Search

Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management. [ABSTRACT FROM AUTHOR]

Fine particulate matter such as PM 2.5 has been the focus of increasing public concerns because of its adverse effect on environment and health risks. However, existing efforts of mapping PM 2.5 concentrations are always limited by coarse spatial resolutions and temporal frequencies. Addressing this shortcoming, here we explicitly estimated hourly PM 2.5 concentrations at 1-km spatial resolution in China from March 2018 to February 2019 using a two-stage random forest model. In the first stage, we used a gap-filling method to generate full-coverage Aerosol Optical Depth (AOD) by fusing AOD data from satellite (Himawari-8 and MODIS) and weather forecast model (CAMS), and additional meteorological and geographical variables. Gap-filled AOD was subsequently used to estimate hourly PM 2.5 in the Stage II. Results showed that our model achieved accurate and robust estimations of PM 2.5 concentrations, with an overall cross-validated R2 of 0.85, root mean squared error of 11.02 μg/m3, and mean absolute error of 6.73 μg/m3. CAMS-simulated PM 2.5 , elevation, and gap-filled AOD were identified to be important variables contributing to the model performance of PM 2.5 estimation. The model performance varied over the daily temporal scale. Specifically, daily estimation model performed better in spring and winter but worse in summer and autumn. We provide an alternative to generate spatially and temporally explicit mapping of PM 2.5 concentrations with fine resolutions, making it possible to achieve real-time monitoring of air pollutions. The detailed spatial heterogeneity and diurnal variability of PM 2.5 concentrations will also be valuable for environmental exposure assessments. Unlabelled Image • A two-stage random forest model was developed to estimate hourly full-coverage PM2.5 at 1 km resolution in China. • Gap-filled AOD was generated using AHI, MAIAC and CAMS AOD. • Estimation of PM2.5 concentrations achieved relatively high accuracies. • CAMS PM2.5 simulations, elevation and gap-filled AOD greatly contributed to the model performance. [ABSTRACT FROM AUTHOR]