Depth sounding at the nearshore zone is difficult to carry out frequently because of financial constraints or lack of human-power resources. However, this data is very important in various coastal studies such as assessing changes in beach morphology, coastal evolution modelling and nearshore sediment dynamics. A simple method which produces scene independent parameters is presented to estimate nearshore zone water depth from satellite data. This aspect of the method may allow the parameters to be used for other scenes and multidate scenes (observation at future date) for the same sensor without field data for re-calibration if water column conditions are approximately similar to the conditions at calibration.
The model underlying the method was derived from Jerlov's model. In developing the model, it was assumed that the substrate material remains constant over the scene and that the reflectance characteristics of the substrate with zero water cover can be obtained from the digital count recorded at the shoreline. Since the shoreline represents the boundary between land and sea, its digital value was decided from a critical digital value. termed as threshold value to separate land from water body. In deciding the threshold value we introduce errors such as the effects of wave run-up and other environmental effects. By introducing an adjustment parameter cc; to account for these errors and effects, a linear relation was developed between the natural logarithm of the threshold value that has been corrected for deep water and atmospheric effects and the natural logarithm of digital count representing the substrate with zero water cover, Ai. This relation may enable Ai values at other scenes and multidate scenes (if water column condition is approximately the same as condition at calibration) to be estimated knowing the threshold value which is scene dependent and the adjustment parameter which is scene independent.
Through regression of known bathymetric data and natural logarithm of pre-processed satellite digital counts, water attenuation coefficient, k1, and Ai were determined. The adjustment parameter was calibrated with satellite observation data and depth sounding data. Water depth can be estimated by inputting digital

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We have studied an algorithm and a new vegetation index for analyses of ADEOS-II/GLI data, based on the pattern decomposition method.1) To simulate spectral response patterns of the GLI sensor, reflectances of about 450 samples were measured in the field with a spectrometer covering the spectral range of the GLI sensor. About 96% of the information of the nineteen-dimensional GLI data was successfully transformed into three pattern decomposition coefficients.2) It was confirmed.by the experiment using the spectrometer that land cover ratios in a pixel are estimated from the pattern decomposition coefficients correctly. Furthermore, GLI data with 250m and 1km spatial resolutions were simulated from LANDSAT/TM data with 30m spatial resolution. Using the data, it was shown that land cover ratios in the GLI pixel estimated from the pattern decomposition coefficients are nearly equal to those of TM pixels in corresponding areas of the GLI pixel.3) A new vegetation index, VIPD (Vegetation Index based on Pattern Decomposition) was developed. VIPD utilizes all the nineteen-dimensional GLI data, and reflects the amount of vegetation and the degree of vegetation vigor. The index is more sensitive for vegetation cover ratio, for the vertical thickness of vegetation, and for vegetation type, such as broad leaves and needle leaves, than is NDVI.
From these results, it became evident that the algorithm based on the pattern decomposition method is sufficiently able for analyzing hyper-multidimensional GLI data, and the new vegetation index is useful in the study of vegetation.

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By using 6S code, atmospheric effects on the channels 1 and 2 data of NOAA/AVHRR imagery for observing NDVI in the midlatitude and subarctic regions are evaluated. It is found that the amount of disturbance highly depends upon sun-target-sensor geometry and terrain elevation at the ground point of object as well as atmospheric conditions. A new atmospheric correction algorithm by applying a look-up table (LUT) method to 6S code is proposed to reduce processing time for the huge volume of AVHRR data in the operational mode. Finally, the algorithm is applied to the five AVHRR images of Inner Mongolia, and is confirmed its accuracy and efficiency.

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New methods for ice concentration estimation from satellite based passive microwave data based on inversion are proposed. The proposed methods are superior to the existing methods, the NASA Team algorithm and the Comiso's Bootstrap algorithm with up to 45% of improvement on ice concentration estimation accuracy based on the simulation study. Also 1.5 to 2.1% of improvement was achieved for the proposed method compared to the NASA Team and Comiso's Bootstrap algorithms for the actual SSM/I data of Okhotsk uisng JERS-1/SAR data as a truth data for estimating ice concentration.

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