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Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas

  • role： First author第一作者
  • Affiliation：

    College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 China

    Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China

  • Email：chenxy20@lzu.edu.cn
  • Introduction：陈香月，研究方向为大气环境遥感。E-mail：chenxy20@lzu.edu.cn
CHEN Xiangyue12,  
  • Affiliation：

    Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China

DING Jianli2,  
  • Affiliation：

    School of Artificial Intelligence, Shenzhen Polytechnic, Shenzhen 518055, China

WANG Jingzhe3,  
  • Affiliation：

    Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China

GE Xiangyu2,  
  • Affiliation：

    Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China

ZHANG Zipeng2,  
  • Affiliation：

    Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, Xinjiang University, Urumqi 830046, China

ZHANG Zhe2,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000 China

  • Email：zuohch@lzu.edu.cn
  • Introduction：左洪超，研究方向为陆面过程、大气污染扩散和输送及气候变化。E-mail：zuohch@lzu.edu.cn
ZUO Hongchao1*

résumé

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). It is expected to have good potential in improving the AOD inversion of dark and bright surfaces of land. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and determine subtle aerosol features. Retrieval of satellite aerosol properties is therefore often challenging due to considerable seasonal variations in surface reflectance and aerosol properties. To date, MAIAC AOD over arid regions under data-scarce environments has been evaluated. Considering these uncertainties, a systematic effort was made to evaluate the MAIAC AOD over arid areas using Aerosol Robotic Network (AERONET) ground-based AOD from 2000 to 2019. Considerable MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied ground surfaces and temporal scales. We employed a broader perspective and evaluated MAIAC performance under varying aerosol loading, aerosol types, surface coverage, and viewing geometry. The results show that (1) MAIAC performed well over various temporal scales, including monthly, seasonal and annual scales. Although underestimation is prevalent, MAIAC AOD in the spring and winter months correspond to the highest and lowest retrieval accuracies, respectively. (2) MAIAC performed well over four different typical land surface land surfaces, showing the highest retrieval accuracy over grassland, yet it slightly overestimated AOD. Construction land is most affected by the aerosol model, and farmland is strongly disturbed by surface reflectance and underestimated most obviously (Below EE = 46.5%). (3) The accuracies of the MAIAC AOD observations of Terra and Aqua are similar; R2 is more than 0.75, but both are underestimated, especially for Aqua. In general, MAIAC’s ability to provide AOD at high spatial resolution appears promising over arid areas and is expected to be helpful to study the characteristics of fine aerosols in arid areas and promote the study of local air quality.

mots-clés

remote sensing;aerosols;AOD;AERONET;MAIAC;arid areas

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