An Improved Correction Method of Nighttime Light Data Based on EVI and WorldPop Data

Pengfei Liu,Dandan Zhang,Qing Wang,Yongzong Lu

doi:10.3390/rs12233988

Abstract

Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data has the shortcomings of discontinuous and pixel saturation effect. It was also incompatible with the Soumi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data. In view those shortcomings, this research put forward the WorldPop and the enhanced vegetation index (EVI) adjusted nighttime light (WEANTL) using EVI and WorldPop data to achieve intercalibration and saturation correction of DMSP/OLS data. A long time series of nighttime light images of china from 2001 to 2018 was constructed by fitting the DMSP/OLS data and NPP/VIIRS data. Corrected nighttime light images were examined to discuss the estimation ability of gross domestic product (GDP) and electric power consumption (EPC) on national and provincial scales, respectively. The results indicated that, (1) after correction, the nighttime light (NTL) data can guarantee the growth trend on national and regional scales, and the interannual volatility of the corrected NTL data is lower than that of the uncorrected NTL data; (2) on the national scale, compared with the established model of NTL data and GDP data (NTL-GDP), the determination coefficient (R2) and the mean absolute relative error (MARE) are 0.981 and 8.518%. The R2 and MARE of the established model of NTL data and EPC data (NTL-EPC) were 0.990 and 4.655%; (3) on the provincial scale, the R2 and MARE of NTL-GDP model under the provincial units are 0.7386 and 38.599%. The R2 value and MARE of NTL-EPC model are 0.8927 and 29.319%; (4) on the provincial scale, the R2 and MARE of NTL-GDP model on time series are 0.9667 and 10.877%. The R2 and MARE of NTL-GDP model on time series are 0.9720 and 6.435%; the established TNL-GDP and TNL-EPC models with 30 provinces data all passed the F-test at the 0.001 level; (5) the prediction accuracy of GDP and EPC on time series was nearly 100%. Therefore, the correction method provided in this research can be applied in estimating the GDP and EPC on multiple scales reliably and accurately.

Highlights

Night light remote sensing, as an active branch of remote sensing [1], can obtain the information of visible light and near infrared electromagnetic wave emitted from the ground surface on cloudless nights [2] It can reflect the urban night lighting and capture the night lighting of fishing boats [3], natural gas combustion [4], and forest fire light [5]
Three indicators were selected for quantitative verification of pixel correction results: total nighttime light (TNL), normalized difference index (NDI), and the sum of normalized difference index (SNDI) [54]
As shown in Formula (13), TNL refers to the sum of night light radiation values in the target area, which is a key indicator to measure the overall trend of regional economic development

Summary

Introduction

As an active branch of remote sensing [1], can obtain the information of visible light and near infrared electromagnetic wave emitted from the ground surface on cloudless nights [2] It can reflect the urban night lighting and capture the night lighting of fishing boats [3], natural gas combustion [4], and forest fire light [5] It intuitively reflects the human activities and social development rules [6] and is widely applied in the estimation of socioeconomic parameters such as GDP [7], population density [8], electric power consumption (EPC) [9], greenhouse gas emissions [10] and poverty index [11]; urban expansion [12] and urbanization research [13]; major events assessment such as energy crisis [14], earthquake [15] and war [16]; environmental effect analysis of urban expansion [17]; light pollution [18] and effect analysis [19]. The illumination area is overestimated due to the reflection of urban light to urban edge and internal water body, and increases the error of distribution estimation [29]

Methods

Results

Discussion

Conclusion