一种TROPOMI对流层NO_(2)柱浓度数据校正方法

A Calibration Method for TROPOMI Tropospheric NO_(2) Column Concentration Data

数据质量是定量遥感研究的基础。最新型的大气环境卫星“哨兵-5P”搭载了对流层观测仪(tropospheric monitoring instrument,TROPOMI),目前TROPOMI的对流层NO_(2)数据尚缺乏有效校正方法。针对此问题,以中国大陆NO_(2)数据为例提出一种校正方法。首先,利用数据质量值(qa_value)对数据进行检索过滤;其次,采用基于伪不变特征值(pseudo-invariant features,PIF)的相对辐射校正、最大值合成(maximum value composites,MVC)去云以及箱型图法(boxplot method,BPM)过滤处理异常值;最后,将校正结果与地面实测数据进行对比评价。结果表明,通过一系列校正,“星-地”数据的相关性普遍提高,Pearson相关性提高了11%~70%,37.5%的样本显著性水平由P<0.05提高到P<0.01。该校正方法能够显著提高对流层NO_(2)数据的可靠性,可为大气NO_(2)定量遥感研究提供数据支撑。

Data quality is the foundation of quantitative remote sensing research.The latest atmospheric environment satellite“Sentinel-5P”carries tropospheric monitoring instrument(TROPOMI),and TROPOMI’s tropospheric NO_(2) data still lacks an effective correction method.Aiming to the above problems,we propose a correction method,and focus our analysis of NO_(2) data correction on China's Mainland.Firstly,qa_value is used to retrieve and filter the data,and then relative radiometric correction based on pseudo-invariant features(PIF),cloud removal based on maximum value composites(MVC)and box-plot method(BPM)are used to eliminate outliers.Then,we evaluate the calibration accuracy by comparing the calibration results with the ground measurement data.The results show that:through a series of corrections,the correlation of satellite-to-ground data has generally improved,Pearson correlation has increased by 11%~70%,and the significance level of 37.5%samples has increased from P<0.05 to P<0.01.The correction method proposed in this paper can significantly improve the reliability of tropospheric NO_(2) data,which could provide data support for research on quantitative remote sensing of atmospheric NO_(2).