Aerial photographs and GIS were applied for assessing landslide hazard, producing landslide hazard maps, and evaluating these maps, in the Kulekhani watershed (124 km²), located in the central region of Nepal. A landslide distribution map was produced from aerial photo interpretation and field checking. Eight factors, slope gradient, slope aspect, elevation, drainage basin order, distance from ridge, distance from valley, geology, and land use/cover, were analyzed by a multivariate statistical (discriminant) method, to determine the factors and classes influencing landsliding. Using a GIS, three different sets of samples from non-landslide group, and one set of samples from landslide group were generated from the unaligned stratified random and simple random sampling methods, respectively. Three different combinations were then used to examine the effects of the sampling methods on the critical factors and classes, and on the hazard maps produced from the results of the analysis. Each hazard map was produced with four levels of hazard classes: high; moderate; less; and least. The geology was found to be the most important factor to landsliding. Other important factors include elevation, land use/cover and slope aspect. Combinations of different samples did not yield substantial difference in the critical factors and classes, implying that the unaligned stratified random sampling is the appropriate sampling technique for generating the non-landslide group. The accuracies of three hazard maps were found to be acceptable for their practical use in hazard mitigation and watershed management planning.

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NDVI from satellite data offers various kinds of physical values related with plants. Landsat TM has high resolution and gives the seasonal change of NDVI for each land cover. Then, we proposed seasonal change models for NDVI using a logistic curve. NDVI for each land cover except water surface fitted very well with logistic curves for less than 300 Julian day. We modified the logistic curve adding a negative-slope line. This modified logistic curve expresses periodic change and gave good fit with NDVI. On the other hand, leaf area, leaf length, dry weight, and spectrum reflectance were measured for paddy pots in greenhouse cultivation. These results led to a series of relationship among these parameters. Finally, the relationship between NDVI and leaf area index was obtained. Using these regression curves for NDVI, the temporal and spatial distributions of LAI of paddy fields in the watershed could be estimated.

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The split-window algorithm produces a large error under humid conditions because of the dependence of each coefficient on the water vapor amount. The water vapor dependent (WVD) algorithm proposed by Francois et al. (1996) reduces such error by expressing each coefficient as a quadratic function of the water vapor amount. In the present article, the concept of the WVD algorithm is applied; to the multichannel (MC) algorithm which gives the surface temperature with the linear combination of the brightness temperatures measured at N channels; and to the extended multichannel (EMC) algorithm which gives the surface brightness temperature at each channel with the same combination. New algorithms named a MC/WVD and an EMC/WVD algorithms, as well as the WVD algorithm, express each coefficient as a quadratic function of the water vapor amount: in actual processing, a global data assimilation system, a sounder and the other data source provide the water vapor amount used in each coefficient. As the results of a simulation analysis with 964 atmospheric profiles, the rms errors of the new algorithms are showed to be 0.2-0.3 K smaller than those of the old algorithms under the conditions of sea observations. Particularly, the EMC/WVD algorithm is showed to be so robust against the emissivity uncertainty as to be applicable to land observations; the rms errors of the algorithm for AVHRR channel 4 and ASTER channel 12 are less than 1 K under the surface conditions including 97 terrestrial materials. And it is also showed that the land surface temperature (LST) offset (LST minus surface air temperature) is an important error factor for the new algorithms as well as the old algorithms.

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Quantitative evaluation of the surface roughness is a key to detect the surface soil moisture from the active microwave remote sensing. This study develops a new inversion algorithm of the surface roughness from a viewpoint of the hydrological application. Field experiments were conducted using C-band scatterometer system mounted on a car and the backscatter from the bare soil surface was observed with multiple angles at the same time. Measured surface roughness parameters vary and are very difficult to determine the representative roughness parameters even in a single field. The inversely obtained ones with the assumption that the soil moisture is known, does not agree with the measured ones, but the dependency of the backscattering coefficients computed from the theoretical model with the inversely obtained parameters, on both the incident angle and the soil moisture, shows good agreement with the observed ones. The result can be explained theoretically and terminology "surface roughness factor" is proposed to evaluate the surface roughnesses quantitatively.

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Direct solar radiation measurements were carried out on board in the Osaka Bay, on March 18, 1997, and at Sand Dune Agriculture Experiment Station, Ishikawa, on April 26, 1997. Based on those data, aerosol optical thickness at 550nm (τ_a550) and Angstrom exponent (α) were estimated. They were also retrieved from the degree of polarization at wavelengths of 443, 670, and 865 nm measured quasi-simultaneously by the POLDER sensor onboard the ADEOS satellite. The τ_a550 values retrieved from the degree of polarization were compared with those from the direct solar radiation measurements. The difference was about 0.03. The α values estimated from the degree of polarization were smaller than those from the direct solar radiation by 0.5 in typical cases.

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A method for cross calibration of different sensors onboard same satellites is proposed. BRDF of the standard plaque for the reference of the surface BRDF measurement is measured and corrected in the proposed cross calibration. Furthermore, difference of the spectral responses of the sensors as well as differnt Instantaneous Field of View: IFOV are taken into acount in the cross calibration. As the results from the experiment with ADEOS/AVNIR and OCTS, it is found that the proposed method shows a good cross calibration between both and works for evaluation of NEdR or Signal to Noise ratio: S/N. Through a comparison of the evaluated S/N with the previously reported S/N from NASDA, it is also found that the proposed method is appropriate for the S/N validation.

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Radiative transfer of the atmosphere-ocean system in the visible and near infrared region of spectrum is outlined. Ocean surface model and single scattering model are discussed in this chapter.

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