Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system c...Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system called near-equatorial satellite system to perform different kinds of remote sensing applications in equatorial regions. This study proposed a newly invented laboratory and fieldwork goniometer designed to simulate and capture intensity variation and measure the bidirectional spectral reflectance of earth surface. The proposed goniometer is a multi-purpose and multi-field device. It is able to simulate different satellite systems and measure the intensity variation and spectral reflectance of earth’s surface features with freely azimuth and zenith angles of sensors and illumination source in fieldwork and/or laboratory. However, the system of invention is focusing on specific satellite orbital to work with the parameters and properties of NEqO satellite system in order to obtain NEqO system imagery for performing different applications such as geometric correction, relative radiometric normalization and change detection for future work. The significant of this invention is that most of the invented goniometers of remote sensing are able to work just in field or just in laboratory and use, carry just optical sensor or hyperspectral sensor. Specifically, our invention can do all these functions that are not available in existing goniometers. The proposed device offers several advantages, namely, high measurement speed, flexibility, low cost, efficiency, and possible measurement depending on the free zenith/azimuth angles of sensors and illumination sources. The proposed goniometer includes ten parts, and two different sensors (optical and hyperspectral).展开更多
Carbon monoxide(CO)is one of the primary reactive trace gases in the Earth’s atmosphere and plays an important role in atmospheric chemistry.The Geostationary Interferometric Infrared Sounder(GIIRS)onboard the FY-4 s...Carbon monoxide(CO)is one of the primary reactive trace gases in the Earth’s atmosphere and plays an important role in atmospheric chemistry.The Geostationary Interferometric Infrared Sounder(GIIRS)onboard the FY-4 series satellites is currently the only geostationary hyperspectral thermal infrared sensor capable of monitoring the unprecedented hourly CO concentrations in East Asia during both daytime and nighttime.In this study,we presented a radiative transfer model-driven machine learning approach to quickly convert CO spectral features extracted from FY-4B/GIIRS into CO total columns.We built machine learning models for land and ocean regions separately from July 2022 to June 2023,and these models reproduced more than 97.77%(land)and 98.49%(ocean)of the CO column variance in the training set.We estimated the absolute uncertainty of the retrieved CO column based on error propagation theory and found that it is dominated by GIIRS measurement noise.We compared the machine learning retrieval results with optimal estimation and ground-based Fourier transform infrared measurements,and the results reveal the consistent spatial distribution and temporal variation across these different datasets.Our results confirm that the machine learning method has the potential to provide reliable CO products without the computationally intensive iterative process required by traditional retrieval methods.The diel cycle and monthly variation of CO over land and ocean demonstrate the value of GIIRS in monitoring the long-range transport of anthropogenic pollutants and biomass burning emissions.展开更多
文摘Researchers in the remote sensing field use different types of images from satellite systems and simulator devices, such as goniometers. However, no device can simulate the new generation of optical satellite system called near-equatorial satellite system to perform different kinds of remote sensing applications in equatorial regions. This study proposed a newly invented laboratory and fieldwork goniometer designed to simulate and capture intensity variation and measure the bidirectional spectral reflectance of earth surface. The proposed goniometer is a multi-purpose and multi-field device. It is able to simulate different satellite systems and measure the intensity variation and spectral reflectance of earth’s surface features with freely azimuth and zenith angles of sensors and illumination source in fieldwork and/or laboratory. However, the system of invention is focusing on specific satellite orbital to work with the parameters and properties of NEqO satellite system in order to obtain NEqO system imagery for performing different applications such as geometric correction, relative radiometric normalization and change detection for future work. The significant of this invention is that most of the invented goniometers of remote sensing are able to work just in field or just in laboratory and use, carry just optical sensor or hyperspectral sensor. Specifically, our invention can do all these functions that are not available in existing goniometers. The proposed device offers several advantages, namely, high measurement speed, flexibility, low cost, efficiency, and possible measurement depending on the free zenith/azimuth angles of sensors and illumination sources. The proposed goniometer includes ten parts, and two different sensors (optical and hyperspectral).
基金supported by the Hong Kong Research Grants Council(26304921,16306224)Hong Kong Envi-ronment and Conservation Fund(78-2019,13-2022)+1 种基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(grant XDB41000000)the USTC Research Funds of the Double First-Class Initiative.
文摘Carbon monoxide(CO)is one of the primary reactive trace gases in the Earth’s atmosphere and plays an important role in atmospheric chemistry.The Geostationary Interferometric Infrared Sounder(GIIRS)onboard the FY-4 series satellites is currently the only geostationary hyperspectral thermal infrared sensor capable of monitoring the unprecedented hourly CO concentrations in East Asia during both daytime and nighttime.In this study,we presented a radiative transfer model-driven machine learning approach to quickly convert CO spectral features extracted from FY-4B/GIIRS into CO total columns.We built machine learning models for land and ocean regions separately from July 2022 to June 2023,and these models reproduced more than 97.77%(land)and 98.49%(ocean)of the CO column variance in the training set.We estimated the absolute uncertainty of the retrieved CO column based on error propagation theory and found that it is dominated by GIIRS measurement noise.We compared the machine learning retrieval results with optimal estimation and ground-based Fourier transform infrared measurements,and the results reveal the consistent spatial distribution and temporal variation across these different datasets.Our results confirm that the machine learning method has the potential to provide reliable CO products without the computationally intensive iterative process required by traditional retrieval methods.The diel cycle and monthly variation of CO over land and ocean demonstrate the value of GIIRS in monitoring the long-range transport of anthropogenic pollutants and biomass burning emissions.
