The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.

The urban heat island (UHI) effect significantly impacts urban environments and quality of life, yet research comparing coastal and inland cities is relatively lacking. This study, using the MYD11A2 dataset, analyzes the spatiotemporal evolution of land surface temperature (LST) and the surface urban heat island intensity index (SUHIII) in Beijing (inland) and Dalian (coastal) from 2003 to 2023, exploring the driving factors from 2003 to 2018 and proposing mitigation strategies for similar cities. Key findings: (1) Beijing’s SUHIII is 0.45 °C higher than Dalian’s during summer days, while Dalian’s SUHIII is 0.24 °C stronger than Beijing’s during winter nights, likely due to Dalian’s maritime climate, which raises nighttime LSTs and intensifies the winter SUHIII. (2) Both cities show similar trends in LST and SUHIII, with fluctuations until 2010, an increase after 2011, and a peak in 2023, with the expansion of heat island areas occurring mainly in suburban regions. (3) From 2003 to 2018, TEMP is the primary factor promoting SUHIII, followed by ET and POP, with EVI as the main mitigating factor. Beijing’s PREP weakens SUHI, while Dalian’s PREP promotes it. Coastal cities should focus on water bodies and wetland planning to mitigate heat islands, especially in areas like Dalian which are affected by PREP.

The retrieval of aerosol optical depth (AOD) from remote sensing data using the Dark Object (DO) method, which utilizes the measured radiance in areas with dark underlying scenes, such as dark vegetation or shadowed areas, is widely applied in atmospheric science. While many studies have aimed to improve its accuracy and validate its effectiveness, comprehensive uncertainty analysis associated with AOD retrieved based on the DO method remains limited. Therefore, this study focuses on addressing the uncertainty issues in AOD retrieval, using shadowed regions as a case study based on the DO method. We developed a method suitable for AOD retrieval from China's GF-1/WFV satellite data, which lacks shortwave infrared band information, and analyzed the retrieval process to identify the sources and propagation of uncertainty in the AOD retrieval process. Following metrological principles, we performed thorough calculations and analysis of the uncertainties associated with key influencing factors throughout the retrieval process, including with the retrieval algorithm, input data and related aspects. Furthermore, by considering the interrelationships among influencing factors and synthesizing uncertainties, we traced the propagation of uncertainty from GF-1/WFV Level 1 products to the final AOD products. The AOD retrieval results indicate strong consistency with AERONET ground observations. We characterized the uncertainties associated with AOD retrieval using an uncertainty tree diagram, quantifying specific uncertainty results. This provides insights into satellite-based AOD retrieval methods and investigates the traceability of China's land satellite AOD products.

Hyperspectral remote sensing is crucial due to its continuous spectral information, especially in the quantitative remote sensing (QRS) field. Surface reflectance (SR), a fundamental product in QRS, can play a pivotal role in application accuracy and serves as a key indicator of sensor performance. However, the distinctive spectral characteristics of a hyperspectral image (HSI) make it particularly susceptible to noise during the process of imaging, which inevitably degrades data quality and reduces SR accuracy. Moreover, the validation of hyperspectral SR faces challenges due to the scarcity of reliable validation data. To address these issues, aiming at fast and efficient processing of Chinese domestic ZY1-02D hyperspectral level-1 data, this study proposes a comprehensive processing framework: (1) To address the low efficiency of traditional bad line detection by visual examination, an automatic bad line detection method based on the pixel grayscale gradient threshold algorithm is proposed; (2) A spectral correlation-based interpolation method is developed to overcome the poor performance of adjacent-column averaging in repairing wide bad lines; (3) A reliable validation method was established based on the spectral band adjustment factors method to compare hyperspectral SR with multispectral SR and in-situ ground measurements. The results and analysis demonstrate that the proposed method improves the accuracy of ZY1-02D SR and the method ensures high processing efficiency, requiring only 5 min per scene of ZY1-02D HSI. This study provides a technical foundation for the application of ZY1-02D HSIs and offers valuable insights for the development and enhancement of next-generation hyperspectral sensors.

Sea surface temperature (SST) is widely used to characterize marine productivity, environmental pollution, and climate variability, and is commonly derived from thermal infrared measurements obtained by optical satellite sensors. However, accurately retrieving large-scale SSTs remains challenging due to the complexity of air–sea coupling processes and the difficulty of accurately obtaining key intermediate parameters. This study proposes a day–night-differentiated SST retrieval method with emissivity correction rather than treating it as a fixed value. Specifically, radiance characteristics from the mid-infrared band are integrated alongside those from thermal infrared bands. The retrieved SSTs are then validated against the MODIS SST product and in situ measurements. The results demonstrate strong consistency between the retrieved SST and the MODIS SST product, with overall root mean square errors (RMSEs) of 0.66 K and 0.82 K for daytime and nighttime, respectively. In winter the RMSEs improve to 0.37 K (day) and 0.42 K (night). In situ validation against Argo measurements in 2019 shows that the RMSEs of the retrieved SSTs are approximately 0.26 K for both day and night. This confirms the efficacy of the proposed SST retrieval approach, providing a feasible solution for high-precision SST retrieval in high-latitude regions with large view zenith angles.

Land surface temperature (LST) is a critical indicator of the interactions between the Earth’s surface and atmosphere and has long been available from satellite observations in the thermal infrared (TIR) region. Recognized as a primary way to evaluate the accuracy of LSTs, in situ validation is still a challenging task because of uncertainties in ground measurements, spatial scale mismatch between ground and satellite-based measurements, the heterogeneity of natural land surfaces, etc., leading to a lack of consistency among sets of validation results. Therefore, to improve robustness against uncertainties, an optimized approach for LST validation via the Kalman filter is presented, and prediction of CVE (comprehensive validation estimate) which is close to “true” value, and more precise than those based on a single measurement alone is obtained. After the uncertainties involved in the validation process are constrained, this method is applied to FY-3D/MERSI-II LSTs with ground measurements from four sites in China. The results indicate that the CVE is 1.11 K, with an uncertainty of 0.07 K. Additionally, a comparison is performed with the weighted average method, and the efficacy of the Kalman filter approach in enhancing the validation accuracy is confirmed.