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.

In this study, remote sensing analysis of coastal erosion is conducted for three typical regions of Alaska and Eastern Siberia based on remote sensing data collected between 1974 and 2017. The comparative studies were made on the difference in coastal erosion at different latitudes and the difference and influencing factors in coastal erosion at similar latitudes. The coastline retreatment is used to indicate coastal erosion. It is found that the most extensive erosion occurred along Alaska’s coast, followed by that of the Eastern Siberian coasts. Based on the analysis of the historical time series of snow and ice as well as climate data, it is found that at similar latitudes, the erosion of the Arctic coasts is closely related to the trend and fluctuations of the sea surface temperature (SST). Specifically, it is found that in Alaska, coastal erosion is closely related to the fluctuation of the SST, while in Eastern Siberia, it is related to the increasing or decreasing trend of the SST. A decreasing trend is associated with low coastal erosion, whereas an increasing trend is associated with accelerated coastal erosion. In the Arctic, the strong fluctuations of the SST, the continuous decline of the sea ice cover, and the consequent increase of the significant wave height are the critical factors that cause changes in coastal permafrost and coastal erosion.

Bottom reflectance is a significant parameter characterizing the bottom types for clear optically shallow waters, typically in oceanic islands and reefs. However, there is not an effective physics-based method for inverting the bottom reflectance using multispectral images. In this study, we propose a novel approach for quantitatively inverting the bottom reflectance at 550 nm without the dependence of in situ bottom reflectance data or any other priori knowledge. By linking different pixels in the same image and utilizing the strong linear relationship between their water depths and the spectral related parameters, the global situation of the radiative transfer model was constrained, and an exponential relationship between the log-transformed ratio of the blue–green band reflectance and the bottom reflectance was established. The proposed model was checked by comparing the Hydrolight input bottom reflectance with that inverted from Hydrolight simulated spectrum, resulting in correlating well. Our method has successfully performed using WorldView-2 and Landsat-8 in Midway Island in the North Pacific Ocean, with the cross- and indirectly checking and obtained reliable and robust results. In addition, we assessed the potential of the quantitative bottom reflectance in benthic classification and inversion ranges under different bottom reflectance. These results indicated that compared with those methods relying on in situ data or hyperspectral imagery, our algorithm is more likely to efficiently improve the parameterization of bottom reflectance, which can be very useful for benthic habitat mapping and transferred to large-scale regions in clean reef waters, as well as monitor time-series dynamics of oceanic bottom types to forecast coral reef bleaching.