Evaluation of LJ1-01 Nighttime Light Imagery for Estimating Monthly PM2.5 Concentration: A Comparison With NPP-VIIRS Nighttime Light Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Guo Zhang¹  Miaozhong Xu¹  Yingrui Shi¹ 

[1] State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;

关键词: Geographically weighted regression (GWR) model;    LJ1-01;    moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD);    NPP-VIIRS;    particulate matter (PM $_{2.5}$ ) concentration;    satellite observation;   

DOI  :  10.1109/JSTARS.2020.3002671

来源: DOAJ

【 摘 要 】

Air quality degradations caused by fine particulate matter (PM_2.5) can lead to various health problems, and accurate PM2.5 data are critical for managing the environment and ensuring public health. Radiation signals collected by nighttime light (NTL) remote sensing satellites are influenced by PM_2.5 concentrations, and thus, incorporating NTL imagery in statistical models has been widely used to predict PM_2.5 concentrations. However, scarce work has been carried out with new-generation NTL data from the LJ1-01 satellite, which has a fine spatial resolution and wide measurement range. In this study, we integrated satellite observation data and meteorological data to construct five models based on the geographically weighted regression to validate the feasibility of LJ1-01/NPP-VIIRS in Moderate Resolution Imaging Spectroradiometer AOD-based PM_2.5 prediction in the Beijing-Tianjin-Hebei region. The models were validated by the cross-validation method. The results showed that the addition of NTL information could improve the performance of the PM_2.5 prediction model. The seasonal R² with NTL in AOD-PM_2.5 model have improved by 5.07%, 4.50%, 2.95%, and 2.56% in model fitting and 1.20%, 1.75%, 2.20%, and 4.41% in cross-validation. Furthermore, the LJ1-01 NTL data revealed additional details and improved the prediction accuracy, compared with the NPP-VIIRS in AOD-PM_2.5 model, the seasonal R² with LJ1-01 in AOD-PM_2.5 model increased by 1.16%, 1.79%, 0.76%, and 1.15% in model fitting and 1.04%, 0.85%, 0.78%, 1.37% in cross-validation. Thus, our findings indicate that LJ1-01 and NTL data have the potential for predicting PM_2.5 and that they could constitute a useful supplemental data source for estimating ground-level PM_2.5 distributions.

【 授权许可】

Unknown