Thermal infrared remote sensing multi-parameter AI integrated retrieval paradigm theory and technology
[Purpose]To improve the inversion accuracy of land-atmosphere energy exchange parameters such as Land Surface Temperature(LST),Land Surface Emissivity(LSE),Water Vapor Content(WVC),and Near-Surface Air Temperature(NSAT).[Method]This study proposed an AI-based thermal infrared remote sensing multi-parameter integrated inversion paradigm theory and technology.Through physical logical reasoning,it was demonstrated that a closed set of physical equations can be constructed between the input and output parameters of deep learning,which ensured the physical meaning and interpretability of AI remote sensing multi-parameter integrated inversion.This meant that the input variables could uniquely determine the output variables.For cases with strong correlations between input and output variables,the parameters could be directly inverted with high accuracy.For cases with weak correlations,incorporating strongly correlated prior knowledge could improve the inversion accuracy.[Result]Physical-logical reasoning indicated that thermal infrared remote sensing multi-parameter inversion required at least four thermal infrared bands to construct four radiation equation sets,which ensured the unambiguous determination of output variables by input variables.Based on the causal relationship between input and output variables,two parameter inversion techniques inversion were identified for integrated inversion:"direct synchronous retrieval"and"iterative retrieval".The integration of MODIS data from 5 bands(27,28,29,31 and 32)was applied for the inversion of the four parameters.The inversion results showed that the average theoretical error of LST was less than 0.5 K,the emissivity error was less than 0.008,the atmospheric water vapor error was less than 0.1 g/cm2,and the average error of the NSAT inversion application was less than 2.0 K.[Conclusion]The reasonable application of direct synchronous inversion and iterative inversion can maximize the inversion accuracy of multiple parameters,making the AI-based thermal infrared remote sensing multi-parameter integrated inversion a milestone in the history of thermal infrared remote sensing parameter inversion.
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