Search

Under special conditions such as night, rain, and fog, visible light imaging technology often performs poorly, while radar and other imaging technologies are limited due to their high costs. Infrared imaging technology, based on the principle of thermal radiation, can provide clear imaging effects under these extreme conditions and has a relatively low cost of use. Therefore, it has been widely applied in various fields, including military, medical, industrial, and agricultural applications. However, due to the limitations of infrared wavelengths and imaging technology, traditional infrared imaging devices struggle to capture rich texture information, leading to infrared images that lack texture details and have low resolution, which significantly restricts the further research and application of infrared imaging technology in various fields. In recent years, with the widespread attention to infrared imaging technology, researchers have proposed various new infrared image enhancement techniques. Despite this, the lack of texture information in ordinary infrared images leads to the enhancement effect of existing technologies being unsatisfactory. Therefore, we have conducted a systematic investigation of the research advancements in the field of infrared image enhancement, encompassing infrared image enhancement methods, related datasets, and evaluation metrics, with the aim of exploring a research solution that could potentially break through current technological limitations. Based on these investigations, we have evaluated the performance of various representative infrared image enhancement methods, with the hope of providing a reference for future research. In addition, this article also provides a comprehensive introduction to the potential applications of infrared image enhancement technology and discusses significant research directions for the future.

Nonmethane volatile organic compounds (NMVOCs) emitted in excess from anthropogenic sources significantly contribute to the formation of harmful pollutants, thereby degrading air quality. While satellite measurements have become valuable tools for tracking anthropogenic emitters, they have primarily targeted inorganic species and methane (CH4 ${\text{CH}}_{4}$). This study demonstrates the potential of infrared atmospheric sounding interferometers (IASI) to detect anthropogenic NMVOC point sources on a global scale. Using an advanced oversampling technique, we enhance the spatial resolution of IASI measurements to identify emitters of three major NMVOCs: methanol (CH3 ${\text{CH}}_{3}$OH), acetylene (C2H2 ${\mathrm{C}}_{2}{\mathrm{H}}_{2}$), and propylene (C3H6 ${\mathrm{C}}_{3}{\mathrm{H}}_{6}$). These point sources are primarily associated with chemical and petrochemical facilities, coal‐burning activities, metallurgy, pharmaceutical manufacturing sites, and megacities. We also highlight the value of combining IASI measurements of NMVOCs with those of the inorganic species, such as sulfur dioxide (SO2 ${\text{SO}}_{2}$) and ammonia (NH3 ${\text{NH}}_{3}$), to aid in the identification of anthropogenic point sources. Plain Language Summary: Human activities release various gases into the atmosphere, including organic (carbon‐based) gases, which contribute to the creation of pollutants that can be harmful to both the environment and public health. While satellite measurements increasingly monitor gas emissions from specific human‐related sources, they have predominantly focused on inorganic pollutants, like sulfur dioxide (SO2 ${\text{SO}}_{2}$), ammonia (NH3 ${\text{NH}}_{3}$), and methane (CH4 ${\text{CH}}_{4}$). This study explores the potential of satellite observations from infrared atmospheric sounding interferometers (IASI) to detect human‐made sources of organic gases globally. Utilizing a specialized technique to increase the spatial resolution of the satellite data, we pinpoint local emitters of three significant organic species: methanol (CH3 ${\text{CH}}_{3}$OH), acetylene (C2H2 ${\mathrm{C}}_{2}{\mathrm{H}}_{2}$), and propylene (C3H6 ${\mathrm{C}}_{3}{\mathrm{H}}_{6}$). These emitters correspond to chemical and petrochemical plants, coal‐burning operations, metal production facilities, pharmaceutical manufacturing sites, and large urban areas. Additionally, combining IASI organic gas data with measurements of SO2 ${\text{SO}}_{2}$ and NH3 ${\text{NH}}_{3}$ enhances our ability to detect and identify emission point sources. Key Points: Oversampled IASI satellite data enable the identification of large anthropogenic point sources of CH3OH, C2H2, and C3H6 worldwideIdentified emitters correspond to petrochemistry, coal exploitation, metallurgy, pharmaceutical manufacturing, and megacitiesCombining IASI NMVOC data with inorganic pollutants SO2 and NH3 aids in identifying emission sources [ABSTRACT FROM AUTHOR]

