Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a multispectral highspatial resolution imaging sensor being developed by Japanese Ministry of International Trade and Industry and one of facility sensors on US National Aeronautics and Spase Administration's EOS-AM 1 platform slated to be launched in June 1998. In this paper, algorithms for water surface temperature (WST) estimation using data from Thermal Infrared Radiomater (TIR), which is one of ASTER subsystems, were investigated through linear regression analyses of simulated ASTER TIR data.
ASTER TIR has five channels in the thermal infrared spectral region (8-12μm) with twelve-bit quantization and expected noise equivalent temperature difference (NEAT) less than 0.3 K. ASTER TIR imagery will be useful for the monitoring of small scale thermal phenomena in coastal regions and lakes because of its high spatial and radiometric resolutions. The capability of multichannel observation of ASTER TIR allows the application of empirical algorithms for water surface estimation such as Split-Window Method.
Brightness temperature values measured by ASTER TIR were calculated using LOWTRAN7 atmospheric transmission and radiance code for various WSTs, atmospheric models, and NEATs. In addition to six atmospheric models originally included in LOWTRAN7 code, twelve atmospheric models were prepared and used in LOWTRAN7 calculation to represent atmospheric characteristics around Japanese Islands. Linear regression analyses of these simulated data indicated that, with NEAT of 0.3 K, expected errors of estimated WST by both Two-Channel and Five-Channel Methods are less than 1.1 K. It was also shown that coefficients of equations for WST estimation depend on atmospheric models used in the regression.

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As for river flowing through urban or residential area, careful consideration should be given to the prevention of the flood disaster and conservation of river environment. Having lots of flood prevention installations such as motor pump houses, sluices and sluice pipes, the confluence space of river is a section of especial importance for river management. Since the effects of rising stage and turbulent flow on the river structure such as revetment and groin on the downstream from the confluence cause anxiety, it is desirable to analyze these phenomena quantitatively. On the assumption that the distance of a river flow in which a homogeneous mixture of suspended and dissolved solids is made has a close relation with the length of confluence section through main river and tributary, it is thinkable that the confluences section length may be estimated quantitatively by statistically processing the spectral reflectance characteristics of the ground surface substance obtained from satellite remote sensing data.
In this study a method to estimate the length of confluence section was examined, reading turbidity with the aid of spectral reflectance characteristics obtained from the data of the earth observation satellite. The investigation site was the confluence section of the Kise River which joins the Kano River at Tokura region on its downstream. The Kano River empties into Suruga bay in the neighborhood of Numazu City, Shizuoka Pref.

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Various kinds of high resolution remote sensors are available, such as Landsat TM (spatial resolution 30 mx 30 m), SPOT HRV (20m×20m) and ADEOS AVNIR (16m×16m) launched in 1996. It is significant to quantify the accuracy of interpretation or classification of high resolution remotely sensed images. This paper assesses land cover classification accuracy quantitatively for four remotely sensed images acquired almost simultaneously with various sensors with different spatial resolutions: Landsat TM, SPOT HRV, JERS OPS (18m×24m), Airborne multispectral scanner (AMSS: 6.25m×6.25m). Comparison of the four classified images shows that the AMSS image with the finest resolution has not given remarkable improvement in terms of classification accuracy. Moreover, the authors compare the false remotely sensed images produced by three degradation algorithms. The first algorithm is using arithmetic mean, and the second one is using cubic convolution interpolation, and the third one is using MTF. The algorithm using MTF is recommend as the image degradation algorithm in a strict sense. The false images using MTF have 10-m to 30-m resolution resampled from the 6.25-m AMSS image. Consequently, the 6.25-m AMSS image and the degraded images with finer resolution than 12 m have not given better classification. These consequences that the images with the finest resolution has not given the finest classification accuracy is attributed to the limited number of categories considered. The authors also produce the 16-m false AVNIR image by the degradation algorithm using MTF, and the classification accuracy of ADEOS AVNIR images is predicted.

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The effect of specular reflection at the water surface is one of the most important problems in estimating water quality concentration by remote sensing. Its removal is indispensable for precise water quality estimation.
Takashima et al. developed a model removing the effect of specular reflection at the water surface besed on Cox and Munk model. However, the Takashima model has not been verified yet whether it works well in the real case. In this study, validity of the model was verified based on the spectral signature data measured above and below the water surface. As a result, it was found that the model work well when wind velocity measured above the water surface is more than 1 m/s.

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