This paper presents the algorithm to estimate the Evapotranspiration Index (ET-Index)
developed for a research product of the 1st generation of the Global Change Observation Mission satellite for the
Climate (GCOM-C1) satellite of the Japan Aerospace Exploration Agency (JAXA). The ET-Index is
equivalent to a widely used "Crop Coefficient" in the field of irrigation engineering, defined as the actual
evapotranspiration normalized for weather conditions. The ET-Index is convertible to an actual quantity
of evapotranspiration using local weather data. In the proposed method, the ET-Index is estimated
primarily by the land surface temperature image of a satellite, with some additional inputs including the
Digital Elevation Model (DEM) and global wind speed reanalysis data.
The algorithm estimates the ET-Index by using the surface temperature as an indicator of
surface wetness, employing two extreme hypothetical surface conditions called "wet surface," defined as a
surface having a zero sensible heat flux, and "dry surface," defined as the surface having a zero ET. A
derived ET-Index map is widely applicable for water resources management in agriculture and
environmental conservation. Applications of the proposed algorithm to Landsat and MODIS thermal images showed
good performances in semi-arid regions in China and the western United States.
This paper shows a way to use a general digital camera as a multi spectral camera. The purposes of this
development are cost reduction and simplified processing for the spectroscopic measurement.
It is necessary for obtainning radiance in each pixel to know the camera’s sensitivity and spectral response. So
authors used a camera which can store images as RAW format in this study. Authors estimated the camera’s
RGB-sensitibities and RGB-responses based on discrete expression of RGB-responses, approximation of RGB-
sensitivities and exposure relationship and simultaneous estimation scheme of sensitivity and response. So
authors have been able to compute incident radiance from a RGB pixel value with 8bit accuracy.
Also in this paper, authors developed the spectral response dividing method with a long-pass filter.
As a primary application of this method, radiance based NDVI and red edge information can be estimated. The
NDVI or red edge image is made from an image taken by a digital camera which has sensitivity in the near
infrared spectrum. This image is validated by simultaneously measured radiance with a spectroradiometer.
The fire detection product from the sensor named SGLI onboard the upcoming JAXA’s satellite GCOM-C1
will be produced. The fire detection algorithm and the fire temperature and the fire proportion algorithm are
developed. SGLI does not have 4 micrometer channel which plays the important role to detect the fire, but
SGLI has 2 observation channels in SWIR window spectrum. The satellite detected radiance is sensitive with
the high temperature within the pixel. These 2 channels are used to detect the fire and the fire temperature and
proportion with the combination of the near infrared and thermal infrared spectrum data.
In the Northern hemisphere, the CO2 concentration in the warm season indicated anomalously high values in 2003, and low values in 2004. To investigate the reasons of the interannual variation, a numerical simulation using a land biosphere – atmosphere full couple GCM was carried out. Relationship between interannual variations of CO2 and those of the land surface elements was investigated. In 2003, high surface temperature and low soil wetness conditions in the Eurasian Continent and in North America, and low downward short wave radiation condition in East Asia, occurred in the warm season. It is considered that these climate conditions in 2003 induced relatively low GPP and NEP values in the
continental scale. Comparison of the simulation results of GCM with satellite data (MODIS and AMSR-E) was
performed concerning the remarkable interannual changes from 2003 to 2004. Global distributions of the seasonal
changes by the model almost agree with those by the satellite data regarding both the land surface temperature and the
soil moisture. The interannual changes of land surface temperature by the model agree well with those by the MODIS
data. As to the soil moisture, the regions exist where the interannual changes by the model disagree with those by the
AMSR-E data especially in the warm season. The values of elements calculated by the model are physically and
bioecologically consistent each other in the model. Therefore, the model results are useful as the relative information for
the validation of the global scale or regional scale products of satellite data estimated separately by each algorithm.
So far the land surface temperature (LST) estimation from space is made by many kinds of sensors, as the operational
product, ASTER1 and MODIS2 onboard TERRA satellite made the land surface temperature product
in early 2000. Just after this, AATSR3 onboard the European satellite ESA published the land surface product.
