Lake Kasumigaura is polluted by massive growth of blue green algae during a typical summer. Measuring a quantity of chlorophyll-a in the phytoplankton is of importance in tackling with the lake water pollution problem. In this paper, a remote sensing method to estimate distribution of rich chlorophyll-a concentration in lakes or inland seas is proposed. In particular, relation between the quantity of chlorophyll-a and the spectral signature of water is investigated based on the observed data on the water surface.
To eliminate the influence of specular reflection from the water surface on the spectral signature of water, spectral reflectance below the water surface was measured. And the basic relationship between chlorophyll-a concentration and the spectral characteristics of water was analyzed. As a result, chlorophyll-a estimation model was derived using the ratio of spectral reflectance at two different wavelengths of 675 nm (red range) and 700 nm (near-infrared range). It was found that the spectral signature of near-infrared range is effective in remote sensing of rich chlorophyll water area like lakes or inland seas. Furthermore, in order to verify the appropriateness of the proposed model, theoretical analysis on the model was investigated based on the Kubelka-Munk theory.

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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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