A Multi-channel Thermal Infrared Remote Sensing Inversion Method for Surface Temperature

Abstract

The invention discloses a surface temperature multi-channel thermal infrared remote sensing inversion method. The method comprises the overall steps of acquiring the satellite observation brightness temperatures of N channels; arranging the satellite observation brightness temperatures of the N channels to obtain an N * (N-1) group of channel arrangement consisting of two different channel satellite observation brightness temperatures; selecting M groups from the N * (N-1) groups of channel arrangements, and obtaining the ground observation brightness temperature of M channels by applying an SW algorithm; collecting all available atmosphere information, and estimating atmosphere downlink radiance of the M channels; and based on the obtained ground observation brightness temperatures of theM channels and the estimated atmosphere downlink radiation brightness of the M channels, performing inversion by using a TES algorithm to obtain a final surface temperature and emissivity. Accordingto the surface temperature multi-channel thermal infrared remote sensing inversion method without any auxiliary information provided by the invention, the surface temperature and the emissivity can beobtained through inversion from multi-channel thermal infrared satellite remote sensing observation data at the same time, and the limitation of priori knowledge precision is avoided.

Description

technical field [0001] The invention relates to an inversion method, in particular to a multi-channel thermal infrared remote sensing inversion method for surface temperature, belonging to the technical field of remote sensing inversion. Background technique [0002] Surface temperature is an important physical quantity that describes regional and global hydrothermal changes, and is also a key factor driving energy exchange between land and the atmosphere. Accurate surface temperature products can not only reflect the spatiotemporal dynamic change information of surface radiation under the state of energy balance, but also contribute to the estimation of hydrothermal parameters such as soil moisture and atmospheric temperature. Therefore, surface temperature has become an important input parameter in many research fields such as hydrology, meteorology, and climatology. Thermal infrared satellite remote sensing technology provides a means of obtaining large-scale surface tem...

AI Technical Summary

Problems solved by technology

Among them, the single-channel method assumes that the accurate atmospheric profile and surface emissivity information are known; the day / night method does not need to know the surface emissivity information, but needs the shape information of the atmospheric profile, and assumes that the surface emissivity is in the daytime In addition, the accuracy of this method is also affected by the day / night image registration error, and the equations need to be solved. The method is more complicated and not easy to promote and use; the split window method does not need to know any atmospheric information , it can use two adjacent channels to remove the influence of the atmosphere, but the retrieval accuracy of the surface temperature is greatly affected by the emissivity accuracy of the two input channels; the temperature / emissivity separation method can accurately calculate the surface temperature and emissivity separation, but this method requires precise atmospheric correction to ensure high accuracy

Obviously, these methods have their own advantages and disadvantages, and all of them require certain prior knowledge to obtain accurate land surface temperature information.

However, in the actual application environment, since the available prior knowledge such as the atmospheric profile usually comes from products based on ground observation data or other satellite observation data, there is not only a spatial resolution gap between the thermal infrared satellite observation data In most cases, there is also a mismatch in the acquisition time, and these products themselves also contain certain uncertainties, which will seriously affect the accuracy of prior knowledge, and then affect the reflection of surface temperature and emissivity. Accuracy