Remote measurements of Earth’s surface from ground, airborne, and spaceborne instruments show that its albedo is highly variable and is sensitive to solar zenith angle（SZA） and atmospheric opacity. Using a validate...Remote measurements of Earth’s surface from ground, airborne, and spaceborne instruments show that its albedo is highly variable and is sensitive to solar zenith angle（SZA） and atmospheric opacity. Using a validated radiative transfer calculating toolbox, DISORT and a bidirectional reflectance distribution function library, AMBRALS, a land surface albedo（LSA） lookup table（LUT） is produced with respect to SZA and aerosol optical depth. With the LUT, spectral and broadband LSA can be obtained at any given illumination geometries and atmospheric conditions. It provides a fast and accurate way to simulate surface reflectance over large temporal and spatial scales for climate study.展开更多
The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which ...The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR （visible and near infrared） data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM （genetic algorithms ＆ self-organizing feature maps）-ANN （artificial neural net-work）. Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.展开更多
基金supported by the National Natural Science Foundation of China(No.41305019)the Anhui Provincial Natural Science Foundation(No.1308085QD70)
文摘Remote measurements of Earth’s surface from ground, airborne, and spaceborne instruments show that its albedo is highly variable and is sensitive to solar zenith angle（SZA） and atmospheric opacity. Using a validated radiative transfer calculating toolbox, DISORT and a bidirectional reflectance distribution function library, AMBRALS, a land surface albedo（LSA） lookup table（LUT） is produced with respect to SZA and aerosol optical depth. With the LUT, spectral and broadband LSA can be obtained at any given illumination geometries and atmospheric conditions. It provides a fast and accurate way to simulate surface reflectance over large temporal and spatial scales for climate study.
基金Supported by the Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping（No.200815）, the Natural Science Foundation of China （NSFC 40371087, 40701119）, the Major State Basic Research Development Program of China （973 Program） （No. 2007CB714401）, the National High Technology Research and Development Program of China （863 Program） （No. 2007AA10Z201 ）.
文摘The multi-source data fusion methods are rarely involved in VNIR and thermal infrared remote sensing at present. Therefore, the potential advantages of the two kinds of data have not yet been adequately tapped, which results in low calculation precision of parameters related with land surface temperature. A new fusion method is put forward where the characteristics of the high spatial resolution of VNIR （visible and near infrared） data and the high temporal resolution of thermal infrared data are fully explored in this paper. Non-linear fusion is implemented to obtain the land surface temperature in high spatial resolution and the high temporal resolution between the land surface parameters estimated from VNIR data and the thermal infrared data by means of GA-SOFM （genetic algorithms ＆ self-organizing feature maps）-ANN （artificial neural net-work）. Finally, the method is verified by ASTER satellite data. The result shows that the method is simple and convenient and can rapidly capture land surface temperature distribution of higher resolution with high precision.