During the process of infrared remote sensing monitoring, obtaining real-time measurements of sky background radiation is extremely inconvenient. The current methods incur a certain amount of lag. In this study, within the existing theoretical framework, a fast transmittance calculation method using interpolation was adopted, and a simplified transmission model was established. This led to the development of a new and simplified method for rapid temperature and humidity retrieval. Compared to the line-by-line integration method, the interpolation method significantly improves the speed of transmittance calculation by several tens of times, while maintaining a high level of accuracy. The relative deviation between the results obtained using the interpolation method and those obtained through line-by-line integration is less than 1 ‱. With the proposed method, temperature and humidity profile information can be retrieved from measured spectra within 5 min and corresponding background spectra can be obtained. The differences between the calculated background radiation and the measured spectra using the new method are smaller, making it more suitable for calculating sky background radiation. Additionally, the rapid retrieval results of the temperature profiles in the lower atmosphere have a certain level of accuracy (the mean deviation is less than 2 K). [ABSTRACT FROM AUTHOR]

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens' theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. [ABSTRACT FROM AUTHOR]

During the process of infrared remote sensing monitoring, obtaining real-time measurements of sky background radiation is extremely inconvenient. The current methods incur a certain amount of lag. In this study, within the existing theoretical framework, a fast transmittance calculation method using interpolation was adopted, and a simplified transmission model was established. This led to the development of a new and simplified method for rapid temperature and humidity retrieval. Compared to the line-by-line integration method, the interpolation method significantly improves the speed of transmittance calculation by several tens of times, while maintaining a high level of accuracy. The relative deviation between the results obtained using the interpolation method and those obtained through line-by-line integration is less than 1 ‱. With the proposed method, temperature and humidity profile information can be retrieved from measured spectra within 5 min and corresponding background spectra can be obtained. The differences between the calculated background radiation and the measured spectra using the new method are smaller, making it more suitable for calculating sky background radiation. Additionally, the rapid retrieval results of the temperature profiles in the lower atmosphere have a certain level of accuracy (the mean deviation is less than 2 K).

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP50 of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

• Studied the impact of coating emissivity, base-height ratio, and reflection characteristics on the performance of plane blackbodies with various pyramid array structures. • Investigated various pyramid array structures, analysed their effects on emissivity, and evaluated the temperature field distributions of radiation surfaces with different blackbodies. • Emphasized the optimization of surface structures and the use of high-emissivity coatings to significantly improve the radiation capabilities of plane blackbodies. • Developed a light transmission model using the Monte Carlo Method to simulate emissivity and conducted experimental validations with fabricated specimens, demonstrating strong alignment with the simulation results. • Provided crucial data and insights for exploring the plane blackbody emissivity of different array structures and coating materials. The plane blackbody radiation source is pivotal to the infrared remote sensing detection system, with emissivity serving as a critical metric for its performance evaluation. This metric is predominantly influenced by the surface structure and coating properties. To improve emissivity, existing works have utilized pyramid structures to expand the contact area between the radiation surface and light, thereby augmenting light absorption. Nonetheless, there is relatively little research exploring the impact of pyramid structure on emissivity, leading to a scarcity of related emissivity data for plane blackbody. In this paper, we explored the impact on the blackbody emissivity induced by different pyramid structures, including the triangular pyramid array (TPA), the quadrangular pyramid array (QPA), the pentagonal pyramid array (PPA), and the hexagonal pyramid array (HPA). Also, the effects involving coating properties, the base-height ratio of the pyramid, and reflection characteristics on emissivity were investigated based on the Monte Carlo Method. Then, the specimens with diffuse reflection (DR) and near-specular reflection (NSR) were fabricated and tested experimentally. The uncertainties of simulation and experiment were maintained below 0.21 % and 0.50 %, respectively. Experimental results are well aligned with the corresponding simulation results, indicating that the normal emissivity of the TPA blackbody is 0.12 % higher than the QPA blackbody, and the directional emissivity uniform of the TPA blackbody is superior to that of the QPA blackbody for the DR model in the 8–14 μm waveband. Furthermore, the temperature field distributions of radiation surface for TPA and QPA blackbody were analysed, noting a maximum temperature difference of 50 mK and 30 mK, respectively. This paper provides experimental support and design references for further improving the emissivity of plane blackbodies. [ABSTRACT FROM AUTHOR]

Because of the calibration requirements of large-area infrared remote-sensing equipment, a large-area blackbody radiation source with the area of 1.05 m × 1.05 m is developed at the National Institute of Metrology (NIM), China. The temperature range of the blackbody is from room temperature to 573 K. A multiple temperature control technique is employed for improving the temperature stability and uniformity of the blackbody. The effects of the heating zone and material thickness on the temperature uniformity are investigated by thermodynamic simulation, and then the uniformity and stability of the blackbody temperature are tested. Retrieval results illustrate that the temperature uniformity of the blackbody radiation source system is less than ± 0.65 K. The temperature stability of the blackbody at all temperature points is less than 0.1 K in 10 min. The expanded combined standard uncertainty of the blackbody is 0.97 K at 373 K, 1.13 K at 473 K, and 1.53 K at 573 K. [ABSTRACT FROM AUTHOR]

