This paper proposes a new model to estimate trees parameters, like tree size or density. Estimation of trees parameters using remote sensing data is required, for example, to evaluate greenhouse gas absorption or to manage forest. For this purpose, Geometric-Optical model has been proposed. This model calculates the geometry of a satellite and trees analytically, but it is difficult to consider trees over a slope area with oblique observation. Ray tracing technique is used in our new model to calculate the geometry accurately then the new model has much flexibility. Our new model also considers the spectral features of trees and grasses from a spectral library. Our model can be useful for spectral analysis. For the modeling, observation condition, environment condition, and observation target are defined. These include the satellite position, the sun position, target trees and grass. Then ray tracing is used to calculate the observed image geometrically. At that time, the model calculates the sunshade and shadow of trees also. After ray tracing, each pixel is assigned the spectral features from spectral library. The model calculates the brightness depending on the spectral band. Finally the pixel size is reduced to match the real satellite observation.
Trees parameters can be estimated to resolve an inverse problem based on this model using surface reconstruction method. Tree density and the size has been calculated from the simulated satellite image to evaluate the inversion.
The model was also evaluated by BOREAS data to estimate the tree distance by the surface reconstruction method. The estimated results was compared to the GIS data, and reached to the good results.

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The campaign named "Sea ice observation experiment in the Sea of Okhotsk" has been carried out on Lake Saroma and its surrounding area since 1993. The objective of this experiment is to evaluate a possibility of SAR data to monitor sea ice in the Sea of Okhotsk. Lake Saroma is a salt water lake which connects to the Sea of Okhotsk through two channels, and covered with ice in winter time. The salinity, surface roughness and vertical layer structure of the ice are considered to be similar to the thin first-year ice in the Sea of Okhotsk based on the truth data. Therefore, Lake Saroma was selected as one of experimental sites of this study.
The first result we found was that SAR backscattering coefficient decreased as the ice thickened. It was considered that SAR observed a difference of ice surface dielectric constants which were related to the ice thickness. The backscatter from a first-year ice is generally dominated by surface scattering. If the roughness of the ice is known and its effect to the backscattering coefficient is estimated, the backscattering variation caused by the change of dielectric constant can be detected.
RADARSAT has a capability to change the incidence angle to get incidence angle dependencies of the target backscatter. Since the incidence angle characteristic of surface scattering is changed with roughness, there is a possibility to estimate roughness of the target by multi-incidence angle SAR data.
This paper proposes a new method to retrieve ice surface roughness and thickness from RADARSAT multiincidence angle data. This method is applied to the data acquired over Lake Saroma in 1998. The results show reasonable agreements in measured and estimated ice thickness as well as ice surface roughness at Lake Saroma sampling sites.

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The measurement error in the horizontal wind velocity with a space-borne incoherent Doppler lidar system was estimated by using modeled atmosphere and analytical calculations. The result suggested that the integrated pulse energy of more than 100J and the detector with the quantum efficiency of more than 50% are necessary to satisfy the demand of numerical weather precision. The calculated errors for coherent and incoherent Doppler lidar systems were compared with varying each resource parameter and signal-to-noise ratio. The result indicated that the coherent Doppler lidar system is more useful than the incoherent one under usual aerosol condition, while the incoherent one is useful in case of very low aerosol density.

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Sea fog in Huang Hai sea is observed by NOAA/AVHRR data at April 2001. Its large CCT values of 3 to 5 channels and small Ch4-Ch5 value can be used to distinguish from cloud. The sea fog does not arrive at high altitude and its lifespan is about 1 day.

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The dataset of AVHRR 10-day composite image for Asian region is produced by using HRPT data received at Tokyo University, Kuroshima (Japan), Ulaanbaatar (Mongolia) and Bangkok (Thailand). The processing for dataset includes radiometric calibration, geometric correction, extraction of optimal pixels and atmospheric correction. The dataset is composed of 13 layers which include NDVI and SST, the reflectances in channels 1 and 2, the albedo in channels 1 and 2, brightness temperatures of channels 3, 4 and 5, sun-target-sensor geometry of sensor zenith, solar zenith and relative azimuth angles and measurement date of each pixel selected. The datasets for 1998 and 1999 are available.

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The calibration standard of the spectral radiance that provides the reflected radiance on the standard panel derived from the standard lamp is easy to use and maintain for field radiometer users. The reflected radiance of this calibration standard is, however, not directly calibrated by a blackbody but calculated from the bi-directional reflectance factor of the standard panel and the irradiance of the standard lamp with regulated DC power supply. Thus, the reflected radiance of the calibration standard may not always have enough accuracy for the radiometric calibration of field radiometer, even if the standard lamp and the standard panel are well calibrated. The validations for the reflected radiance of the calibration standard were therefore conducted by the National Space Development Agency of Japan (NASDA) calibration standard, which has been directly calibrated by the blackbody, and by the solar-based calibration method at Ivanpah Playa and Railroad Valley, NV, USA. Since, these validations showed large difference in reflected radiance, the practical calibration standard of spectral radiance was obtained only for practical use of field radiometer calibration by calculating the reflected radiance of this calibration standard from the NASDA calibration standard. After the calibration, field radiometers from seven organizations were calibrated by measuring the common radiance of this practical calibration standard.

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