Thick cloud and shadows have a significant impact on the availability of optical remote sensing data. Although various methods have been proposed to address this issue, they still have some limitations. First, most approaches rely on a single clear reference image as complementary information, which becomes challenging when the target image has large missing areas. Secondly, the existing methods that can utilize multiple reference images require the complementary data to have high temporal correlation, which is not suitable for situations where the difference between the reference image and the target image is large. To overcome these limitations, a flexible spatiotemporal deep learning framework based on generative adversarial networks is proposed for thick cloud removal, which allows for the use of three arbitrary temporal images as references. The framework incorporates a three-step encoder that can leverage the uncontaminated information from the target image to assimilate the reference images, enhancing the model’s ability to handle reference images with diverse temporal differences. A series of simulated and real experiments on Landsat 8 and Sentinel 2 data is performed to demonstrate the effectiveness of the proposed method. The proposed method is especially applicable to small/large-scale regions with reference images that are significantly different from the target image.

The Wide-swath Thermal Infrared Remote Sensor (WTI) is the primary payload on Chinese Gaofen-5A(GF-5A) satellite. WTI is a cross-track infrared scanner developed by the Beijing Institute of Space Mechanics & Electricity(BISME). It uses the measurements of high temperature blackbody(HTBB) and low temperature blackbody (LTBB) as the on-orbit radiometric calibration reference sources allowing to update the calibration coefficients on a scan-by-scan basis. There is scan-angle effect, different calibration coefficients for different scan angles, for WTI. The method to get the angle-dependent calibration coefficients based on HTBB and LTBB calibration data is presented in this paper. The scan angle correction coefficients (SACs) are applied to account for the scan-angle effect. The SACs are calculated based on cross-calibration which utilize the Advanced Geostationary Radiation Imager(AGRI) aboard on FY-4B as reference. The calibration site data with the infrared ground temperature automatic measurement equipment and the atmospheric data from ERA5 are utilized to evaluate the absolute calibration accuracy. The root mean square error(RMSE) of WTI are as follows: 0.82K for 8.2μm, 0.74K for 8.6μm, 0.75K for 10.8μm, 0.85K for 12.0μm. To assess on-orbit radiometric performances of WTI, the key parameters including temperature stability, Noise Equivalent Temperature Difference (NETD) and System Radiation Stability(SRS) are calculated. Since the launch of GF-5A in December 2022, the radiometric performances have been monitored based on routine measurements of the onboard calibration data. There are only 0.4mK, 7mK and 8mK temperature fluctuation respectively for IRFPA , LTBB and HTBB for a single imaging time about 1200 sconds. The fluctuations of NETD and SRS are less than 1% and 0.002% respectively. The results indicate that there are no significant attenuation in WTI on-orbit radiometric performance during the first two years following its launch.

Angular reflectance effects are essential for radiometric calibration and the interpretation of remotely sensed data, yet remain difficult to characterize under realistic field conditions. This study presents a UAV-based approach for high-angular-resolution hyperspectral HDRF measurement over the Dunhuang Radiometric Calibration Site (DRCS). Six circular UAV flights were conducted at viewing zenith angles from 10° to 60° using a non-imaging hyperspectral sensor, with continuous ground-based irradiance measurements used to derive HDRF values in the 400–850 nm range. Ross–Li model fitting achieved high accuracy (R² > 0.968), while residual analysis identified systematic discrepancies associated with forward-scattering geometry and secondary illumination from nearby solar towers, with local residuals up to 4.5%. These results highlight the value of dense angular sampling and rapid UAV-based measurements for interpreting field-measured HDRF and for the informed application of reflectance models in calibration environments.

High-resolution TIR missions require sustained and well-characterized radiometric accuracy to support applications such as land surface temperature retrieval, drought monitoring, and surface energy budget analysis. To address this need, we develop an operational and automated ground-based vicarious radiometric calibration framework for TIR sensors and demonstrate its performance using the Wide-swath Thermal Infrared Imager (WTI) onboard Gaofen-5 01A (GF-5A). Three arid Gobi calibration sites were selected by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, Shuttle Radar Topography Mission (SRTM)-derived topography, and WTI-based radiometric uniformity metrics to ensure low cloud cover, flat terrain, and high spatial homogeneity. Automated ground stations deployed at Golmud, Dachaidan, and Dunhuang have continuously recorded 1 min contact surface temperature since October 2023. Field-measured emissivity spectra, Integrated Global Radiosonde Archive (IGRA) radiosonde profiles, and MODTRAN (MODerate resolution atmospheric TRANsmission) v5.2 simulations were combined to compute top-of-atmosphere (TOA) radiances, which were subsequently collocated with WTI imagery. After data screening and gain-stratified regression, linear calibration coefficients were derived for each TIR band. Based on 189 scenes from February–July 2024, all four bands exhibit strong linearity (R-squared greater than 0.979). Validation using 45 independent scenes yields a mean brightness–temperature root-mean-square error (RMSE) of 0.67 K. A full radiometric-chain uncertainty budget—including contact temperature, emissivity, atmospheric profiles, and radiative transfer modeling—results in a combined standard uncertainty of 1.41 K. The proposed framework provides a low-maintenance, traceable, and high-frequency solution for the long-term on-orbit radiometric calibration of GF-5A WTI and establishes a reproducible pathway for future TIR missions requiring sustained calibration stability.

Unmanned aerial vehicles (UAVs) provide a flexible platform for surface reflectance measurement at spatial scales between ground observations and satellite remote sensing. This study develops a UAV-based spectroradiometric system for surface reflectance retrieval under natural illumination conditions using non-imaging hyperspectral sensors. The system integrates two stabilized spectroradiometers mounted on a UAV to simultaneously measure hemispherical downwelling irradiance and upwelling surface radiance at flight altitude, enabling reflectance retrieval through a radiance–irradiance ratio framework without relying on ground calibration targets or radiative transfer model inversion. Field experiments were conducted over agricultural plots, and the UAV-derived reflectance was quantitatively validated against ground-based dual-spectroradiometer measurements. The results demonstrate stable irradiance measurements during flight and good agreement between UAV- and ground-derived reflectance across the 400–900 nm spectral range. The proposed system offers a practical and reliable solution for hyperspectral reflectance retrieval using UAV platforms.