Surface water bodies, such as rivers, lakes, and reservoirs, play an irreplaceable role in global ecosystems and climate systems. Sentinel-2 imagery provides new high-resolution satellite remote sensing data. Based on the analysis of the spectral characteristics of the Sentinel-2 satellite, a novel water index called the Sentinel-2 water index (SWI) that is based on the vegetation-sensitive red-edge band (Band 5) and shortwave infrared (Band 11) bands was developed. Four representative water body types, namely, Taihu Lake, Yangtze River, Chaka Salt Lake, and Chain Lake, were selected as study areas to conduct a water body extraction performance comparison with the normalized difference water index (NDWI). We found that (1) the contrast value of the SWI was larger than that of the NDWI in terms of various water body types, including purer water, turbid water, salt water, and floating ice, which suggested that the SWI could achieve better enhancement performance for water bodies. (2) An effective water body extraction method was proposed by integrating the SWI and Otsu algorithm, which could accurately extract various water body types with high overall accuracy. (3) The method effectively extracted large water bodies and wide river channels by suppressing shadow noise in urban areas. Our results suggested that the novel method can achieve efficient water body extraction for rapidly and accurately extracting various water bodies from Sentinel-2 data and the novel method has application potential for larger-scale surface water mapping.

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.

Automatic water body extraction method is important for monitoring floods, droughts, and water resources. In this study, a new semantic segmentation convolutional neural network named the multi-scale water extraction convolutional neural network (MWEN) is proposed to automatically extract water bodies from GaoFen-1 (GF-1) remote sensing images. Three convolutional neural networks for semantic segmentation (fully convolutional network (FCN), Unet, and Deeplab V3+) are employed to compare with the water bodies extraction performance of MWEN. Visual comparison and five evaluation metrics are used to evaluate the performance of these convolutional neural networks (CNNs). The results show the following. (1) The results of water body extraction in multiple scenes using the MWEN are better than those of the other comparison methods based on the indicators. (2) The MWEN method has the capability to accurately extract various types of water bodies, such as urban water bodies, open ponds, and plateau lakes. (3) By fusing features extracted at different scales, the MWEN has the capability to extract water bodies with different sizes and suppress noise, such as building shadows and highways. Therefore, MWEN is a robust water extraction algorithm for GaoFen-1 satellite images and has the potential to conduct water body mapping with multisource high-resolution satellite remote sensing data.

Surface water mapping is essential for monitoring climate change, water resources, ecosystem services and the hydrological cycle. In this study, we adopt a multilayer perceptron (MLP) neural network to identify surface water in Landsat 8 satellite images. To evaluate the performance of the proposed method when extracting surface water, eight images of typical regions are collected, and a water index and support vector machine are employed for comparison. Through visual inspection and a quantitative index, the performance of the proposed algorithm in terms of the entire scene classification, various surface water types and noise suppression is comprehensively compared with those of the water index and support vector machine. Moreover, band optimization, image preprocessing and a training sample for the proposed algorithm are analyzed and discussed. We find that (1) based on the quantitative evaluation, the performance of the surface water extraction for the entire scene when using the MLP is better than that when using the water index or support vector machine. The overall accuracy of the MLP ranges from 98.25–100%, and the kappa coefficients of the MLP range from 0.965–1. (2) The MLP can precisely extract various surface water types and effectively suppress noise caused by shadows and ice/snow. (3) The 1–7-band composite provides a better band optimization strategy for the proposed algorithm, and image preprocessing and high-quality training samples can benefit from the accuracy of the classification. In future studies, the automation and universality of the proposed algorithm can be further enhanced with the generation of training samples based on newly-released global surface water products. Therefore, this method has the potential to map surface water based on Landsat series images or other high-resolution images and can be implemented for global surface water mapping, which will help us better understand our changing planet.

Rapid urbanization and economic development inevitably lead to light pollution, which has become a universal environmental issue. In order to reveal the spatiotemporal patterns and evolvement rules of light pollution in China, images from 1992 to 2012 were selected from the Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and systematically corrected to ensure consistency. Furthermore, we employed a linear regression trend method and nighttime light index method to demonstrate China’s light pollution characteristics across national, regional, and provincial scales, respectively. We found that: (1) China’s light pollution expanded significantly in provincial capital cities over the past 21 years and hot-spots of light pollution were located in the eastern coastal region. The Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei regions have formed light pollution stretch areas; (2) China’s light pollution was mainly focused in areas of north China (NC) and east China (EC), which, together, accounted for over 50% of the light pollution for the whole country. The fastest growth of light pollution was observed in northwest China (NWC), followed by southwest China (SWC). The growth rates of east China (EC), central China (CC), and northeast China (NEC) were stable, while those of north China (NC) and south China (SC) declined; (3) Light pollution at the provincial scale was mainly located in the Shandong, Guangdong, and Hebei provinces, whereas the fastest growth of light pollution was in Tibet and Hainan. However, light pollution levels in the developed provinces (Hong Kong, Macao, Shanghai, and Tianjin) were higher than those of the undeveloped provinces. Similarly, the light pollution heterogeneities of Taiwan, Beijing, and Shanghai were higher than those of undeveloped western provinces.

The Yemen conflict has caused a severe humanitarian crisis. This study aims to evaluate the Yemen crisis by making use of time series nighttime light images from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite sensor (NPP-VIIRS). We develop a process flow to correct NPP-VIIRS nighttime light from April 2012 to March 2017 by employing the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light image. The time series analyses at national scales show that there is a sharp decline in the study period from February 2015 to June 2015 and that the total nighttime light (TNL) of Yemen decreased by 71.60% in response to the decline period. The nighttime light in all provinces also showed the same decline period, which indicates that the Saudi-led airstrikes caused widespread and severe humanitarian crisis in Yemen. Spatial pattern analysis shows that the areas of declining nighttime light are mainly concentrated in Sana’a, Dhamar, Ibb, Ta’izz, ’Adan, Shabwah and Hadramawt. According to the validation with high-resolution images, the decline in nighttime light in Western cities is caused by the damage of urban infrastructure, including airports and construction; moreover, the reason for the decline in nighttime light in eastern cities is the decrease in oil exploration. Using nighttime light remote sensing imagery, our findings suggest that war made Yemen dark and provide support for international humanitarian assistance organizations.