Remote Sensing (Mar 2022)

Columnar Water Vapor Retrieval by Using Data from the Polarized Scanning Atmospheric Corrector (PSAC) Onboard HJ-2 A/B Satellites

  • Yanqing Xie,
  • Weizhen Hou,
  • Zhengqiang Li,
  • Sifeng Zhu,
  • Zhenhai Liu,
  • Jin Hong,
  • Yan Ma,
  • Cheng Fan,
  • Jie Guang,
  • Benyong Yang,
  • Xuefeng Lei,
  • Honglian Huang,
  • Xiaobing Sun,
  • Xiao Liu,
  • Ying Zhang,
  • Maoxin Song,
  • Peng Zou,
  • Yanli Qiao

DOI
https://doi.org/10.3390/rs14061376
Journal volume & issue
Vol. 14, no. 6
p. 1376

Abstract

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As the latest members of Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianzai-2 (HJ-2) series satellites were launched on 27 September 2020 by China and are usually abbreviated as HJ-2 A/B satellites. The polarized scanning atmospheric corrector (PSAC) is one of main sensors onboard HJ-2 A/B satellites, which is mainly used to monitor atmospheric components such as water vapor and aerosols. In this study, a columnar water vapor (CWV) retrieval algorithm using two bands (865 and 910 nm) is developed for PSAC. The validation results of PSAC CWV data based on ground-based CWV data derived from Aerosol Robotic Network (AERONET) show that PSAC CWV data has a high accuracy, and all statistical parameters of PSAC CWV data are better than those of Moderate-resolution Imaging Spectroradiometer (MODIS) CWV data released by NASA. Overall, there is no obvious overestimation or underestimation in PSAC CWV data. The root mean square error (RMSE), mean absolute error (MAE), relative error (RE), and percentage of CWV data with error within ±(0.05+0.10∗CWVAERONET) (PER10) of PSAC CWV data are 0.17 cm, 0.13 cm, 0.08, and 78.19%, respectively. The RMSE, MAE, RE, and PER10 of MODIS CWV data are 0.59 cm, 0.48 cm, 0.28, and 16.55%, respectively. Compared with MODIS CWV data, PSAC CWV data shows a 71% decrease in RMSE, a 73% decrease in MAE, a 71% decrease in RE, and a 372% increase in PER10. In addition, the results of day-to-day comparisons between PSAC CWV data and AERONET data show that PSAC CWV data can effectively characterize the change trend of CWV.

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