Search Results (27)

When objects are obscured by shadows or dim surroundings, image quality is improved by fusing near-infrared and visible-light images. At night, when visible and NIR lights are insufficient, long-wave infrared (LWIR) imaging can be utilized, necessitating the attachment of a visible-light sensor to an LWIR camera to simultaneously capture both LWIR and visible-light images. This camera configuration enables the acquisition of infrared images at various wavelengths depending on the time of day. To effectively fuse clear visible regions from the visible-light spectrum with those from the LWIR spectrum, a multi-band fusion method is proposed. The proposed fusion process subsequently combines detailed information from infrared and visible-light images, enhancing object visibility. Additionally, this process compensates for color differences in visible-light images, resulting in a natural and visually consistent output. The fused images are further enhanced using a night-to-day image translation module, which improves overall brightness and reduces noise. This night-to-day translation module is a trained CycleGAN-based module that adjusts object brightness in nighttime images to levels comparable to daytime images. The effectiveness and superiority of the proposed method are validated using image quality metrics. The proposed method significantly contributes to image enhancement, achieving the best average scores compared to other methods, with a BRISQUE of 30.426 and a PIQE of 22.186. This study improves the accuracy of human and object recognition in CCTV systems and provides a potential image-processing tool for autonomous vehicles. Full article

We present a low-cost prototype of a visible and near-infrared (VIS-NIR) remote sensing platform, optimized to detect and characterize natural flaming fire fronts from airborne nighttime light (NTL) observations, and its radiometric calibration. It uses commercially available CMOS sensor cameras and filters with roughly 100 nm bandwidths to effectively discriminate burning biomass from other sources of NTL, a critical ability for wildfire monitoring near populated areas. Our filter choice takes advantage of the strong potassium line emission near 770 nm present in natural flaming. The calibrated cameras operate at 20 ms of exposure time and boast radiance measurements with a sensitivity floor, depending on the filter, in the range 3–5 $\times {10}^{-6}$ W m⁻² sr⁻¹ nm⁻¹ with uncertainties lower than 5% and dynamic ranges near 3000–4000. An additional exposure time with a tenth of the duration is calibrated and extends the dynamic range by a factor of 10. We show images of a spatially resolved fire front from an airborne observation of flaming biomass within this radiance range. Full article

Accurate cloud detection is critical for quantitative applications of satellite-based advanced imager observations, yet nighttime cloud detection presents challenges due to the lack of visible and near-infrared spectral information. Nighttime cloud detection using infrared (IR)-only information needs to be improved. Based on a collocated dataset from Fengyun-3D Medium Resolution Spectral Imager (FY-3D MERSI) Level 1 data and CALIPSO CALIOP lidar Level 2 product, this study proposes a novel framework leveraging Light Gradient-Boosting Machine (LGBM), integrated with grey level co-occurrence matrix (GLCM) features extracted from IR bands, to enhance nighttime cloud detection capabilities. The LGBM model with GLCM features demonstrates significant improvements, achieving an overall accuracy (OA) exceeding 85% and an F1-Score (F1) of nearly 0.9 when validated with an independent CALIOP lidar Level 2 product. Compared to the threshold-based algorithm that has been used operationally, the proposed algorithm exhibits superior and more stable performance across varying solar zenith angles, surface types, and cloud altitudes. Notably, the method produced over 82% OA over the cryosphere surface. Furthermore, compared to LGBM models without GLCM inputs, the enhanced model effectively mitigates the thermal stripe effect of MERSI L1 data, yielding more accurate cloud masks. Further evaluation with collocated MODIS-Aqua cloud mask product indicates that the proposed algorithm delivers more precise cloud detection (OA: 90.30%, F1: 0.9397) compared to that of the MODIS product (OA: 84.66%, F1: 0.9006). This IR-alone algorithm advancement offers a reliable tool for nighttime cloud detection, significantly enhancing the quantitative applications of satellite imager observations. Full article

Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHI_SWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article

