Scientists propose aerosol retrieval method for newly launched satellite
Aerosols, suspended particles in the atmosphere, influence the Earth's climate system by directly scattering and absorbing solar and terrestrial radiations as well as by acting as cloud condensation nuclei and ice nuclei to modify the properties of clouds.
Newly launched satellite sensors often face challenges in obtaining sufficient observations in a short period to support the development of land surface reflectance (LSR) constraints for aerosol retrieval.
A research team led by Prof. Li Zhengqiang from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS), together with collaborators, proposed a generalized land surface reflectance reconstruction method that provides efficient constraints to surface reflectance for high-precision aerosol retrieval. The study was published in Remote Sensing of Environment on June 16.
Based on the reference surface reflectance product, the proposed method only needs a short time observation of the target sensor, and it uses spatial-temporal minimum reflectance technology to reconstruct the long-time historical surface reflectance product of the reference sensor. Then the reconstructed virtual LSR can be used to construct reliable surface constraints for high-precision retrieval of aerosol parameters.
The researchers applied this method to the Particulate Observing Scanning Polarimeter (POSP) onboard Gaofen-5 (02) satellite. They found that using the overlapping observations of POSP and MODIS for only one month, a stable surface reflectance reconstruction relationship between the two could be established. And the retrieved aerosol optical depth based on the reconstructed LSR achieved high accuracy level according to the validation results against AErosol RObotic NETwork observations.
More information: Zheng Shi et al, A generalized land surface reflectance reconstruction method for aerosol retrieval: Application to the Particulate Observing Scanning Polarimeter (POSP) onboard GaoFen-5 (02) satellite, Remote Sensing of Environment (2023). DOI: 10.1016/j.rse.2023.113683
Provided by Chinese Academy of Sciences