The goal of this paper is to introduce how to make use of the artificial neural network technique
to develop a new method which can fast recognize atmospheric profiles' characters from hyperspectral
infrared thermal remote sensing. This technique would accelerate the calculation speed of hyperspectral
infrared atmospheric radiative transfer model (RTM). As the launch of hyperspectral infrared sensors such
as Infrared Atmospheric Sounding Interferometer (IASI), it becomes possible for people to take advantage
of the hyperspectral data which contains abundance of precise spectral information, to add constraint
conditions for the researches of some physical models. But in practice, normal hyperspectral infrared
atmospheric RTM are relatively complex and time costing. The calculation speed of these models is not fast
enough to make these models to respond to the variety of atmospheric radiative, or the bright temperature
timely. Therefore, the practical and effective physical models and research methods, such as the practical
surface temperate inversion model, couldn't be founded relay on these transfer models. In order to solve
this problem, institutions and researchers around the world have tried some methods to develop the fast
calculation of atmospheric RTM. But these methods still have problems on speed, accuracy and the
applicability for certain sensors.
Land surface temperature (LST) is an important measurement for
estimating equilibrium of income and expense of land surface energy. It is also a key input parameter in many geographic models. Therefore, research on land surface temperature retrieval has close relation with thermal infrared-related study, such as
hydrology, ecology, climatology, environment and other fields.
Made in China, the Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting is an advanced satellite constellation (composed of satellite HJ-1A, 1B and 1C) designed for environment and disaster monitoring and mitigation. Whether the sensor data can reach the designed specifications and meet
the demands of application? It is necessary to carry out relative research before the launch of a new satellite. There is an infrared sensor in HJ-1B. Our work has been done before the launch of HJ-1B. This paper focuses on the land surface temperature
retrieval study based on HJ-1B thermal infrared data, which is significant for its potential assessment and effective application in environment monitoring and disaster preventing and management.
According to the characteristics of HJ-1B thermal infrared sensor, a method of using middle infrared (MIR) band and thermal infrared (TIR) band of HJ-1B is put forward in this paper. The spectral response function of bands, standard atmospheric profiles data and radiation transfer simulating software-MODTRAN are used to get
simulated HJ-1B infrared data. And finally, the algorithm accuracy is estimated by comparing the retrieval value and true value of temperature. And the sensitive analyzing of retrieval algorithm is made through some main parameters.
It can be know from our research that the proposed land surface temperature retrieving algorithm for HJ-1B infrared data has a considerable precision, the RMSE value range is 0.01K~2.08K. The RMSE increases with the increase of view zenith
angle. The variation range of temperature retrieval RMSE due to view zenith angle is 0.1K~0.2K. The emissivity and water vapor content influence the land surface temperature retrieving result obviously, and the influence of instrument noise on retrieving result is little and can be ignored.
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