Research On The Infrared Radiation Characteristics Acquisition Method Of Ground Features Based On Stratospheric Sensors

The amount of infrared radiation energy emitted by a ground target is closely related to its surface temperature and emissivity.Thermal infrared remote sensing data record the surface radiation information,from which surface temperature and emissivity distribution can be obtained using infrared remote sensing technology.This will help meet the needs of numerous studies and applications.With the growth of demand for remote sensing applications,remote sensing data should have a higher temporal and spatial resolution.New remote sensing platforms and sensors have been developed rapidly.Among them,the stratospheric platform has the advantages of long residence time and high spatial resolution in Earth observation and has great application prospects.After a long period of development,satellite-based thermal infrared remote sensing methods have become relatively mature,but infrared multispectral observations and remote sensing applications at stratospheric altitudes have not yet been reported.If satellite-based remote sensing methods are used at stratospheric altitudes,the performance and influence factors of the algorithm are unclear.For the application of infrared remote sensing in the stratosphere,this article investigates the method for acquiring the land surface temperature and emissivity.The main research works are as follows:Firstly,simulation experiments of the stratospheric split-window algorithm were conducted.Stratospheric observation data sets were established using radiative transfer models and the algorithm coefficients were also updated.The LST simulation shows atheoretical error of0.185 K.Sensitivity analysis of the influencing factors show that the surface emissivity and the NETD of the instrument are the main influencing factors.The algorithm can achieve an accuracy better than 2 K,if the uncertainty of emissivity and NETD can be controlled within 1% and 0.4K,respectively,with spectral response drift within ±3 nm.Secondly,the surface temperature inversion software was built based on the split-window algorithm.The settings of the required parameters were integrated in the interface,so that the algorithm coefficients could be updated conveniently by modifying the relevant parameters.The software was tested using ASTER data,and the obtained temperature inversion error was 1.03 K,which proves that the inversion accuracy of the algorithm on the measured data is consistent with the simulation results.Finally,for the problem that the split-window algorithm is difficult to obtain the emissivity under high spatial resolution conditions,the stratospheric remote sensing simulation experiment is conducted using the surface temperature and emissivity separation algorithm.The results show that under typical surfaces,the theoretical error of land surface temperature is 0.66 K.The emissivity errors of the split-window channels are 0.018 and0.015,respectively.If the emissivity at this accuracy is used for the split-window algorithm,the resulting LST inversion error should be within 1.5K.The sensitivity analysis results show that the algorithm is more sensitive to temperature and humidity profile noise,this method requires accurate atmospheric correction.The algorithm inversion accuracy is lower for high emissivity surfaces such as vegetation and water bodies,and the method is more suitable for substances with a wide range of emissivity spectral variations.This paper presents a preliminary study and simulation experiments on the method for acquiring the radiative properties of features in stratospheric observation scenario,which can provide a reference for the design and development of temperature inversion methods for stratospheric infrared sensors.