The satellite imagery of nighttime lights (NTLs) has been studied to understand human activities, economic development, and more recently, the ecological impact of brighter night skies. The Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) offers perhaps the most advanced nighttime imaging capabilities to date, but its large pixel size and single band capture large-scale changes in NTL while missing granular but important details, such as lighting type and brightness. To better understand individual NTL sources in a region, the spectra of nighttime lights captured by the DLR Earth Sensing Imaging Spectrometer (DESIS) were extracted and compared against near-coincident VIIRS DNB imagery. The analysis shows that DESIS’s finer spatial and spectral resolutions can detect individual NTL locations and types beyond what is possible with the DNB. Extracted night light spectra, validated against ground truth measurements, demonstrate DESIS’s ability to accurately detect and identify narrow-band atomic emission lines that characterize the spectra of high-intensity discharge (HID) light sources and the broader spectral features associated with different light-emitting diode (LED) lights. These results suggest the possible application of using hyperspectral data from moderate-resolution sensors to identify lamp construction details, such as illumination source type and light quality in low-light contexts. NTL data from DESIS and other hyperspectral sensors may improve the scientific understanding of light pollution, lighting quality, and energy efficiency by identifying, evaluating, and mapping individual and small groups of light sources. Full article

For the first time, nighttime VIS/NIR—SWIR (visible and near-infrared—shortwave infrared) spectra from a satellite mission have been analyzed using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. This article focuses on the spectral characteristics. Firstly, we checked the spectral calibration of EnMAP using sodium light emissions. Here, By applying a newly devised general method, we estimated shifts of $+0.3$nm for VIS/NIR and $-0.2$nm for SWIR; the uncertainties were found to be within the range of $[-0.4\mathrm{nm},+0.2\mathrm{nm}]$ for VIS/NIR and $[-1.2\mathrm{nm},+1.0\mathrm{nm}]$ for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Secondly, by employing a straightforward general method, we identified the dominant lighting types and thermal emissions in Las Vegas, Nevada, USA, on a per-pixel basis, and we considered the consistency of the outcomes. The identification and mapping of different types of LED (light-emitting diode) illuminations were achieved—with 75% of the identified dominant lighting types identified in VIS/NIR—as well as high- and low-pressure sodium and metal halide, which made up 22% of the identified dominant lighting types in VIS/NIR and 29% in SWIR and other illumination sources, as well as high temperatures, where 33% of the identified dominant emission types in SWIR were achieved from space using EnMAP due to the elevated illumination levels in the observed location. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products; moreover, they support the specification of spectral characteristics for upcoming nighttime missions. Full article

Rapid and accurate identification of precipitation clouds from satellite observations is essential for the research of quantitative precipitation estimation and precipitation nowcasting. In this study, we proposed a novel Convolutional Neural Network (CNN)-based algorithm for precipitation cloud identification (PCINet) in the daytime, nighttime, and nychthemeron. High spatiotemporal and multi-spectral information from the Fengyun-4A (FY-4A) satellite is utilized as the inputs, and a multi-scale structure and skip connection constraint strategy are presented in the framework of the algorithm to improve the precipitation cloud identification. Moreover, the effectiveness of visible/near-infrared spectral information in improving daytime precipitation cloud identification is explored. To evaluate this algorithm, we compare it with five other deep learning models used for image segmentation and perform qualitative and quantitative analyses of long-time series using data from 2021. In addition, two heavy precipitation events are selected to analyze the spatial distribution of precipitation cloud identification. Statistics and visualization of the experiment results show that the proposed model outperforms the baseline models in this task, and adding visible/near-infrared spectral information in the daytime can effectively improve model performance. More importantly, the proposed model can provide accurate and near-real-time results, which has important application in observing precipitation clouds. Full article

Small uncrewed aerial systems (sUASs) have the potential to serve as ideal platforms for high spatial and temporal resolution wildfire measurements to complement aircraft and satellite observations, but typically have very limited payload capacity. Recognizing the need for improved data from wildfire management and smoke forecasting communities and the potential advantages of sUAS platforms, the Nighttime Fire Observations eXperiment (NightFOX) project was funded by the US National Oceanic and Atmospheric Administration (NOAA) to develop a suite of miniaturized, relatively low-cost scientific instruments for wildfire-related measurements that would satisfy the size, weight and power constraints of a sUAS payload. Here we report on a remote sensing system developed under the NightFOX project that consists of three optical instruments with five individual sensors for wildfire mapping and fire radiative power measurement and a GPS-aided inertial navigation system module for aircraft position and attitude determination. The first instrument consists of two scanning telescopes with infrared (IR) channels using narrow wavelength bands near 1.6 and 4 µm to make fire radiative power measurements with a blackbody equivalent temperature range of 320–1500 °C. The second instrument is a broadband shortwave (0.95–1.7 µm) IR imager for high spatial resolution fire mapping. Both instruments are custom built. The third instrument is a commercial off-the-shelf visible/thermal IR dual camera. The entire system weighs about 1500 g and consumes approximately 15 W of power. The system has been successfully operated for fire observations using a Black Swift Technologies S2 small, fixed-wing UAS for flights over a prescribed grassland burn in Colorado and onboard an NOAA Twin Otter crewed aircraft over several western US wildfires during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field mission. Full article

The quality of urban spaces is a pivotal part of high-quality spatial development. It is directly connected to the comprehensive, coordinated and sustainable development of a region. In recent years, Northeast China has characterized urban space contraction and development. To study the quality of urban space in Northeast China, this paper fitted the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data with 11 indicators related to high-quality urban development for the period 1992–2018. The feasibility of nighttime light data reflecting urban spatial quality was verified by a linear equation, and the temporal characteristics of urban spatial quality in Northeast China were obtained. The Exploratory Spatial Data Analysis Geographically and Temporally Weighted Regression (ESDA-GTWR) explores the spatial relevance and possible influencing factors of this kind of development. The results suggest that the overall trend of spatial quality in the three northeastern provinces is “initial slow growth and significantly weakened after”. The fast developing cities include Panjin, Liaoyang, Shenyang, and Dalian in the Liaoning Province. On the other hand, cities such as Heihe and Yichun in the Heilongjiang Province have relatively slow development speeds. Furthermore, the spatial quality development in the three northeastern provinces exhibits a trend of continuous concentration. The cities with high spatial qualities are concentrated near the Liaoning Province, with low spatial qualities in the north and high spatial qualities in the southern parts of the three provinces. As there is a notable gap between the northern and the southern regions, the central region represents an area in partial transition. The spatial quality of each city in the three northeastern provinces is the result of a number of intertwined factors, with significant differences in the degree of their influence. The significant degree of influence factors on spatial quality from higher to lower is urbanization, quality of life, rural revitalization, government promotion, and infrastructure. Full article

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era. Full article

In this study, we present a wideband image synthesis technique for day and night object identification. To synthesize the visible and near-infrared images, a base component and a detailed component are first decomposed using a bilateral filter, and the detailed component is synthesized using a local variance map. In addition, considering the difference in the near-infrared image characteristics between daytime and nighttime, the base components are synthesized using a luminance saturation region map and depth and penetration map using a joint bilateral filter. The proposed method overcomes the partial over- or under-exposure caused by sunlight and infrared auxiliary light, which is experienced variously in wideband imaging, and improves the identification of objects in various indoor and outdoor images compared with that achieved by existing methods by emphasizing detailed components. Full article

Ultraviolet-visible-near infrared broadband photodetectors have significant prospects in many fields such as image sensing, communication, chemical sensing, and day and nighttime surveillance. Hybrid one-dimensional (1D) and zero-dimensional (0D) materials are attractive for broadband-responsive photodetectors since its unique charges transfer characteristics and facile fabrication processes. Herein, a Si/ZnO nanowires/Ge quantum dots photodetector has been constructed via processes that combined electrospinning and spin-coating methods. A broadband response behavior from ultraviolet to near-infrared (from 250 to 1550 nm) is observed. The responsivity of the hybrid structure increases around three times from 550 to 1100 nm compared with the pure Si photodetector. Moreover, when the photodetector is illuminated by a light source exceeding 1100 nm, such as 1310 and 1550 nm, there is also a significant photoresponse. Additionally, the ZnO NWs/Ge quantum dots heterostructure is expected to be used in flexible substrates, which benefits from electrospinning and spin-coating processes. The strategy that combines 1D ZnO NWs and 0D solution-processed Ge QDs nanostructures may open a new avenue for flexible and broadband photodetector. Full article

The physiology and behavior of most life at or near the Earth’s surface has evolved over billions of years to be attuned with our planet’s natural light–dark cycle of day and night. However, over a relatively short time span, humans have disrupted this natural cycle of illumination with the introduction and now widespread proliferation of artificial light at night (ALAN). Growing research in a broad range of fields, such as ecology, the environment, human health, public safety, economy, and society, increasingly shows that ALAN is taking a profound toll on our world. Much of our current understanding of light pollution comes from datasets generated by remote sensing, primarily from two missions, the Operational Linescan System (OLS) instrument of the now-declassified Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense and its follow-on platform, the Day-Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar-Orbiting Partnership satellite. Although they have both proved invaluable for ALAN research, sensing of nighttime lights was not the primary design objective for either the DMSP-OLS or VIIRS-DNB instruments; thus, they have some critical limitations. Being broadband sensors, both the DMSP-OLS and VIIRS-DNB instruments suffer from a lack of spectral information. Additionally, their spatial resolutions are too low for many ALAN research applications, though the VIIRS-DNB instrument is much improved over the DMSP-OLS in this regard, as well as in terms of dynamic range and quantization. Further, the very late local time of VIIRS-DNB observations potentially misses the true picture of ALAN. We reviewed both current literature and guiding advice from ALAN experts, aggregated from a diverse range of disciplines and Science Goals, to derive recommendations for a mission to expand knowledge of ALAN in areas that are not adequately addressed with currently existing orbital missions. We propose a stand-alone mission focused on understanding light pollution and its effects on our planet. Here we review the science cases and the subsequent mission recommendations for NITESat (Nighttime Imaging of Terrestrial Environments Satellite), a dedicated ALAN observing mission. Full article

In this study, a technique facilitating the enhanced detection of airborne volcanic ash (VA) has been developed, which is based on the use of visible (VIS), near-infrared (NIR), and infrared (IR) bands by meteorological satellite systems. Channels with NIR and IR bands centered at ~3.8, 7.3, 8.7, 10.5, and 12.3 μm are utilized, which enhances the accuracy of VA detection. The technique is based on two-band brightness temperature differences (BTDs), two-band brightness temperature ratios (BTRs), and background image BTDs. The physical effects of the observed BTDs and BTRs, which can be used to distinguish VA from meteorological clouds based on absorption differences, depend on the channel and time of day. The Advanced Meteorological Imager onboard the GEOKOMPSAT-2A (GK-2A) satellite has several advantages, including the day- and nighttime detection of land and ocean. Based on the GK-2A data on several volcanic eruptions, multispectral data are more sensitive to volcanic clouds than ice and water clouds, ensuring the detection of VA. They can also be used as an input to provide detailed information about volcanoes, such as the height of the VA layer and VA mass. The GK-2A was optimized, and an improved ash algorithm was established by focusing on the volcanic eruptions that occurred in 2020. In particular, the 3.8 μm band was utilized, the threshold was changed by division between day and night, and efforts were made to reduce the effects of clouds and the discontinuity between land and ocean. The GK-2A imagery was used to study volcanic clouds related to the eruptions of Taal, Philippines, on 12 January and Nishinoshima, Japan, from 30 July–2 August to demonstrate the applicability of this product during volcanic events. The improved VA product of GK-2A provides vital information, helping forecasters to locate VA as well as guidance for the aviation industry in preventing dangerous and expensive interactions between aircrafts and VA. Full article

Contrary to its daytime counterpart, nighttime visible and near infrared (VIS/NIR) satellite imagery is limited in both spectral and spatial resolution. Nevertheless, the relevance of such systems is unquestioned with applications to, e.g., examine urban areas, derive light pollution, and estimate energy consumption. To determine optimal spectral bands together with required radiometric and spatial resolution, at-sensor radiances are simulated based on combinations of lamp spectra with typical luminances according to lighting standards, surface reflectances, and radiative transfers for the consideration of atmospheric effects. Various band combinations are evaluated for their ability to differentiate between lighting types and to estimate the important lighting parameters: efficacy to produce visible light, percentage of emissions attributable to the blue part of the spectrum, and assessment of the perceived color of radiation sources. The selected bands are located in the green, blue, yellow-orange, near infrared, and red parts of the spectrum and include one panchromatic band. However, these nighttime bands tailored to artificial light emissions differ significantly from the typical daytime bands focusing on surface reflectances. Compared to existing or proposed nighttime or daytime satellites, the recommended characteristics improve, e.g., classification of lighting types by >10%. The simulations illustrate the feasible improvements in nocturnal VIS/NIR remote sensing which will lead to advanced applications. Full article