Retrieval Of Water Vapor And Aerosol Parameters Based On PSAC Onboard The HJ-2 Satellite

The Huanjing Jianzai（HJ）-2 A and B satellites are two optical satellites planned in the“Medium and Long-term Development Plan for China’s National Civil Space Infrastructure（2015-2025）”.They have been successfully launched on September 27,2020,to replace the overdue Huan Jing（HJ）-1 A and B satellites.Each satellite is equipped with four imaging sensors,including the 16m charge-coupled device（CCD）camera,the hyper-spectral imager（HSI）,the infrared multispectral scanner（IRMSS）,and the polarized scanning atmospheric corrector（PSAC）.The HJ-2 satellites are mainly used for disaster monitoring and environmental protection,and takes into account the data needs of land resources,water conservation,agriculture,forestry,earthquake and other related research.The HJ-2 satellites can only achieve the goal of disaster monitoring if its main payload,the CCD camera,can acquire high-quality images.However,the presence of atmospheric aerosol and water vapor can cause severe distortion of CCD images.This will seriously limit the application range of CCD camera.Due to the significant spatial and temporal differences in the distribution of aerosol and water vapor,it is necessary to use the aerosol and water vapor data synchronized with the main payload of HJ-2 satellite to achieve accurate atmospheric correction of data of the main payload of HJ-2 satellite.Therefore,it is of great significance to carry out remote sensing monitoring studies of water vapor and aerosol based on the PSAC synchronized with the main payload of HJ-2 satellite.In the work of developing water vapor retrieval algorithm for PSAC,we propose a column water vapor（CWV）retrieval algorithm that jointly uses ground-based and remote sensing data.The CWV retrieval model used in this algorithm is constructed based on the matching results between PSAC data and ground-based CWV data.This menas that the error of satellite observation data have been taken into account when constructing the model.Therefore,the model can eliminate the negative impact of the error of satellite observation data on the CWV retrieval results to a certain extent.This is the unique advantage of the water vapor retrieval algorithm developed in this study compared with the commonly used near-infrared water vapor retrieval algorithm based on look-up table.The validation results based on the ground-based data show that the PSAC CWV data developed in this study have high accuracy,and its accuracy is higher than that of the PSAC CWV retrieval results developed by using the commonly used look-up table retrieval algorithm and the accuracy of the CWV data released by NASA（MOD05）.The root mean square error（RMSE）,relative error（RE）,and the percentage of retrieval results with error within±(0.05+0.10*(2(1)（PER10）for the PSAC CWV data developed by using the algorithm proposed in this study are 0.16 cm,0.08 and 76.70%,respectively.The RMSE,RE and PER10 of PSAC CWV developed based on the commonly used look-up table retrieval algorithm are 0.28 cm,0.13 and62.41%,respectively.The RMSE,RE and PER10 of MOD05 are 0.70 cm,0.27 and28.72%,respectively.In addition,since the algorithm used to develop PSAC CWV data used in this work can obtain CWV results based on PSAC L1 data only through a simple formula calculation,the algorithm has a high retrieval efficiency.The CWV retrieval algorithm developed in this study is applied to the water vapor retrieval of Medium Resolution Spectral Imager-2（MERSI-2）onboard Feng Yun-3D satellite and Moderate-resolution Imaging Spectroradiometer（MODIS）onboard Terra satellite to test its retrieval accuracy and portability.The validation results based on ground-based data show that both MERSI-2 CWV data and MODIS CWV data developed by using the CWV retrieval algorithm proposed in this study have high accuracy.Among them,the statistical parameters of the MERSI-2 CWV data developed based on the method proposed in this study are closer to those of the official MERSI-2 CWV data released by the National Satellite Meteorological Center（NSMC）,and the statistical parameters of the MODIS CWV data developed based on the method proposed in this study are better than those of the official MODIS CWV data released by National Aeronautics and Space Administration（NASA）.This means that the CWV retrieval algorithm developed for PSAC in this study has high accuracy and good portability.Due to the band settings,observation angles and the amount of PSAC data,the current aerosol retrieval algorithm cannot be directly used for PSAC.To solve this problem,this study developed a new aerosol optical depth（AOD）retrieval algorithm using both polarization and non-polarization data according to the characteristics of PSAC data.The validation results based on the ground-based data show that the accuracy of PSAC AOD data developed in this work is comparable to that of the MODIS Combined AOD data released by NASA（MOD04）.The RMSE,RE,and the percentage of retrieval results with error within±(0.05+0.15* AOD_AERONET)for PSAC AOD data developed in this study are 0.17,0.30,and 52.47%,respectively.The RMSE,RE,and the percentage of retrieval results with error within±(0.05+0.15* AOD_AERONET)for MODIS AOD data are 0.17,0.28,and 60.77%,respectively.Since the MODIS AOD data is currently recognized as an AOD dataset with high accuracy,the PSAC AOD data developed in this study can be considered to have high accuracy.The AOD retrieval algorithm developed in this study is applied to the aerosol retrieval of Particulate Observing Scanning Polarimeter（POSP）onboard Gao Fen5-02 satellite with similar channel settings to PSAC,so as to test its retrieval accuracy and portability.The validation results based on ground-based data show that POSP AOD data also have statistical parameters that are very close to those of MODIS AOD data.This means that the AOD retrieval algorithm developed for PSAC has high retrieval accuracy and good portability.