Recently, an anthropologenic activity causes an environmental issues in the global ecosystem. Especially, the wetland ecosystem, which includes rare vegetations and animals, is very sensitive to environmental and climate changes locally as well as globally. In addition, wetlands play an important role in the climate change as a source of methane, one of the greenhouse gasses. Therefore, it is very important to monitor wetland environment globally by remotely sensed data.
Synthetic Aperture Radar (SAR) can acquire the data in almost all weather conditions and give us a possibility to monitor biomass related to vegetation structure. This paper describes some results applying SAR data to monitor wetland biomass. The RADARSAT data acquired in different incidence angles were compared with biomass data measured in Kushiro wetland. We found optimum incidence angle related to ground surface conditions in monitoring wetland biomass. A simple backscattering model for wetland vegetation was developed and applied to derive backscattering coefficients in different biomass and surface conditions. This model could explain scattering mechanisms which is related to wetland biomass.

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Retrieval of precipitable water (vertically integrated water vapor amount) is proposed using near infrared channels of Global Imager onboard Advanced Earth Observing Satellite-II (GLI/ADEOS-II). The principle of retrieval algorithm is based upon that adopted with Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Earth Observing System (EOS) satellite series. Simulations were carried out with GLI Signal Simulator (GSS) to calculate the radiance ratio between water vapor absorbing bands and non-absorbing bands. As a result, it is found that for the case of high spectral reflectance background (a bright target) such as the land surface, the calibration curves are sensitive to the precipitable water variation. For the case of low albedo background (a dark target) such as the ocean surface, on the contrary, the calibration curve is not very sensitive to its variation under conditions of the large water vapor amount. It turns out that aerosol loading has little influence on the retrieval over a bright target for the aerosol optical thickness less than about 1.0 at 500nm. It is also anticipated that simultaneous retrieval of the water vapor amount using GLI data along with other channels will lead to improved accuracy of the determination of surface geophysical properties, such as vegetation, ocean color, and snow and ice, through the better atmospheric correction.

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This paper estimates the vegetation cover ratio in Tokyo metropolitan area. The research carried out to develop a practical technique for promoting efficiency constructing a vegetation cover map by using satellite remote sensing. Though previous studies have proposed to estimate vegetation cover ratio from normalized difference vegetation index (NDVI) based on empirical formulae, physical meaning of those methods are not clear. Besides, those methods are not necessarily practical, because the other vegetation cover ratio data are required to correspond with. Thus, this paper proposes a method to set up estimation formulae by applying linear combination model, which allows to express area ratio more directly. This method is based on the assumption that the radiance of a mixel is represented by linear combination of radiance of its components. To verify this method, vegetation cover ratio of the test site, selected in the City of Inagi, was estimated using JERS-1 OPS data and compared with verification data generated by interpretation of aerial photograph. We concluded that this method is a more practical method. Further, relationships between vegetation cover ratio estimated by the proposed formulae and NDVI of urban land surface, which is a mixed land cover, is shown. These relationships are explained by intervals of intersection points of segments between pure pixels and isoline of NDVI on Visible-Near infrared wavelength space (V-NIR space). Using land use data as substitution for land cover category and applying this method to JERS-1 OPS data, the vegetation cover map of Tokyo metropolitan area was constructed.

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The most comprehensive large-scale characterization of the global sea ice cover so far has been provided by satellite passive microwave data. The energy exchange between ocean and atmosphere over ice-covered areas is largely influenced by the occurrence of openings in the ice pack and of newly and young ice. Thus, accurate retrieval of thin sea ice areas and their ice concentrations from these data is quite important. NASA Team Thin Ice algorithm was proposed for mapping the distribution of new, young and first-year sea ice and calculating ice concentrations in seasonal sea ice zones by Cavalieri (1994). In this paper, a new algorithm is proposed for mapping the global thin sea ice area in freezing season. It utilizes two sea ice concentrations and two mixed ratios of sea ice types derived from NASA Team Standard algorithm (using global tie points) and NASA Team Thin Ice algorithm (using thin ice tie points). The brightness temperatures of 100% sea ice concentrations are calculated using the sea ice concentrations and the mixed ratios of sea ice types from the two algorithms respectively. The thin sea ice area is detected by comparing the brightness temperatures of 100% sea ice concentrations derived from the both algorithms.

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In the conventional scheme of geometric correction of satellite imagery, a number of ground control points (GCPs)are used to determine the geometric transform equation statistically. The selection of GCPs on the map is usually arbitrary, and the identification and measurement of GCPs on the satellite image need intensive manual labor unless they are determined automatically. In this paper, we propose a new geometric correction method, which directly determine the optimum geometric transform equation without selecting the GCPs. It is noted that satellite data over rugged terrain is highly correlated with direct solar irradiance, which can be calculated by using digital elevation model and solar position. The correlation between the simulated irradiance image and the ortho-rectifieds atellite image is used to evaluate the geometric transform equation and to optimize itsparameters. The method is applied to system corrected Landsat TM data with geo-reference parameters provided by NASDA. It is shown that optimization on two parameters (scene center displacement) is sufficient for relatively narrow area (18km by 18km) with subpixel accuracy.

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