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.

Land surface reflectance retrieval from optical hyperspectral data collected with an unmanned aerial vehicle platform.

We present a physical-based atmospheric correction algorithm for land surface reflectance retrieval based on radiative transfer model MODTRAN 5, with which the aerosol optical thickness @550 nm (AOT@550nm), columnar water vapor (CWV) could also be estimated from the hyperspectral data collected over UAV platform. Then, the method was tested on both the synthetic and field campaign-collected hyperspectral data by an UAV-VNIRIS (UAV visible/near-infrared imaging hyperspectrometer) with the spectral range covering from 400 to 1000 nm. The retrieval results were validated with theoretical values from synthetic data and truth values from field campaign measurements. The results show that the averaged MAE (mean absolute error) and RMSE (root mean squared error) of measured and retrieved surface reflectance based on estimated AOT@550nm and CWV is 0.0134 and 0.0130. Meanwhile, the averaged MAE and RMSE of measured and retrieved surface reflectance based on ground measured AOT@550nm and CWV is 0.0101 and 0.0112. The results show that our introduced method has good agreement with the method based on ground-measured AOT@550nm and CWV. These encouraging results also indicate that the introduced physical-based atmospheric approach provides a quick and reliable way to acquire the land surface reflectance from UAV platform-observed hyperspectral data for further quantitative remote sensing applications.

Land surface temperature retrieved from airborne multispectral scanner mid-infrared and thermal-infrared data.

Land surface temperature (LST) is one of the key parameters in the physics of land surface processes at local/global scales. In this paper, a LST retrieval method was proposed from airborne multispectral scanner data comparing one mid-infrared (MIR) channel and one thermal infrared (TIR) channel with the land surface emissivity given as a priori knowledge. To remove the influence of the direct solar radiance efficiently, a relationship between the direct solar radiance and water vapor content and the view zenith angle and solar zenith angle was established. Then, LST could be retrieved with a split-window algorithm from MIR/TIR data. Finally, the proposed algorithm was applied to the actual airborne flight data and validated with in situ measurements of land surface types in the Baotou site in China on 17 October 2014. The results demonstrate that the difference between the retrieved and in situ LST was less than 1.5 K. The bais, RMSE, and standard deviation of the retrieved LST were 0.156 K, 0.883 K, and 0.869 K, respectively, for samples.