Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low inci...Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low incident angles(2.5°–8°),so its swath is much wider and its spatial resolution is much higher than the previous altimeters.This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station.Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles,a neural network is used to retrieve the ocean surface wind speed in this study.The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed,buoy wind speed,and in-situ ship measurements.The results show that the retrieved wind speed bias is about–0.21 m/s,and the root-mean-square(RMS)error is about 1.85 m/s.The wind speed accuracy of IALT meets the performance requirement.展开更多
With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simula...With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simulation methods have matured in static magnetic dipole simulations,but there is still significant room for optimization in the simulation and computation of low-frequency magnetic dipole models.This study employs the Gauss-Newton method and Fourier transform techniques for modeling and simulating low-frequency magnetic dipoles.Compared to the traditional particle swarm optimization(PSO)algorithm,this method achieves significant improvements,with errors reaching the order of10^(-13)%under noise-free conditions and maintaining an error level of less than 0.5%under 10%noise.Additionally,the use of Fourier transform and the Gauss-Newton method enables high-precision magnetic field frequency identification and rapid computation of the dipole position and magnetic moment,greatly enhancing the computational efficiency and accuracy of the model.展开更多
Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecti...Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks,the influence of bandwidth on the inversion accuracy are ignored.In this study,we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City,Xinjiang Uygur Autonomous Region,China and measured the ground spectra of these samples.The original spectra were resampled with different bandwidths.A Partial Least Squares Regression(PLSR)model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored.According to the results,the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm,with the model determination coefficient(R^(2))of 0.5907.The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5-80 nm,but the accuracy decreases significantly at 85 nm bandwidth(R^(2)=0.5473),and the accuracy gradually decreased at bandwidths beyond 85 nm.Hence,bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model.This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.展开更多
基金The National Key Research and Development Program of China under contract No.2016YFC1401002the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0302the National Natural Science Foundation of China under contract No.41606202
文摘Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low incident angles(2.5°–8°),so its swath is much wider and its spatial resolution is much higher than the previous altimeters.This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station.Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles,a neural network is used to retrieve the ocean surface wind speed in this study.The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed,buoy wind speed,and in-situ ship measurements.The results show that the retrieved wind speed bias is about–0.21 m/s,and the root-mean-square(RMS)error is about 1.85 m/s.The wind speed accuracy of IALT meets the performance requirement.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC2206003)。
文摘With the increasing accuracy requirements of satellite magnetic detection missions,reducing low-frequency noise has become a key focus of satellite magnetic cleanliness technology.Traditional satellite magnetic simulation methods have matured in static magnetic dipole simulations,but there is still significant room for optimization in the simulation and computation of low-frequency magnetic dipole models.This study employs the Gauss-Newton method and Fourier transform techniques for modeling and simulating low-frequency magnetic dipoles.Compared to the traditional particle swarm optimization(PSO)algorithm,this method achieves significant improvements,with errors reaching the order of10^(-13)%under noise-free conditions and maintaining an error level of less than 0.5%under 10%noise.Additionally,the use of Fourier transform and the Gauss-Newton method enables high-precision magnetic field frequency identification and rapid computation of the dipole position and magnetic moment,greatly enhancing the computational efficiency and accuracy of the model.
基金supported by the Science and Technology Major Project of Xinjiang Uygur Autonomous Region,China(2021A03001-3)the Key Area Deployment Project of the Chinese Academy of Sciences(ZDRW-ZS-2020-4-30)the National Natural Science Foundation of China(U1803117).
文摘Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands,high resolution,and abundant information.Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks,the influence of bandwidth on the inversion accuracy are ignored.In this study,we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City,Xinjiang Uygur Autonomous Region,China and measured the ground spectra of these samples.The original spectra were resampled with different bandwidths.A Partial Least Squares Regression(PLSR)model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored.According to the results,the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm,with the model determination coefficient(R^(2))of 0.5907.The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5-80 nm,but the accuracy decreases significantly at 85 nm bandwidth(R^(2)=0.5473),and the accuracy gradually decreased at bandwidths beyond 85 nm.Hence,bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model.This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.
