• 遥感科学与应用技术 •

### 变异函数对泛克里金法的细粒子比星-地融合影响研究

1. 1. 中国科学院遥感与数字地球研究所 国家环境保护卫星遥感重点实验室,北京 100101
2. 中国科学院大学,北京 100049
• 收稿日期:2017-04-12 修回日期:2017-05-16 出版日期:2017-08-20 发布日期:2017-08-20
• 通讯作者: 张莹 E-mail:zhaoam@radi.ac.cn;zhangying02@radi.ac.cn
• 作者简介:

• 基金资助:
国家自然科学基金项目（41601386、41671367）;高分对地观测系统重大专项（30-Y20A39-9003-15/17）

### Impact of Variogram Parameters on Merging Satellite and Ground-Based FMF Based on Universal Kriging

ZHAO Aimei1,2(), ZHANG Ying1,*(), LI Zhengqiang1, LI Kaitao1, MA Yan1

1. 1. State Key Laboratory of Environmental Protection and Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
• Received:2017-04-12 Revised:2017-05-16 Online:2017-08-20 Published:2017-08-20
• Contact: ZHANG Ying E-mail:zhaoam@radi.ac.cn;zhangying02@radi.ac.cn

Abstract:

In order to improve the estimation accuracy of fine particulate matter (PM2.5) near the surface, fine mode fraction (FMF), one of the key parameters in the PM2.5 remote sensing method (PMRS) should be improved due to its significant error (more than 0.3). Merging MODIS FMF and ground-based FMF (AERONET&SONET) using the universal kriging (UK) method can effectively improve the accuracy of MODIS FMF over land. However, the parameters (the nugget, the sill value and the range parameter) in exponential variogram function need to be obtained using long-term MODIS FMF data because of the sparse ground-based sites, which cannot meet the need of PMRS based on instantaneous remote sensing estimates. In this study, we calculate the parameters in exponential variogram model and analyze the parameters’ variation using all MODIS data over six years from December 2010 to November 2016. Results show that the seasonal variations of correlation lengths during different years are consistent with each other. Correlation lengths in summer are significantly longer than any other three seasons while the sill values show an opposite trend, suggesting that FMF in summer has a smaller variation than the other three seasons. Furthermore, the other three seasons need more ground-based data than summer when merging MODIS FMF and ground-based FMF data. To quantify the impact of parameters in exponential variogram function on FMF fusion results and achieve instantaneous FMF fusion products, we use the range parameter in winter of 2016 (control test, denoted as CRT) and the mean value of range parameter of 6 winters over 2011-2016 (comparison test, denoted as CMP) as initial values separately to merge MODIS FMF and ground-based FMF. Leave-one-out cross-validation results show that the maximum deviation between FMF fusion results and ground-based FMF in CRT is 0.198 (in CMP is 0.218), significantly decreased compared to the maximum deviation between MODIS FMF and ground-based FMF (0.552). The mean error between FMF fusion results in CRT and ground-based FMF is close to that between FMF fusion results in CMP and ground-based FMF (0.070 vs 0.080). Then, we apply the fusion results in CRT and CMP separately to estimating PM2.5 mass concentration near the surface in combination with the same auxiliary data such as relative humidity, the planet boundary layer height. The estimated PM2.5 mass concentration near the surface has a slight discrepancy with a value of 1.2 μg/m3（77.6 μg/m3 vs 78.8 μg/m3）between CRT and CMP. In addition, compared with in-situ PM2.5 mass concentration measurements, the mean error in CRT is equal to that in CMP (37.4 μg/m3 vs 37.4 μg/m3). It can be concluded that the seasonal average of range parameter for many years can be a substitute for the range parameter in the same season since the FMF fusion results and the PM2.5 estimates are insensitive to range parameter. As a result, we can obtain instantaneous FMF fusion results to improve the estimate accuracy of PMRS when we get more satellite FMF data in the future.

Key words: fusion, FMF, variogram, PM2.5, MODIS