Achieving color consistency is essential for stitching large-area optical satellite imagery. The narrow swath width of individual images, combined with varying acquisition conditions, inherently introduces color differences. These manifest as marked disparities in brightness, color tone, and local contrast, degrading overall regional consistency. Existing methods primarily focus on correcting color inconsistencies between adjacent images, while often overlooking intra-image illumination non-uniformity, thereby propagating radiometric errors into optimization frameworks. Whisk-broom sensors, which can acquire imagery over a much wider swath width along the parallels, frequently exhibit substantial intra-image brightness variations, particularly in high-latitude regions. Compounded by frequent cloud cover and rapid temporal changes of features, extracting reliable color correspondences for optimization becomes intractable. To address these challenges, we propose a novel color correction framework that simultaneously considers intra-image illumination non-uniformity and radiometric variations caused by cloud prevalence and dynamic surface changes. First, a solar elevation angle map is extracted for down-sampled source image based on their geographic metadata and sensor geometry. An inverse compensation based on the normalized sine value of the solar elevation angle is then applied to mitigate brightness disparities caused by varying incident radiance. Second, to address atmospheric effects that vary with wavelength, such as differential absorption and scattering that cause color casts especially in low-illumination regions, a reference spectral channel is selected to guide the correction. Finally, we introduce a hybrid strategy for selecting reliable color correspondences in overlapping regions, using both grayscale and texture similarity under complex coverage conditions. Residual radiometric information is incorporated into a cost function, which jointly considers original color control and overall color balance to enhance the global consistency of the corrected mosaic. Extensive experiments conducted on imagery from the Wide Swath Imager (WSI) of DaQi-1 (DQ-1) and the Chinese Ocean Color and Temperature Scanner (COCTS) of HaiYang-1E (HY-1E) demonstrate that the proposed method effectively removes uneven illumination and color discrepancies. Compared to three state-of-the-art methods, our approach achieves superior performance in both visual quality and quantitative metrics.

The Sustainable Development Goals Science Satellite-1 (SDGSAT-1) is equipped with both low-light and thermal infrared imagers, which can detect infrared radiation and weak light information emitted by vessels. Compared to similar products, its spatial resolution has undergone a significant improvement. Existing remote sensing vessel detection methods only consider the use of single-source data for vessel detection, fail to effectively utilize the complementary information in multisource data, and have difficulty processing the complex vessel shapes of high-resolution satellite data, resulting in unsatisfactory detection results. In view of the sparse distribution of vessels at sea, this article proposes a new vessel target detection method using SDGSAT-1 high-resolution thermal infrared and low-light satellite data. Specifically, guided filtering is used to fuse thermal infrared and low-light data, the background and foreground are separated by partial sum of tensor core norms (PSTNN) model, and then the ordering points to identify the clustering structure (OPTICS) clustering algorithm and intercluster merging are used for detection. This article established a vessel dataset by choosing Shanghai Port, Hong Kong Port, and the Gulf of Mexico and then applied the algorithm. The detection accuracy and recall rate were found to be 97.67% and 97.80% respectively, which were significantly superior to other algorithms. This algorithm overcomes the complex background noise in the dataset and achieves good detection results.

This paper describes a novel on-orbit absolute radiometric calibration technique based on multiple ground observations for China HJ-2B thermal infrared sensor. Two types of natural surfaces were selected as references, i.e., the water bodies of Wuliangsuhai Lake and desert in Kubuqi. During the multiple ground observations, the 102F Fourier transform infrared spectrometer and SI-111 infrared radiometer were used to obtain the land surface emissivity and temperature. The atmospheric parameters were acquired from the ECMWF reanalysis data after temporal and spatial interpolation. With an atmospheric radiative transfer model (MODTRAN5.3), the measured radiance was calculated and the calibration coefficients were determined by combining satellite-observed digital number (DN). The results show that the proposed radiometric calibration accuracy is high in both two thermal infrared channels of HJ-2B, with top-of-atmosphere (TOA) radiance differences of 0.60% and 1.36%, and equivalent brightness temperature differences of 0.39K and 0.96K, respectively.

The Gaofen-5 02 (GF-5 02) satellite is equipped with two Earth observation sensors, the Advanced Hyperspectral Imager (AHSI) and the Full-Spectrum Spectral Imager (VIMI), which are used in environmental monitoring, natural resource exploration and so on. In this study, a spectral calibration method by matching the effective top-of-atmosphere (TOA) reflectance and atmospheric transmittance at the atmospheric absorption channels of 760 nm, 940 nm, 1140 nm, and 2060 nm was introduced. The spectral calibration method was validated with simulated hyperspectral data and the accuracy of center wavelength and full-width at half-maximum (FWHM) were generally better than 0.18 nm and 0.57 nm, respectively. Then, the method was applied to calibrate the center wavelength and FWHM of the GF-5 02/AHSI with a hyperspectral image collected on 7 May 2022. The spectral calibration results showed that the center wavelength and FWHM of GF-5 02/AHSI shifts with -0.375 nm and 0.35 nm, respectively. These results demonstrated a significant improvement in the surface reflectance spectrum of the inversions after spectral calibration at the Railroad Valley Playa (RVP) site.

Urban land surface temperature (LST) plays a crucial role in observing and comprehending energy exchange within urban environments. Urban geometric structure and material composition are critical parameters to characterize urban thermal radiation accurately. In this article, an urban thermal radiation analytical model based on the sky view factor (UTRAM-SVF) was developed by considering the multiple scattering within the urban canopy and the radiation composition of urban components. The cross-comparison of the proposed method was conducted in four ways: the discrete anisotropic radiation transfer (DART) model, urban effective emissivity model based on SVF (UEM-SVF), Landsat 8 Thermal Infrared Sensor (TIRS) data, and Airborne Hyperspectral Scanner (AHS) TIR data. Compared with DART, the results revealed that the root-mean-square error (RMSE) of radiance by the UTRAM-SVF model is 0.04 W/(m$^{2}\cdot $sr$\cdot \mu $m). Furthermore, two field applications were conducted using Landsat 8 TIRS data and AHS TIR data. The differences of at-sensor radiance from UTRAM-SVF model and Landsat 8 TIRS data vary from 0.05 to 0.3 W/(m$^{2}\cdot $sr$\cdot \mu $m), and the RMSE is 0.17 W/(m$^{2}\cdot $sr$\cdot \mu $m). The results of AHS TIR images on the UTRAM-SVF model present that the average biases of at-sensor radiance are 0.07 and 0.19 W/(m$^{2}\cdot $sr$\cdot \mu $m) for two TIR channels. The cross-comparison results show that the proposed method outperformed the UEM-SVF model in evaluating radiance over the complex and heterogenous urban areas (SVF <0.4). The UTRAM-SVF model can be used to monitor urban thermal radiation based on high-resolution TIR data and can further be helpful to retrieve high-resolution urban LST.

In this paper, we developed an algorithm to retrieve aerosols over land surfaces in China from directional multi-spectral polarized data of PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a LIDAR). On the assumption of spherical aerosol, after removing the polarized contribution of molecules and land surfaces from TOA (Top of Atmosphere) polarized reflectance of PARASOL, the aerosol scattered polarized radiance was obtained and interpolated into the LUT (Look Up Table), the AODs (Aerosol Optical Depth) are retrieved by best fitting method. The algorithm was validated with parasol Level 2 product. In addition, we analyzed some radiative and geometric parameters which influence the precision of the retrieval.