground Data System (GDS) and NASA's Land Process Data Active Archive Center (LP
DAAC) and used for ASTER product search by users, and cloud mask images are distributed to users through Internet.
Daily upcoming scenes (about 400 scenes per day) are reassessed and inserted into the IMS databases in 5 to 7 days after
each scene observation date. Some validation studies for the new cloud coverage data and some mission-related analyses
using those data are also demonstrated in the present paper.
The GEO Grid is an e-infrastructure, which is capable in archiving large amount of satellite data and conducting
higher level processing using the advanced grid technologies.<sup>1</sup> The Advanced Space-borne Thermal Emission
and Reflection Radiometer (ASTER) Level 0 data are stored in a cluster system on GEO Grid, and ASTER
ortho-rectified radiance and Digital Elevation Model (DEM) products are able to be generated on this system
globally since 2000. This research shows validation of new ASTER surface reflectance products generated by
the GEO Grid system, which can apply the radiometric and atmospheric correction to ASTER ortho-rectified
radiance data of Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR).
The Third Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) concluded that many collectiveobservations gave a aspect of a global warming and other changes in the climate system. It is very important to understand thisprocess accurately, and to construct the model by whom an environmental change is accurately forecast. Future earthobservation using satellite data should monitor global climate change, and should contribute to social benefits. Especially, human activities has given the big impacts to earth environment. This is a very complex affair, and nature itself also impacts the clouds,namely the seasonal variations. JAXA (former NASDA) has the plan of the Global Change Observation Mission (GCOM) formonitoring of global environmental change. SGLI (Second Generation GLI) onboard GCOM-C (Climate) satellite, which is one of this mission, is an optical sensor from Near-UV to TIR. SGLI can provide the various high accuracy products of aerosol, cloud information, various biophysical parameters (Biomass, Land Cover, Albedo, NPP, Water Stressed Vegetation, LST, etc.), coastal information (CDOM, SS, PAR, CHL, SST, etc.), and cryospheric information (Albedo, Snow/Ice Cover, NDII, Sea ice type, Snow Grain Size, NDSI, Snow Surface Temperature, etc.). This paper shows the introduction of the unique aspects and characteristics of the next generation satellite sensor, SGLI/GCOM-C, and shows the preliminary research for this sensor.
Japan Aerospace Exploration Agency (former NASDA) has successfully launched a new Advanced Earth Orbiting Satellite (ADEOS-II) aboard an H-2A booster on December 14, 2002. ADEOS-II is designed to monitor global climactic change through researches of the Earth's environment. GLI, which is one of five sensors, has high potential for vegetation monitoring, and it will contribute to the future satellite sensor. GLI has 23 channels in VNIR which include 380nm channel, 6 channels in SWIR, and 7 channels in MTIR. And this sensor has two kinds of spatial resolution, which are 1km and 250m. GLI 380nm channel is very unique channel, which can be sensitive for aerosol over land.
GLI land higher level processing includes precise geometric correction, 16-day composite, atmospheric correction, and vegetation index (NDVI and EVI). However, GLI atmospheric correction for land is conducted for only Rayleigh scattering and Ozone absorption. Therefore, this atmospherically corrected NDVI is affected by aerosol over land. On the other hand, 380nm data has the capability of removal of aerosol over the land. The difference between TOA NDVI and the new NDVI subtracted 380nm can be a function of optical thickness of aerosol.
This paper shows that the evaluation of aerosol correction over the land by using GLI 380nm reflectance.
Cross calibration of data products across generations of satellite
program is indispensable to facilitate continuous data products by
satellite observations. To investigate long term environmental change
through vegetation monitoring, it is required to use data from
different platforms, e.g., normalized difference vegetation index (NDVI) from
NOAA-AVHRR series with the ones from TERRA- and AQUA-MODIS. In this
context, cross calibration of spectral vegetation index (VI) is an
important factor which determine the accuracy of such changes. In our
previous work, we introduced a way of deriving analytical relationships
between two vegetation indices based on an equation of vegetation
isoline. The functional form of the relationships was found to be a
ratio of polynomials. On the other hand, most of the studies that
investigate relationships of NDVI products between two sensors simply
assumed first- or second-order polynomial to describe the relationships
of the two data products. In this paper, we discuss the relevancy of
using higher-order polynomials by relating those coefficients
implicitly to biophysical parameters, atmospheric properties, and soil
optical properties. The order of polynomials sufficient to approximate
the relationships is clarified from both analytical and numerical point of view by conducting numerical experiments in addition to analytical derivations.
Propose of a new Vegetation Index is purposes. Ordinal vegetation Index can show intensity of vegetation on the ground. It can not show structure of vegetation surface or texture. Proposed vegetation index utilizes BRF property. It is generated from data from 2 orbit of satellite and be able to show structure of vegetation surface or texture. Principles of this index is coming from field observation using RC helicopter. Each vegetation canopy has different texture and roughness. New index, named BSI (Bi-directional reflectance Structure Index) shows difference of vegetation canopy. It is calculated by using the data of NOAA/AVHRR, ADEOS OCTS. ADEOS-II GLI can derive BSI.
The mission objectives of ADEOS-II (Midori-II) are to improve satellite-based global earth observation system, and to obtain earth observation data for the contribution to better understanding and elucidation of global change mechanism relevant to earth environmental issues. To implement the objectives, five onboard earth observation sensors are selected based on the science requirement primarily focused on the quantitative estimation of geophysical parameters to describe important processes of the earth system such as water and energy cycle, carbon cycle, and changes in polar stratospheric ozone. This paper describes the present status of level-2 products derived from AMSR and GLI observation data after the launch, in the middle of operational observation / calibration and validation phase, as of the beginning of August, 2003 after four months from the beginning of calibration and validation phase on April 15, 2003.
The ADEOS-II satellite was successfully launched with an H-IIA rocket from Tanegashima Space Center in southern Japan on December 14, 2002. Amongst the six remote sensing instruments on-board, the payload includes the Global Imager (GLI) - a 36-channel multi-spectral scanner developed by the National Space Development Agency of Japan (NASDA) for ocean, terrestrial, atmosphere and cryosphere applications. 30 bands operate with a 1 km spatial resolution, while the remaining six bands, primarily dedicated for terrestrial use, acquire data with 250 metres ground resolution at nadir. The cancellation of one of the two planned Data Relay Test Satellites (DRTS) required for data down-link however resulted in reduced acquisition capacity at 250 metre resolution and thus prompted the establishment of a dedicated 250-metre data observation strategy, which aims to optimise 250 m observations over land, and to provide spatially and temporally consistent, multi-seasonal global land coverage, on a repetitive basis during the life-time of the ADEOS-II satellite. Plans for 250 m data product generation are furthermore outlined briefly in this paper.