ALBERT

Description: Pseudo-invariant calibration sites (PICS) have been used for the radiometric calibration and stability monitoring of satellite optical sensors. Several stable PICS, such as those in the Sahara Desert in North Africa, were selected for the vicarious calibration of earth remote sensing satellites. However, the selection procedure of PICSs in the whole of Northwest China has not been fully explored before. This paper presents a novel technique for selecting PICS in Northwest China by combined using the coefficient of variation (CV) and the iteratively reweighted multivariate alteration detection (IR-MAD) technique. IR-MAD, which calculates the differences between two multispectral N-band images from the same scene acquired at different times, is used to identify no-change pixels (NCPs) of the scene through one image pair. The NCPs from IR-MAD using the long-term data of FY-3 visible infrared radiometer (VIRR) and aqua Moderate Resolution Imaging Spectroradiometer (MODIS) were aggregated into the contiguously stable sites. The traditional spatial uniformity and temporal stability from MODIS surface products were used to select the potential PICS. By combining the results of both methods, over thirty PICSs with a wider brightness range of the scene types were selected. To confirm and characterize these PICSs over Northwest China, Landsat operational land imager (OLI) high-spatial-resolution images were used to check the spatial uniformity of the selected site to determine the specific location and the size of these sites. Additionally, the surface spectral reflectance and bidirectional reflectance distribution function (BRDF) were obtained from the field campaign at Chaidamu Basin, 2018. To demonstrate the practical utilization and usability of these PICSs, they were employed in the multi-site top of atmosphere (TOA) reflectance simulation to validate the operational calibration performance of Aqua/MODIS and FY-3D/MERSI-II (Medium Resolution Spectral Imager II). The simulation results showed good consistency compared with the observations from both MODIS and MERSI-II, with a relative bias and root mean square error (RMSE) of