The level-1 (L1) radiance spectra from the first Geostationary Interferometric InfraRed Sounder (GIIRS) have been publicly available since January 2019. On account of the inherent observation characteristics, operation modes, and capabilities, a complete spectrum calibration method of GIIRS/L1 products is originally proposed to form the latest version (V3) algorithm for implementation. Particularly, four targeted improvements in three aspects are independently established: subsample location alignment to yield integrated interferograms in both forward and backward directions with almost zero phase, rough spectral scale unification as well as accurate spectral scale correction to resolve spectral non-uniformity due to a seriously asymmetric configuration of focal plane array, and an additional double-reflected compensation to mitigate the influence of non-ideal onboard blackbody reference upon radiometric accuracy. Preliminary assessments from both domestic and international sources indicate that the spectral and radiometric accuracies of the measured spectra from the latest GIIRS/L1 V3 algorithm show a well-behaved performance in both longwave (LW) and midwave (MW) bands, that is, lower than 10 ppm of spectral scale errors, which is of sufficient accuracy for numerical weather prediction use, and around 1 K for most uncontaminated channels within the LW band. However, non-linearity correction of interferograms and spectral quality improvements, especially for the MW band, should be developed further. In general, a feasible solution of spectrum calibration for a hyperspectral sounder on geostationary platform is provided in detail for reference, which is expected to benefit users of GIIRS data as well as designers responsible for L1 data processing of other similar sensors.

In our study, a retrieval method of temperature profiles is proposed which combines an improved one-dimensional variational algorithm (1D-Var) and artificial neural network algorithm (ANN), using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) infrared hyperspectral data. First, according to the characteristics of the FY-4A/GIIRS observation data using the conventional 1D-Var, we introduced channel blacklists and discarded the channels that have a large negative impact on retrieval, then used the information capacity method for channel selection and introduced a neural network to correct the satellite observation data. The improved 1D-Var effectively used the observation information of 1415 channels, reducing the impact of the error of the satellite observation and radiative transfer model, and realizing the improvement of retrieval accuracy. We subsequently used the improved 1D-Var and ANN algorithms to retrieve the temperature profiles, respectively, from the GIIRS data. The results showed that the accuracy when using ANN is better than using improved 1D-Var in situations where the pressure ranges from 800 hPa to 1000 hPa. Therefore, we combined the improved 1D-Var and ANN method to retrieve temperature profiles for different pressure levels, calculating the error by taking sounding data published by the University of Wyoming as the true values. The results show that the average error of the retrieved temperature profiles is smaller than 2 K when using our method, this method makes the accuracy of the retrieved temperature profiles superior to the accuracy of the GIIRS products from 10 hPa to 575 hPa. All in all, through the combination of the physical retrieval method and the machine learning retrieval method, this paper can certainly provide a reference for improving the accuracy of products.

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods.