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Remote sensing is essential for monitoring fisheries. Optical sensors such as the day–night band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) have been a crucial tool for detecting vessels fishing at night. It remains challenging to ensure stable detections under various conditions affected by the clouds and the moon. Here, we develop a machine learning based algorithm to generate automatic and consistent vessel detection. As DNB data are large and highly imbalanced, we design a two-step approach to train our model. We evaluate its performance using independent vessel position data acquired from on-ship radar. We find that our algorithm demonstrates comparable performance to the existing VIIRS boat detection algorithms, suggesting its possible application to greater temporal and spatial scales. By applying our algorithm to the East China Sea as a case study, we reveal a recent increase in fishing activity by vessels using bright lights. Our VIIRS boat detection results aim to provide objective information for better stock assessment and management of fisheries.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities’ distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized DifferenceVegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission’s proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Suomi NPP has been successfully operating since its launch on 28 October 2011. As one of the major payloads, along with microwave and infrared sounders (Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS)), and ozone mapping/profiling (OMPS) instruments, the Visible Infrared Imaging Radiometer Suite (VIIRS) has performed for well beyond its mission design life. Its data have been used for a variety of applications for nearly 30 environmental data products, including global imagery twice daily with 375 and 750 m resolutions, clouds, aerosol, cryosphere, ocean color and sea-surface temperature, a number of land products (vegetation, land-cover, fire and others), and geophysical and social economic studies with nightlights. During the early days of VIIRS operational calibration and data production, there were inconsistencies in both algorithms and calibration inputs, for several reasons. While these inconsistencies have less impact on nowcasting and near real-time applications, they introduce challenges for time series analysis due to calibration artifacts. To address this issue, we developed a comprehensive algorithm, and recalibrated and reprocessed the Suomi NPP VIIRS radiometric data that have been produced since the launch. In the recalibration, we resolved inconsistencies in the processing algorithms, terrain correction, straylight correction, and anomalies in the thermal bands. To improve the stability of the reflective solar bands, we developed a Kalman filtering model to incorporate onboard solar, lunar, desert site, inter-satellite calibration, and a deep convective cloud calibration methodology. We further developed and implemented the Solar Diffuser Surface Roughness Rayleigh Scattering model to account for the sensor responsivity degradation in the near infrared bands. The recalibrated dataset was validated using vicarious sites and alternative methods, and compared with independent processing from other organizations. The recalibrated radiometric dataset (namely, the level 1b or sensor data records) also incorporates a bias correction for the reflective solar bands, which not only addresses known calibration biases, but also allows alternative calibrations to be applied if so desired. The recalibrated data have been proven to be of high quality, with much improved stability (better than 0.3%) and accuracy (by up to 2%). The recalibrated radiance data are now available from 2012 to 2020 for users and will eventually be archived on the NOAA CLASS database.

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (<0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

Amid rising geopolitical instability, timely, systematic, and independent monitoring of conflict zones is essential. We show how nighttime thermal anomalies from spaceborne sensors can be combined with boundary data, territorial control vector data, and high-resolution damage assessments to generate indicators of conflict at different levels of analysis. Leveraging the Visible Infrared Imaging Radiometer Suite’s Nightfire product, we extract signals of conflict across Ukraine, tracking the status of heavy industry, delineating the frontline, and detecting urban combat. Although VIIRS data face challenges such as low spatial resolution and susceptibility to atmospheric interference, we find that these can provide valuable insights when paired with supporting geodata. Our results thus support fast, low-cost, and scalable monitoring of conflict zones, enabling timely intelligence in rapidly evolving scenarios.

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.

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.

Nighttime light (NTL) images have been broadly applied to extract urban built-up areas in recent years. However, the typical NTL images provided by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) have the drawbacks of low resolution and blooming effect, which bring difficulty for the application of them in urban built-up area extraction. Therefore, this paper proposes the POI (point of interest) and LST (land surface temperature) adjusted NTL urban index (PLANUI) to extract the urban built-up areas with high accuracy. PLANUI is the first urban index to integrate POI and NTL for urban built-up area extraction. In this paper, NPP/VIIRS and Luojia 1-01 images were introduced as the original NTL data and the vegetation adjusted NTL urban index (VANUI) was selected as the comparison item. The threshold method was utilized to extract urban built-up areas from these data. The results show that: (1) Based on the comparison with the reference data, the PLANUI can make up the shortcoming of low resolution and the blooming effect of NTL effectively. (2) Compared with the VANUI, the PLANUI can significantly improve the accuracy of the urban built-up areas extracted and characterize urban features. (3) According to the results based on NPP/VIIRS and Luojia 1-01 images, the PLANUI has extensive applicability, both for regions with different degrees of economic development and NTL data with different resolutions. PLANUI can enhance the features of urban built-up areas with social sensing data and natural remote sensing data, which helps to weaken the NTL blooming effect and improve the extraction accuracy. PLANUI can provide an effective approach for urban built-up area extraction, which plays a certain guiding role for the study of urban structure, urban expansion, and urban planning and governance.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the National Oceanic and Atmospheric Administration-20 (NOAA-20) and the Suomi National Polar-orbiting Partnership Program (S-NPP) satellites were launched in late 2017 and 2011, respectively. This paper presents a recent update in the VIIRS thermal emissive bands (TEB) on-orbit calibration algorithm and inter-compares long-term instrument and TEB sensor data records (SDR) performances of the two VIIRS, to support user communities. The VIIRS TEB calibration algorithm was improved to mitigate calibration biases during the blackbody warm-up/cool-down (WUCD) events. Four WUCD bias correction methods were implemented in the NOAA operational processing in 2019: (1) the Nominal-F method, (2) the WUCD-C method, (3) the Ltrace method, and (4) the Ltrace-2 method. Our evaluation results indicate that the on-orbit performances of the two VIIRS instruments have been generally stable and comparable with each other, except that NOAA-20 VIIRS blackbody and instrument temperatures are lower than those of the S-NPP VIIRS. The degradations in the S-NPP TEB detector responsivities remain small after 9 years on-orbit. NOAA-20 detector responsivities have been generally stable after the longwave infrared degradation during its early mission was resolved by the mid-mission outgassing. NOAA-20 and S-NPP VIIRS TEB SDRs agree with co-located Cross-track Infrared Sounder observations, with daily averaged biases within 0.1 K at nadir. After the implementation of operational WUCD bias correction, residual TEB WUCD biases are similar for NOAA-20 and S-NPP, with daily averaged biases ~0.01 K in all bands.

The global spread of artificial light is eroding the natural night-time environment. The estimation of the pattern and rate of growth of light pollution on multi-decadal scales has nonetheless proven challenging. Here we show that the power of global satellite observable light emissions increased from 1992 to 2017 by at least 49%. We estimate the hidden impact of the transition to solid-state light-emitting diode (LED) technology, which increases emissions at visible wavelengths undetectable to existing satellite sensors, suggesting that the true increase in radiance in the visible spectrum may be as high as globally 270% and 400% on specific regions. These dynamics vary by region, but there is limited evidence that advances in lighting technology have led to decreased emissions.