[Mid-infrared atmosphere radiation transfer analytic model and remote sensing images simulation].

In order to establish a complete set of simulation system for high-resolution mid-infrared remote sensing and provide a powerful reference for spacecraft design and related works, the importance of atmospheric radiative transfer simulation in this system was considered, and a reasonable and high precision imaging numerical simulation method was expected. Taking into account the characteristics of MIR, including scattering and thermal emission, terms of atmospheric radiative transfer were decomposed based on radiative transfer principle, and images of top of atmosphere (TOA) were simulated according to MODTRAN4 and look-up table method. Besides, adjacency effect caused by atmospheric scattering of neighboring pixels radiation was considered, and an extended point spread function in mid-infrared was coupled with analytical model of atmospheric radiative transfer to simulate TOA images. Finally, a preliminary test and simulation results show that the simulation model has better accuracy. If parameters of observation geometry and atmosphere were given and the land surface temperature/emissivity was determined, the calculation of pixel-level atmospheric radiative transfer was to be achieved.

[Multi-layer perceptron neural network based algorithm for simultaneous retrieving temperature and emissivity from hyperspectral FTIR data].

The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data: the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object's blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm(-1) in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simultaneous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to produce the test data, and the result of network test shows the MLP is robust. Meanwhile, the ISSTES algorithm was used to retrieve the temperature and emissivity form the test data. By comparing the results of MLP and ISSTES, we found the MLP can overcome the disadvantage of typical temperature and emisivity separation, although the rmse of derived emissivity using MLP is lower than the ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity of the typical temperature and emissivity separation.

[Variation trends of China terrestrial vegetation net primary productivity and its responses to climate factors in 1982-2000].

A new estimation model of vegetation net primary production (NPP) based on remote sensing data and climatic data was presented, with which, the NPP of China terrestrial vegetation in 1982-2000 was estimated, and the intra- and inter- annual variation patterns of the NPP and its responses to climate factors were studied. The results showed that there was an obvious seasonal regularity in the intra-annual variation of the NPP. In 1982-2000, all the terrestrial vegetation types presented an increasing annual NPP, with the greatest increment for deciduous needle leaf forests and the smallest one for grasses. Evergreen broadleaf forests had the largest inter-annual variation, while grasses had the smallest one. Comparing with temperature, precipitation played a stronger driving role in the intra-annual variation of the NPP, and the effects of precipitation and temperature were more obvious in North China than in South China. The driving roles of the climate factors varied with season and latitude.