| Land Surface Temperature(LST)is one of the key physical quantities to describe the energy exchange between the earth surface and the atmosphere.It reflects the heat transfer process between the earth surface and the atmosphere,as well as the complex effects of the atmosphere on the surface heat absorption,radiation,transmission and scattering.As a key parameter of many disciplines,land surface temperature is the basis of many disciplines and has wide application value.With the continuous development of science and technology and the rapid progress of satellite technology,satellite remote sensing technology can be used to obtain high-precision and high-spatio-temporal resolution land surface temperature data,which provides a reliable method and means for the study of large-scale land surface temperature and long-term time series spatio-temporal changes.The ZY-1 02E satellite was successfully launched at 11:11 am Beijing Time on December 26,2021.The satellite is equipped with a series of high-performance sensors,including high-resolution visible near-infrared sensors,hyperspectral sensors and the addition of long-wave infrared sensors.The comprehensive use of these sensors will greatly improve the remote sensing capability of the satellite.In particular,the thermal infrared remote sensing image resolution of the satellite is up to 16 meters,which means that the satellite can more accurately capture surface temperature changes and provide more refined data support for the fields of weather prediction,resource survey and environmental monitoring.At present,there are few relevant researches on the thermal infrared remote sensing data of Yuanzi-1 02E satellite.The main contents of this thesis are as follows:1.In this thesis,based on the remote sensing data of Yuanzi-1 02E satellite,high precision inversion of surface specific emissivity is carried out,mainly using two algorithms.The first is the traditional NDVI threshold algorithm,which estimates parameters such as NDVI value and vegetation coverage by using visible and near infrared band image data,so as to invert specific surface emissivity.The second algorithm is to establish the conversion relationship of specific surface emissivity based on the correlation between Resource No.1 thermal infrared channel and ASTER thermal infrared channel,and at the same time to correct the specific surface emissivity value considering the change of phenology.In order to verify the accuracy of these two algorithms,the surface specific radiation results obtained by inversion of the two algorithms were cross-verified with Landsat9 surface specific emissivity products.The results show that the ASTER GED based surface-ratio emissivity algorithm has higher overall accuracy than the NDVI threshold based algorithm.2.This thesis aims to achieve high-precision land surface temperature inversion based on the wide band characteristics of the thermal infrared channel of Resource-102E satellite.To this end,two single-channel algorithms are optimized in this thesis.The first algorithm is an improved universal single-channel algorithm.By analyzing the spectral response function of the thermal infrared channel of Resource-1 02E satellite and the atmospheric profile database,we optimized the universal single-channel algorithm model,updated the model parameters,and obtained the algorithm model suitable for Resource-1 02E satellite.The second algorithm is the radiative transfer equation method.We used the reanalysis data from the European Centre for Medium Range Weather Forecasts,combined with the atmospheric radiative transfer model MODTRAN to obtain atmospheric parameters,and obtained the atmospheric parameters at the time of satellite transit through bilinear interpolation,so as to perform surface temperature inversion.In order to verify the accuracy of the two algorithms,we verify the method of field measurement of surface temperature.The results show that the accuracy of both algorithms is within 2K,and the inversion accuracy of the radiation transmission algorithm is higher than that of the improved universal single channel algorithm.3.In view of the demand for industrial capacity monitoring,this thesis builds an industrial capacity monitoring model based on thermal infrared remote sensing surface temperature inversion algorithm.In order to verify the validity of the model,four thermal power enterprises in the study area were selected to perform surface temperature inversion.This model is used to monitor the variation of the production capacity of the four thermal power enterprises on a monthly basis,and the linear regression analysis of the production capacity of the enterprises and the corresponding thermal radiation intensity is carried out.The results show that there is a good correlation between the heat radiation intensity and the production capacity of the enterprise,and the R~2value is 0.89.The results show that the industrial capacity monitoring model we built is feasible in practice. |
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