Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R² values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.

Nighttime light observations from remote sensing provide us with a timely and spatially explicit measure of human activities, and therefore enable a host of applications such as tracking urbanization and socioeconomic dynamics, evaluating armed conflicts and disasters, investigating fisheries, assessing greenhouse gas emissions and energy use, and analyzing light pollution and health effects. The new and improved sensors, algorithms, and products for nighttime lights, in association with other Earth observations and ancillary data (e.g., geo-located big data), together offer great potential for a deep understanding of human activities and related environmental consequences in a changing world. This paper reviews the advances of nighttime light sensors and products and examines the contributions of nighttime light remote sensing to perceiving the changing world from two aspects (i.e., human activities and environmental changes). Based on the historical review of the advances in nighttime light remote sensing, we summarize the challenges in current nighttime light remote sensing research and propose four strategic directions, including: Improving nighttime light data; developing a long time series of consistent nighttime light data; integrating nighttime light observations with other data and knowledge; and promoting multidisciplinary and interdisciplinary analyses of nighttime light observations.

Monthly composites of night-time light acquired from the Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) had been used to evaluate socio-economic dynamics and human rights during the Syrian Civil War, which started in March 2011. However, DMSP/OLS monthly composites are not available subsequent to February 2014, and the only available night-time light composites for that period were acquired from the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS). This article proposes an intercalibration model to simulate DMSP/OLS composites from the VIIRS day-and-night band (DNB) composites, by using a power function for radiometric degradation and a Gaussian low pass filter for spatial degradation. The DMSP/OLS data and the simulated DMSP/OLS data were combined to estimate the city light dynamics in Syria’s major human settlement between March 2011 and January 2017. Our analysis shows that Syria’s major human settlement lost about 79% of its city light by January 2017, with Aleppo, Daraa, Deir ez-Zor, and Idlib provinces losing 89%, 90%, 96%, and 99% of their light, respectively, indicating that these four provinces were most affected by the war. We also found that the city light in Syria and 12 provinces rebounded from early 2016 to January 2017, possibly as a result of the peace negotiation signed in Geneva.

As the successor of the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) mission significantly improves the spatial resolution of precipitation estimates from 0.25° to 0.1°. The present study analyzed the error structures of Integrated Multisatellite Retrievals for GPM (IMERG) monthly precipitation products over Mainland China from March 2014 to February 2015 using gauge measurements at multiple spatiotemporal scales. Moreover, IMERG products were also compared with TRMM 3B43 products. The results show that: (1) overall, IMERG can capture the spatial patterns of precipitation over China well. It performs a little better than TRMM 3B43 at seasonal and monthly scales; (2) the performance of IMERG varies greatly spatially and temporally. IMERG performs better at low latitudes than at middle latitudes, and shows worse performance in winter than at other times; (3) compared with TRMM 3B43, IMERG significantly improves the estimation accuracy of precipitation over the Xinjiang region and the Qinghai-Tibetan Plateau, especially over the former where IMERG increases Pearson correlation coefficient by 0.18 and decreases root-mean-square error by 54.47 mm for annual precipitation estimates. However, most IMERG products over these areas are unreliable; and (4) IMERG shows poor performance in winter as TRMM 3B43 even if GPM improved its ability to sense frozen precipitation. Most of them over North China are unreliable during this period.

This study investigates whether night-time light images acquired from the Defense Meteorological Satellite Program’s Operational Linescan System provide spatial and temporal insight with regard to the humanitarian aspects of the Syrian crisis. Evaluating the ongoing crisis in Syria is challenging since reliable witness reports are hard to gather in a war zone. Therefore satellite images, as one of the few sources of objective information, are potentially of great importance. We used 38 monthly Defense Meteorological Satellite Program’s Operational Linescan System composites covering the period between January 2008 and February 2014. The results indicate that night-time light and lit area in Syria declined by about 74% and 73%, respectively, between March 2011 and February 2014. In 12 of 14 provinces, night-time light declined by >60%. Damascus and Quneitra are the exceptions, with night-time light having declined only by about 35%. Notably, the number of internally displaced persons (IDPs) of each province shows a linear correlation with night-time light loss, with an R² value of 0.52. Through clustering the time series images, we found that the international border of Syria represents a distinct boundary between regions of differing night-time light spatiotemporal patterns. The contrast across the border increases as the region studied encompasses a wider zone on either side of the border. These findings lend support to the hypothesis that night-time light can be a useful source for monitoring humanitarian crises such as that unfolding in Syria.

Luojia 1-01 satellite, launched on 2 June 2018, provides a new data source of nighttime light at 130 m resolution and shows potential for mapping urban extent. In this paper, using Luojia 1-01 and VIIRS nighttime light imagery, we compared several methods for extracting urban areas, including Human Settlement Index (HSI), Simple Thresholding Segmentation (STS) and SVM supervised classification. According to the accuracy assessment, the HSI method using LJ1-01 data had the best performance in urban extent extraction, which presented the largest Kappa Coefficient value, 0.834, among all the results. For the urban areas extracted by VIIRS based HSI method, the largest Kappa Coefficient value was 0.772. In contrast, the largest Kappa Coefficient values obtained by STS method were 0.79 and 0.7512 respectively when using LJ1-01 and VIIRS data, while for SVM method the values were 0.7829 and 0.7486 when using Landsat-LJ and Landsat-VIIRS composite data respectively. The experimented results demonstrated that the utilization of nighttime light imagery can largely improve the accuracy of urban extent extraction and LJ1-01 data, with a higher resolution and more abundant spatial information, can lead to better identification results than its predecessors.