The estimation of crop phenology is necessary to grasp the water use of irrigated farmlands. To estimate the crop phenology, time-series patterns of Leaf Area Index (LAI) are considered effective. A method is proposed for extracting LAI time-series patterns by calculating the difference between vegetation coverage estimated in visible bands and visible + near-infrared bands. The method proved to be more suitable for the estimation of LAI time-series patterns than conventional vegetation indices.

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To assess environmental change at global scale, accurate estimates of surface radiative fluxes at high temporal resolution are needed. An algorithm for the estimation of the shortwave radiation budget from the GMS-5/SVISSR data has been developed. In this study, a component of this algorithm used for deriving COT is evaluated. The COT retrieved from the GMS-5/SVISSR is compared with similar parameters derived from Terra/MODIS during APEX-E2. It was found that the assumption on the effective radius of clouds as well as the sensor quantization noise can introduce a large error in COT derived from GMS-5/SVISSR. In the present analysis we show that the errors in COT of area-level clouds in the aggregate due to unknown effective radius can be reduced progressively as compared to errors of pixel-level ones.

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We compared satellite and ground-based observations of tropospheric NO₂ to test whether satellite observations could successfully detect the behavior of tropospheric NO₂. The satellite data were tropospheric NO₂ vertical column density (VCD) derived from Global Ozone Monitoring Experiment (GOME) spectrometer measurements (hereafter GOME-NO₂), and the ground-based data were surface NO₂ volume mixing ratio (VMR) observed by the network of air-quality monitoring stations in Japan. The analysis was performed in the Tokyo region (the Kanto Plain) from January 1996 to June 2003. A strong correlation between GOME-NO₂ and the surface VMR was observed, with the two quantities showing similar seasonal variation of maximum in winter, minimum in summer. This provided initial evidence that GOME was successful in observing the behavior of NO₂ near the surface level in the Tokyo region. We performed a more rigorous comparison in which the surface NO₂ VMR was scaled to the tropospheric VCD using vertical NO₂ VMR profiles, which were calculated using the chemical transport model CMAQ/REAS. This second comparison indicated that the GOME observations represent the behavior of NO₂ more closely at the relatively unpolluted stations than at the highly polluted stations in the network of air-quality monitoring. This tendency was thought to result from the horizontal heterogeneity within a GOME footprint. Comparison with a previous study in northern Italy showed that the GOME-NO₂ measurements over Tokyo tended to be smaller than those over northern Italy. Because Tokyo is located in a coastal land region with a gulf, areas of ocean intruding into the GOME pixels could lower the observed GOME-NO₂. The pollution in Tokyo is so spatially concentrated that the rural regions contaminating GOME pixels could also reduce the observed NO₂ concentration from its true spatially resolved value.

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Cloud is always problem of optical remote sensing data. ALOS/AVNIR-2 images also will be affected. Microwave images are not affected by cloud. ALOS satellite has both optical and microwave sensor, similar resolution (∼10m). Therefore, main purpose of this study is to develop a method for removing cloud in ALOS/AVNIR-2 images based on ALOS/PALSAR data. This method is developed based on interpolating under cloud pixel values for ALOS/AVNIR-2 images. To remove cloud, it needs to be defined. A combination method of Total Reflectance Radiance Index (TRRI) and Cloud-Soil Index (CSI) is used to define cloud. Because around cloud pixels are mixture of cloud and other objects, that is very difficult to define. Therefore, the around cloud pixels are extended from cloud. Cloud shadow problem also is discussed in this study. Condition to apply this method is satisfied when objects in optical image and radar image change not very much. This method is experimented on simulated ALOS data from Landsat/TM and JERS-1/SAR images. Interpolated image is a free cloud and shadow image. Visual logic of objects is good. Original image and interpolated image are almost similar together. This method also can be applied for combination of two optical images to remove cloud if change of objects covered by cloud is not so much. Result of this study is a program with many functions like : define cloud, extend cloud, get shadow, remove cloud and so on. This is free software for every user.

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Interferometric SAR (InSAR), which is an application technique of synthetic aperture radar, is becoming established as the method for monitoring of ground displacement that can observe subtle surface movement over a wide area at high resolution. In this study, the authors developed the method to measure a long-term deformation by combining InSAR and time series analysis, aiming at establishment of practical and flexible measurement technique. It utilizes smoothness constrained inversion which assumes the amount of deformation as unknown parameter for time series analysis. The proposed method separates with ground change and noise components from each combinations of InSAR, and composes long-term deformation.
We applied it to the measurement of ground subsidence around Kanto Plain with ENVISAT/ASAR data, and verified its accuracy by comparing with leveling data obtained around Kujukuri area. As a result, it was shown that the spatial shape of local uplift and subsidence areas detected by the proposed method were analogous with the deformation map generated from leveling data. In addition, it was also shown that the correlation coefficient between the result of proposed method and leveling data were high, although bias of about 10mm was included.

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