従来のモデルではChl.a濃度の推定が困難であった懸濁物質量や溶存有機物量の多い茨城県霞ヶ浦を対象水域に選び,水面で測定されたスペクトルデータを基にChl.a濃度と水面直下での分光スペクトルの関係を調べた。検討結果を以下にまとめる。
ChLaの吸収帯である波長(675nm)と従来水質の定量には使用されなかった近赤外波長帯(700nm)の2波長の水中分光反射率の比は,日時が変化してもChl.a濃度とかなり高い相関があった。このことより,リモートセンシングにおいて近赤外光の波長帯が,懸濁物質量や溶存有機物量の多い水域に対し有効であることが示された。
さらに,その提案されたChLa推定モデルが懸濁物質の影響が強い水域においてなぜ有効であるのかクベルカームンクの式を基にいくつかの仮定をもうけて理論的に検証した。その結果,675nmと700nmとの水中分光反射率の比は,水中の物質の後方散乱の影響を避けることができ,Chl.a濃度が0～30[μg/l]の場合,提案したモデルは,SS濃度に関係なくChl.a濃度を推定できることが示された。また,Chl.a濃度が30～60[μg/l]の場合は,SS濃度が既知ならば(17)式によりChl.a濃度を推定することができることが示された。Chl.a濃度が60[μg/l]以上の場合,計算値と実測値は,一致しなかった。
今後,Chl.a濃度が60[μg/l]以上の場合の検討および他の季節における提案したChl.a推定モデルの検証,そしてその推定モデルを実際のリモートセンシングに応用するために水面反射光や大気散乱光の影響を除去する方法についての検討が必要である。また,このような波長を有する高濃度水域観測用センサーの開発に向けての努力が必要である。

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The purposes of this study are to investigate quantitative characteristics of brightness temperature of LANDSAT/TM and to propose convenient method to rectify it. By the regression analysis between brightness temperature of TM/Band 6 and that of NOAA/AVHRR/Channel 4 and 5, it was proved that the brightness temperature of TM has wider dynamic range than that of AVHRR. Then, the equation for rectifying the error of the brightness temperature of TM was derived based on the regression analysis. The rectification by this equation provided good accordance between temporal change of land surface's brightness temperature and that of air temperature. The rectified brightness temperature also got closer to the surface temperature measuered on the ground. These results verified that rectification of the brightness temperature of TM by the regression is a practical method to improve its reliability.

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Some effective use of GMS image data is examined in this paper for climete analysis over very wide area. A data set which even an ordinary personal computer is capable of handling easily is derived as an example from all the infrared data observed in the whole area covered by GMS. With the use of this data set semiglobal investigations are made on power and cross spectrum analyses. Results obtained are found not only to.confirm the well known periodicities and westward or eastward waves on climete, but also to indicate interesting geographical features on spectra, new travelling waves and others.

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Wetland vegetation classification was performed using casi image and measured detailed elevation data was overlaid with the casi image to analyze the relationship between the vegetation distribution and the slight elevation difference inside the wetland. This relationship exists because the water content distribution in wetland is related to the elevation difference. The analyses we have conducted are as follows. 1) An airborne spectral image (casi) with 2 m ground resolution was acquired over the Akai mire wetland on the June 2, 1994. When the image was acquired, the wetland vegetation was at the initial growth stage. In the analysis, radiance data of 3 channels (green, red, and near infrared band) which are known effective for detecting wetland vegetation difference were used. 2) 50 m grid digital elevation data were measure at both inside and surrounding wetland area by a total station. These data were interpolated and transformed into the 2 m raster data, to which the casi image was geometrically corrected and overlaid. 3 D view image of the casi data was produced using this digital elevation model. By comparing these images, relationships between wetland vegetation condition and the elevation difference were clarified. 3) k-means clustering (unsupervised learning) method was used to classify the casi image. 20 cluster classes were first calculated and merged into 8 vegetation community classes by vegetation investigations. We have succeeded in classifying even the sphagnum moss types. 4) The correspondence between the contour plot of detailed elevation and the wetland vegetation have shown the correspondence between the elevation differences and the vegetation types such as shhagnum moss types, grasses infesting on the sphagnum moss, and Pine trees at the edge of the wetland.

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This paper describes estimation of vegetation cover rates and vegetation vigor independently by spectral reflectance. In the study of plants by remote sensing, Normalized Differential Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI) and Vegetation Index (VI) have been commonly used. These indexes, which use the reflectance of red and near infrared, indicate the information about vegetation cover rates and vegetation vigor inclusively. Therefore, we made experiments to estimate vegetation cover rates and vegetation vigor independently using manilagrass in the growth cabinet. The results show that the reflectance around 550 nm and 980 nm is not affected by vegetation vigor but vegetation cover rates. The authors derive new indexes based on the result. Vegetation Cover Index (VCI), which is the reflectance difference between 550 nm and 980 nm, indicates vegetation cover rate independently of vegetation vigor. Vegetation Vigor Index (VVI), which is the ratio of PVI to VCI, indicates vegetation vigor independently of vegetation cover rates. The other experiment using sorgo shows the applicability of the new indexes to other crops in the field.

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During the night pass, NOAA satellites transmit processed AVHRR CH.3 and CH.4 data in APT (Automatic Picture Transmission) format. A method to improve the spatial resolution of APT for practical use is proposed. More than 120 APT scenes received at around 11 Z in winter season from 1994 to 1995 were examined to select 55 meso-scale pictures of cloud-free sea areas around the Japan. Islands. Through the comparison of average temperature between APT-CH.A (AVHRR-CH.3) and CH.B (CH.4), the difference is found to be negligible only when the sea surface temperature is below 18-C. By reconstructing an interlaced imagery from these two images, spatial resolution improvements can be accomplished. Several examples show the improvement by one hundred and several tens of percentage in the extraction of current front edges in the cloud-free sea.

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