The operational land surface temperature estimation has about 10 years history and the improvement of the
estimation algorithm are made. The LST estimation has the intrinsic difficulty which the unknown variables
are more than the formulae. To avoid this difficulty, MODIS and AATSR use the statistical method which the
surface emissivity is assigned as the known variable and ASTER uses the semi–analytical method which estimates
the land surface temperature and emissivity simultaneously from the atmospheric-ally corrected satellite
radiance. The both methods has complementary advantages and disadvantages so that these methods improved
independently. The author tried to integrate the split window formula to the semi–analytical method as the additional
formula to make the problem determine for the SGLI sensor onboard GCOM–C1 which will be launched
2015 by JAXA. This paper describes the detail of the integration and the estimation results.
The early stage of the water stressed forest shows the higher temperature before the spectral reflectance change. To
detect the water stressed forest, the satellite detected surface temperature is utilized. The day and night surface
temperature difference is the key factor of the detection, in the case of non-stressed forest the daytime surface
temperature suppress the latent heat increase and the nighttime surface temperature is almost same as the air temperature
at the surface, so that the water stress makes the daytime temperature increases. The day and night surface temperature
difference is primary affected by the forest water stress level. To remove the another effect to the temperature difference
such as the nighttime low air temperature in autumn, the modified day and night surface temperature difference is
defined for the forest water stress detection index. Using the day night surface temperature product from MODIS and the
latent heat flux dataset acquired at some sites of the AMERIFLUX, The water stressed forest is identified using the
proposed index. Also the numerical simulation for the sensitivity analysis of the proposed index is made and the
effectiveness of the index is clarified.
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.1 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 Fourth Assessment Report of IPCC predicted that global warming is already happening and it should be caused from
the increase of greenhouse gases by the extension of human activities. These global changes will give a serious
influence for human society. Global environment can be monitored by the earth observation using satellite. For the
observation of global climate change and resolving the global warming process, satellite should be useful equipment and
its detecting data contribute to social benefits effectively. JAXA (former NASDA) has made a new plan of the Global
Change Observation Mission (GCOM) for monitoring of global environmental change. SGLI (Second Generation GLI)
onboard GCOM-C (Climate) satellite, which is one of this mission, provides an optical sensor from Near-UV to TIR.
Characteristic specifications of SGLI are as follows; 1) 250 m resolutions over land and area along the shore, 2) Three
directional polarization observation (red and NIR), and 3) 500 m resolutions temperature over land and area along shore.
These characteristics are useful in many fields of social benefits. For example, multi-angular observation and 250 m high
frequency observation give new knowledge in monitoring of land vegetation. It is expected that land products with land
aerosol information by polarization observation are improved remarkably. We are studying these possibilities by ground
data and satellite data.
Stratospheric Platform (SPF) project is an enterprize to develop
an airship to float at the altitude of 20 km for the use of
earth observations and telecommunications. SPF-II is one of the
steps toward realizing SPF to examine the stabilized flight of
an airship at the altitude of 4 km, and is planned to be aloft
in 2004. The earth observatoin facility on SPF-II will consist of three sensors: a visible-near infrared (VIS-NIR) sensor for the wavelength region 500 -- 1000 nm, a thermal infrared (TIR) sensor, and a trafic-monitoring sensor. In this paper, we present an outline
of VIS-NIR and TIR sensors. The VIS-NIR and TIR sensors are planned as wide field (110 degree) imagers with 2-dimensional FPAs. Polarizetion will be measured with the VIS-NIR sensor. FOV of the VIS-NIR sensor is to be 8 km square at the footprint, and horizontal resolution to be 8 m with a 1280 × 1024 pixel Si-CCD FPA.
The TIR sensor adopts an uncooled 320 × 240 pixel bolometer array, and has a FOV of 8 km × 6 kmsquare at the footprint, with horizontal resolution of 25 m. It covers three wavelength bands of 8.5, 10.8, and 12.0 μm with the filter wheel device.
Land Surface Emissivity product is one of standard products generated from Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra satellite launched in December 1999. This product is important for detailed lithologic mapping and precise land surface temperature determination. The accuracy of ASTER-derived emissivity is a function of various factors such as radiometric calibration of the instrument, assumptions used in a temperature-emissivity separation algorithm, and spatial temperature/material mixture in a pixel. In this study, the effects of spatial material mixture on ASTER-derived emissivity are investigated as one of the validation activities of this product. First, the mixture effects on ASTER-derived emissivity are evaluated through numerical simulations under various land surface material and temperature conditions. Also, at several sites including Cuprite, Nevada, ASTER-derived emissivity and airborne sensor-derived emissivity are compared. Applications of ASTER emissivity products to environmental and geologic studies will be also presented.