Hyperspectral imaging technology enables the simultaneous acquisition of geometric, radiometric, and spectral information about targets, making it an important tool for Earth-based remote sensing and target detection. However, owing to the inherent nonuniformity of detector responses and the influences of the imaging chain, the image data captured by push-broom hyperspectral imagers inevitably exhibit nonuniformity. This nonuniformity includes not only discrepancies between different pixels along the spatial dimension but also varying response deviations among different pixels across the spectral dimension. If these discrepancies are not effectively corrected, they can significantly impact the quantitative applications of hyperspectral image data. To address the limitations of existing methods—which often struggle to fully utilize spatial and spectral information, require manual parameter adjustments for each hyperspectral image, involve cumbersome coefficient calculations, and have limited generalization capabilities—this paper presents a scene-based nonuniformity correction method for push-broom hyperspectral imagers. The method can adaptively identify uniform regions in different scenes to derive correction coefficients and can utilize spectral-dimensional information for constraints, effectively ensuring satisfactory calibration results in both spatial and spectral dimensions while enabling automatic execution on each scene image. The proposed method shows strong adaptability to different hyperspectral imagers, scenes, and spectral bands, with a quality improvement factor of 42.55 and an average relative deviation of less than 1.04%. It preserves spatial detail without significant loss, maintains the integrity of the spectral curve, and requires less run time. This work provides a solid technical foundation for the real-time processing of terrestrial and on-orbit hyperspectral images.

To improve the assimilation accuracy of satellite microwave observations, it is necessary to conduct precipitation detection before assimilation and exclude the data contaminated by the scattering of hydromorphic particles. The induction of microwave scattering is closely related to the effective radius of hydromorphic particles, and the motion state of hydromorphic particles at different scales can directly affect the internal dynamic structure of clouds, which will lead to precipitation. To further analyze and validate the impact of microwave scattering in a precipitation environment on the assimilation of satellite microwave soundings within the temperature-sensitive band, this paper utilized the on-orbit observations of Microwave Temperature Sounder -III (MWTS-III) and Medium Resolution Spectral Imager (MERSI) onboard the Fengyun-3E (FY-3E) satellite to achieve multispectral fusion observation and precipitation detection over oceans, combined with the vertical dynamic characteristics of global numerical forecast in the cloud environment of China Meteorological Administration (CMA) Global Forecast System (CMA-GFS). By conducting the dynamic analysis of the hydrometeors in the cloud, the maximum possible effective particle radius of hydromorphic particles under the no-phase-transition condition and internal vertical motion is obtained. The consistency of the cloud liquid water path (CLWP) retrieved by MWTS-III and multispectral fusion precipitation detection is evaluated subsequently. The results show that the distribution of hydrometeors in the CMA-GFS forecast is broadly in line with the CLWP distribution obtained from the MWTS-III retrieval, regardless of Mie scattering or Rayleigh scattering. However, compared with Mie scattering, the weak convection of CMA-GFS shows better adaptability in the Rayleigh environment.

This study utilizes the 89 GHz dual-polarization channel of the Ground-Based Multi-Frequency and Dual-Polarization Microwave Radiometer (GMD-MR) to overcome the challenges posed by the insensitivity of low-frequency microwave channels to cloud ice particles. By integrating data from Micro-Rain Radars (MRRs), we developed and implemented advanced convolutional and deep learning models. These models leverage brightness temperature, polarization differences, and constraints from cloud and precipitation data to quantitatively estimate cloud ice content, cloud water content profiles, rainwater content profiles, and precipitation rates, achieving correlation coefficients of 0.6, 0.7, 0.84, and 0.84, respectively. Our analysis of the spatiotemporal dynamics of ice water, cloud liquid water, and rain liquid water paths during precipitation events highlights their predictive value for precipitation occurrence. With a prediction accuracy of 97% and a temporal correlation coefficient of 0.9, our findings affirm the effectiveness of ground-based radiometers and micro-rain radars in precipitation detection. This study demonstrates the capability of multi-instrument joint retrieval for various meteorological parameters, highlighting the significant potential of multi-source microwave data fusion in quantitative precipitation estimation. It establishes and reinforces the foundation for future investigations into the physical processes of precipitation evolution.