High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial aut...High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial automation.As demands for accuracy,miniaturization,and reliability continue to grow,developing novel sensor architectures and functional materials has become essential to achieving enhanced performance under extreme or complex conditions.展开更多
Hyper-spectral remote sensing may provide an effective solution to retrieve the methane （CH4） concentra- tion in an atmospheric column. As a result of exploring the absorptive characteristics of CH4, an appropriate ...Hyper-spectral remote sensing may provide an effective solution to retrieve the methane （CH4） concentra- tion in an atmospheric column. As a result of exploring the absorptive characteristics of CH4, an appropriate band is selected from hyperspectral data for the detection of its column concentration with high precision. Following the most recent inversion theory and methods, the line-by-line radiative transfer model （LBLRTM） is employed to forward model the impact of four sensitive factors on inversion precision, including CH4 initial profile, tempera- ture, overlapping gases, and surface albedo. The results indicate that the four optimized factors could improve the inversion precision of atmospheric CH4 column concen- tration.展开更多
文摘High-sensitivity sensors represent a critical frontier in modern sensing technology,driving innovations across fields such as biomedical monitoring,precision instrumentation,environmental detection,and indus-trial automation.As demands for accuracy,miniaturization,and reliability continue to grow,developing novel sensor architectures and functional materials has become essential to achieving enhanced performance under extreme or complex conditions.
文摘Hyper-spectral remote sensing may provide an effective solution to retrieve the methane （CH4） concentra- tion in an atmospheric column. As a result of exploring the absorptive characteristics of CH4, an appropriate band is selected from hyperspectral data for the detection of its column concentration with high precision. Following the most recent inversion theory and methods, the line-by-line radiative transfer model （LBLRTM） is employed to forward model the impact of four sensitive factors on inversion precision, including CH4 initial profile, tempera- ture, overlapping gases, and surface albedo. The results indicate that the four optimized factors could improve the inversion precision of atmospheric CH4 column concen- tration.
