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.

Surface temperature (ST) is a vital physical parameter influencing surface-atmosphere interactions. This study presents an uncertainty-based validation approach applied to Sentinel-3/SLSTR land surface temperature (LST) and sea surface temperature (SST) products.In situmeasurements were obtained from various sites in China, namely, the Dunhuang Gobi site (DHGS), Huailai Guanting Reservoir site (HGRS), Wuliangsuhai Lake site (WLSLS) and Yantai Ocean site (YTOS). The spatial representativeness ofin situmeasurements at each site was assessed using available clear-sky and high-quality ASTER LST products from April 2000 to June 2023. The four sites exhibited high spatial homogeneity, demonstrating suitability for validating STs. Therefore,in situmeasurements from these homogeneous sites were used to validate the Sentinel-3/SLSTR ST products during the daytime and nighttime using a temperature-based method. The results showed that the root mean square error (RMSE) values are lower than 1.6 K, except for those at DHGS. Furthermore, since ground-based ST validation is affected by the coupled effects of surface and atmospheric characteristics, the validation results are different under different atmospheric and surface conditions. Consequently, assessing the consistency among multiple validation results becomes challenging. To address this issue, by assuming the independence of the validation samples, we propose a method for obtaining the key comparison reference value (KCRV) from multiple validation results based on Sentinel-3/SLSTR ST products. The KCRV is close to the ‘true’ value, indicating the high quality of the validation results. For the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR LST products, the KCRVs are 1.91 K and 1.71 K, respectively, with corresponding uncertainties of 0.08 K and 0.08 K, respectively. Similarly, for the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR SST products, the KCRVs are 0.78 K and 0.71 K, respectively, with uncertainties of 0.08 K and 0.07 K, respectively.

Knowing how much measurement noise is in a signal is critical for evaluating the overall performance of a satellite observation. We developed a triple collocation observation (TCO) algorithm for estimating measurement noise by collocation comparing the local deviations of three satellite data sets. When we evaluated our algorithm with a synthetic data set, the results showed that the algorithm effectively derived measurement noise from satellite signals despite the many intermission signal differences among the satellites. The TCO algorithm produced <6.66% uncertainty in the measurement noise estimates that we derived from the synthetic data set. In addition, to maximally isolate measurement noise from ocean color images, we developed a set of data quality control criteria to apply when identifying synchronous pixel pairs. Using images from the Medium Resolution Spectral Imager II (MERSI II), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, we applied our data quality control criteria and found that the TCO algorithm produced measurement noise consistent with the measured prelaunch or specifications for VIIRS and MERSI II instrument noise. However, the TCO measurement noise was significantly lower than the spaced MODIS noise because MODIS’s extended service time likely produced instrument degradation. Overall, MODIS performed better than MERSI II but worse than VIIRS. Furthermore, we found that the residual error in remote sensing reflectance exponentially decreased as the measurement signal-to-noise ratio (MSNR) increased. Because of this exponential relationship, the MSNR should not be lower than 181 to achieve the <5% uncertainty goal of remote sensing reflectance at 443 nm that NASA proposed. Our results suggest that the TCO algorithm is an effective approach for comprehensively estimating and comparing instrument performance.

When using a field calibration site to perform in-orbit radiometric calibration of a space-borne remote sensor, the measured signal by the sensor may contain radiation from adjacent pixels, due to scattering in the Earth’s atmosphere and the sensor viewing characteristics. If this is not accounted for in the modeling, the accuracy of the radiometric calibration will be reduced. Nonuniformities of the ground target are more significant for the calibration of high-resolution sensors. In addition, if the brightness contrast of neighboring targets is not small, the impact of the adjacency effects will be more significant. It is important to quantitatively analyze and estimate the influence of this kind of adjacency effects, to reduce the uncertainty of in-orbit radiometric calibration. To evaluate the adjacency effects caused by atmospheric multiple scattering, this article constructed a local atmospheric point spread function model using long time-series satellite-ground synchronous observation data and developed an adjacency effects simulation method, which considers background reflectance spectral information. Tests on Sentinel-2A and Worldview-3 imagery overpassing the Baotou calibration and validation site (Baotou C&V site) (China) indicate that the proposed modeling method can effectively account for the influence of the adjacency effects in vicarious calibration. Uncertainties of relevant parameters and their contributions to the calibration result were also analyzed, and uncertainty assessment results show that the vicarious radiometric calibration scheme considering adjacency effects correction can bring about a total uncertainty less than 7%.

Cross calibration is critical to provide consistent measurements from multiple sensors. The intrinsic varieties between the Gao Fen (GF)-1 Wide Field of View (WFV) and Terra MODIS are caused by the relative spectral responses, sensor and solar zenith angle, azimuth angle. The sensor and solar observation conditions combined with the typical reflectance spectrum over Dunhuang test site were used to calculate the spectral band adjustment factors (SBAFs). A total of 32 image pairs between WFV and MODIS over Dunhuang site are available considering the cloud contamination. The SBAFs have the similar changing tendency in both 4 bands. According to the SBAFs, the gain of WFV can be calculated. TOA radiance products of MODIS and WFV are compared which shows that the accuracy of cross calibration coefficients is depending on the SBAF.