When monitoring target areas covered with vegetation from a satellite, it is very useful to estimate the vegetation index using the surface anisotropic reflectance, which is dependent on both solar and viewing geometries, from satellite data. In this study, the algorithm for estimating optical properties of atmospheric aerosols such as the optical thickness (τ), the refractive index (Nr), the mixing ratio of small particles in the bimodal log-normal distribution function (C) and the bidirectional reflectance (R) from only the radiance and polarization at the 865nm channel received by the PARASOL/POLDER is described. Parameters of the bimodal log-normal distribution function: mean radius, r1, standard deviation, σ1, of fine aerosols, and r2, σ2 of coarse aerosols were fixed, and these values were estimated from monthly averaged size distribution at AERONET sites managed by NASA near the target area. Moreover, it is assumed that the contribution of the surface reflectance with directional anisotropy to the polarized radiance received by the satellite is small because it is shown from our ground-based polarization measurements of light ray reflected by the grassland that degrees of polarization of the reflected light by the grassland are very low values at the 865nm channel. First aerosol properties were estimated from only the polarized radiance and then the bidirectional reflectance given by the Ross-Li BRDF model was estimated from only the total radiance at target areas in PARASOL/POLDER data over the Japanese islands taken on April 28, 2012 and April 25, 2010. The estimated optical thickness of aerosols was checked with those given in AERONET sites and the estimated parameters of BRDF were compared with those of vegetation measured from the radio-controlled helicopter. Consequently, it is shown that the algorithm described in the present study provides reasonable values for aerosol properties and surface bidirectional reflectance.
A method for identifying the released region of Asian dust using the wind velocity near the surface and the long-range
inverse transport model that traces the wind field in the backward direction from positions where Asian dust was
observed is described. The spatio-temporal concentration distribution of dust clouds over the East Asia was computed in
the case that Asian dust clouds were observed in Japan from April 1 to April 2, 2007. In this case, the released mass flux
of Asian dust in source regions was determined such that the simulated concentration of Asian dust almost corresponds
to the concentration of the suspended particulate matter (SPM) measured at various places in Japan. In order to better
understand through which paths sand dust particles are transported to Japan, the time variation of the concentration
distribution of Asian dust clouds and the wind field at 950hPa from 22:00 on March 30 to 15:00 on April 4, 2007was
animated every one hour in the Google Earth.
The optical parameters of the Asian dust over the desert in China and the surface reflectance were estimated simultaneously, using reflectances and polarizations at the wavelength 670 nm in several points selected from ADEOS/POLDER data taken on April 10, 1997. It was assumed that the land surface is the diffuse reflector and the number size distribution of the Asian dust is represented by the Junge power-law. The optical parameters of the dust layer, such as the optical thickness, refractive index of the dust aerosol and index of the Junge power-law, and the ground reflectance were determined such that the sum of the relative absolute error between the observed reflectance and polarization and those obtained from the simulation of radiative transfer in the atmosphere-ground system is minimum. As a result, it was found that the refractive index of Asian dust is 1.4 to 1.5, the optical thickness of dust layer is 0.1 to 0.2, the index of Junge power-law is -5.3 to -6.0 and the surface reflectance is 0.3 at the wavelength 670 nm.
Polarization measurements of the sky radiation by PSR-1000 at a ground station when the yellow sand dust (Asian dust) came flying to the Kanazawa city, Japan from the deserts in the northern part of China were made. The PSR-1000 is the multi-spectral polarimeter and has the same wavelength regions (443nm, 490nm, 565nm, 670nm, 765nm and 865nm) as the ADEOS/POLDER sensor. First of all, the wavelength dependency of degrees of polarization is examined and it is shown that the wavelength dependency for polarization will be due to the size distribution of dust particles transported from the deserts in China. Next, a new method for estimating optical properties, such as the optical thickness, the number size distribution and the refractive index, of yellow sand dust and the background reflectance from degrees of polarization measured by PSR-1000 is described. Finally, this method was applied to the polarization data acquired on March 13, 2000 and April 14, 2002.
The optical parameters of the hazy yellow sand dust over the Takula Makan desert in China and the surface reflectance were estimated, using multi-viewing reflectances and polarizations at the wavelength 443nm in several points selected from ADEOS/POLDER data taken on April 10, 1997. In this case, it was assumed that the land surface is the diffuse reflector and the number size distribution of yellow sands is represented by the Junge power-law. The optical parameters of dust clouds and the ground reflectance were determined such that the relative absolute error between the observed reflectance and polarization and those obtained from the simulation of radiative transfer in the atmosphere-ground system is minimum. As a result, it was found that the refractive index of yellow sand dust is 1.36 to 1.45, the optical thickness of dust clouds is 0.44 to 0.54, the index of Junge power-law is -4.2 to -5.0 and the surface reflectance is 0.1 to 0.17.
The objective of this study is to find areas where landslides may occur in the near future by using satellite remote sensing data and thematic-map data related to landslides. We used Landsat TM data, geological maps, and inclination angles to predict landslide areas. As a result, we conclude that NVI data which was calculated from satellite data, geological types, and inclination angles are important factors to extract areas where landslides may occur.
An inference of landslide areas using digital elevation data and Landsat TM band 6 data has been performed on the basis of the assumption that the occurrence of landslides is closely related to the amount of underground-water and the topographic features of watersheds. It is shown that it is possible to distinguish between dangerous landslide areas and non-landslide areas by using spatial features of watersheds, such as the area, mean slop and shape factor, and the ground surface temperature obtained from Landsat TM data.