On-orbit radiometric calibration of the optical sensors on-board SuperView-1 satellite using three independent methods.

On-orbit radiometric calibration of the optical sensors on-board SuperView-1 satellites is the foundation for further quantitative applications. A field calibration campaign was orchestrated to radiometrically calibrate the SuperView-1 optical sensors at the Baotou calibration site in China during September 2018. Based on the collected datasets, three independent methods (reflectance-based, radiance-based, and cross-calibration) were used to determine the radiometric calibration coefficients of the SuperView-1 optical sensors with multiple permanent artificial calibration targets. Comparisons of the desert top-of-atmosphere radiance calculated based on the coefficients determined with independent methods were analyzed. Comparison results show that the minimum and maximum relative differences of the radiometrically-calibrated desert TOA radiance between the reflectance-based and radiance-based methods are 1.26% and 4.23% for SV0102 and SV0104, respectively. While, the minimum and maximum relative differences of the radiometrically-calibrated desert TOA radiance between the reflectance-based and radiance-based methods are 0.82% and 6.83% for SV0101 and SV0103, respectively. The reasonably good agreement of the radiometrically calibrated coefficients of the SuperView-1 on-board sensors between these independent methods is encouraging. An uncertainty analysis was also discussed, and the results suggest that the overall uncertainties of the predicted TOA radiance are less than 4.5%, 4.0%, and 5.15% for the reflectance-based, radiance-based, and cross-calibration methods, respectively.

[A Method to Reconstruct Surface Reflectance Spectrum from Multispectral Image Based on Canopy Radiation Transfer Model].

Due to the lack of enough spectral bands for multi-spectral sensor, it is difficult to reconstruct surface retlectance spectrum from finite spectral information acquired by multi-spectral instrument. Here, taking into full account of the heterogeneity of pixel from remote sensing image, a method is proposed to simulate hyperspectral data from multispectral data based on canopy radiation transfer model. This method first assumes the mixed pixels contain two types of land cover, i.e., vegetation and soil. The sensitive parameters of Soil-Leaf-Canopy (SLC) model and a soil ratio factor were retrieved from multi-spectral data based on Look-Up Table (LUT) technology. Then, by combined with a soil ratio factor, all the parameters were input into the SLC model to simulate the surface reflectance spectrum from 400 to 2 400 nm. Taking Landsat Enhanced Thematic Mapper Plus (ETM+) image as reference image, the surface reflectance spectrum was simulated. The simulated reflectance spectrum revealed different feature information of different surface types. To test the performance of this method, the simulated reflectance spectrum was convolved with the Landsat ETM + spectral response curves and Moderate Resolution Imaging Spectrometer (MODIS) spectral response curves to obtain the simulated Landsat ETM+ and MODIS image. Finally, the simulated Landsat ETM+ and MODIS images were compared with the observed Landsat ETM+ and MODIS images. The results generally showed high correction coefficients (Landsat: 0.90-0.99, MODIS: 0.74-0.85) between most simulated bands and observed bands and indicated that the simulated reflectance spectrum was well simulated and reliable.