The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China's first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The R² values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the in situ measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

The ZY1-02D satellite, which was launched in 2019, is China’s first civil hyperspectral satellite. However, the laboratory calibration and vicarious calibration methods could not provide accurate radiometric calibration coefficients after the satellite had been launched. In this article, we describe how a cross-calibration method was utilized to calibrate the ZY1-02D hyperspectral imager using the well-calibrated Gaofen-5 Advanced Hyperspectral Imager (GF-5 AHSI). The 6S radiative transfer model was selected to simulate the apparent reflectance of the two hyperspectral sensors under corresponding imaging conditions, and the calibration coefficients were calculated by spectral channel matching. The reflectance-based vicarious calibration was carried out for comparison. Through the validation experiments, it is shown that the reflectance data obtained by cross-calibration and vicarious calibration are basically consistent, showing a stable radiation performance. At the Dunhuang calibration site, the ratio of measured surface reflectance to the cross-calibrated image reflectance is between 0.9 and 1.1, the R2 are more than 0.96, and the spectral angles are less than 3°. The validation results for different ground features also show the applicability of the corrected coefficients. When compared with different sensors, the maximum difference between the ZY1-02D reflectance results after cross-calibration and Landsat-8/Sentinel-2 is less than 0.04 and the mean difference is less than 0.02, which further proves that the ZY1-02D hyperspectral imager has a high radiation accuracy after cross-calibration. The proposed cross-calibration method could be used as an effective supplement to the on-orbit calibration method, and could also be extended to other satellite hyperspectral imagers.

Soil degradation threatens environmental health and sustainable land use, with declining soil organic matter (SOM) and increasing salinization being two key manifestations. Accurate assessment of the spatial distribution of SOM and salinity is of great importance for both agricultural productivity and ecological conservation. This paper proposes a novel end-to-end deep learning model—the residual attention regression network (RARNet)—to enable quantitative estimation of soil parameters based on satellite-borne hyperspectral imagery. RARNet combines two-dimensional spectral convolution, residual feature learning, and attention mechanisms to jointly capture local spectral patterns and global spectral dependencies, thereby improving spatial–spectral feature representation. The experimental results obtained over a 17,000 km² black-soil region using a satellite hyperspectral dataset show the improved predictive performance of RARNet. Under the 5-fold cross-validation setting, the validation coefficients of determination (R²) are 0.7291 and 0.8368, the RMSE values are 2.0986 and 2.4806, and the MAE values are 1.7449 and 2.0152 for SOM and soil salinity, respectively. Compared with representative deep learning baselines, including LSTM and CNN with spectral attention, RARNet achieved higher average performance. It also improved R² by approximately 10%–15% compared with the ResNet backbone and by 15%–25% compared with the CARS-XGBoost method. The trained model was applied to the study area, generating large-scale spatial distribution maps of SOM and salinity. The mapping results revealed that the region suffers from low SOM levels and widespread salinization, with over half the area showing a salt content of 1.0 g/kg or higher. Overall, the proposed RARNet provides a feasible framework for large-scale mapping of SOM, salinization, and their spatial patterns in the study area. This demonstrates the potential of combining satellite hyperspectral imagery with deep learning for monitoring of soil properties and land degradation. Although further validation is required for other soil types and climates, the current findings provide a quantitative reference for land restoration and regional ecological risk assessment.