• A YOLO-SR for infrared small target detection is proposed. • A bottleneck transformer block (BTB) is proposed to capture long-range dependencies in infrared images. • A C3-Neck module is proposed to fuse spatial and channel information. Infrared remote sensing imaging plays a crucial role in military observation, nighttime security surveillance, forest fire monitoring, and so on. In these applications, detecting dim small targets has always been a challenging problem, especially in complex backgrounds and low-contrast conditions. Existing model-driven methods usually lack robustness in handling noise and small-size targets. Deep learning-based approaches are heavily dependent on data and have limitations in feature processing and fusion, leading to missed detections and false alarms. To address these issues, we propose a small target detection method for infrared images with image super-resolution technology and deep learning. Firstly, we apply super-resolution image preprocessing and multiple data augmentation to the input infrared images. Secondly, we develop a deep-learning network based on YOLO called YOLO-SR, which incorporates a bottleneck transformer block after the spatial pyramid pooling module in the backbone layer to capture long-range dependencies in the infrared images. We design a C3-Neck module in the neck layer to better extract and fuse spatial and channel information. Experimental results show that the proposed method achieves mAP@0.5 scores of 95.2% on the public datasets and effectively addresses the issues of missed detections and false alarms compared to current state-of-the-art data-driven detection methods. [ABSTRACT FROM AUTHOR]

The eruption of Hunga Tonga–Hunga Ha'apai (Tonga for short) is of significant interest, particularly due to the impact of sulfate aerosols on stratospheric chemistry. We report on ACE-FTS measurements of changes in the abundances of stratospheric molecules post-Tonga: 2022–Aug 2023. Observed variations in ozone levels within the Southern Hemisphere show evidence of being influenced by competing NO x , ClO x and HO x catalytic cycles. Analysis of H 2 O concentration and 2H abundance in ACE data shows the Tonga water plume's progression southward. The excess water from Tonga induces changes in stratospheric chemistry and dynamics. The extensive array of molecules, including their associated isotopologues, measured by ACE-FTS data enables a more comprehensive analysis of atmospheric changes. • Measurements of H2O isotopologues indicate an abundance of HDO in the H2O plume. • Changes in multiple molecules align with latitudinal progression of the Tonga plume. • Ozone levels in the Southern Hemisphere showed fluctuations over a range of altitudes. • Perturbations occurred in ClOx, NOx, and HOx catalytic cycles in the Tonga plume. [ABSTRACT FROM AUTHOR]

Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction. [ABSTRACT FROM AUTHOR]

Low signal-to-noise ratio (SNR) infrared point target detection and tracking is crucial to study regarding infrared remote sensing. In the low-SNR images, the intensive noise will submerge targets. In this letter, a saliency-guided double-stage particle filter (SGDS-PF) formed by the searching particle filter (PF) and tracking PF is proposed to detect and track targets. Before the searching PF, to suppress noise and enhance targets, the single-frame and multi-frame target accumulation methods are introduced. Besides, the likelihood estimation filter and image block segmentation are proposed to extract the likelihood saliency and obtain proper proposal density. Guided by this proposal density, the searching PF detects potential targets efficiently. Then, with the result of the searching PF, the tracking PF is adopted to track and confirm the potential targets. Finally, the path of the real targets will be output. Compared with the existing methods, the SGDS-PF optimizes the proposal density for low-SNR images. Using a few accurate particles, the searching PF detects potential targets quickly and accurately. In addition, initialized by the searching PF, the tracking PF can keep tracking targets using very few particles even under intensive noise. Furthermore, the parameters have been selected appropriately through experiments. Extensive experimental results show that the SGDS-PF has an outstanding performance in tracking precision, tracking reliability, and time consumption. The SGDS-PF outperforms the other advanced methods. [ABSTRACT FROM AUTHOR]

In 2000 at the first AGU session in this decadal series, and a year after the launch of the Terra satellite as part of the NASA Earth Observing System (EOS), we discussed the prospects for infrared (IR) data to advance volcano science. There were high expectations despite none of the sensors on Terra (or any of the future EOS missions) being designed specifically for volcanology. Ten years later, we focused on the IR data from those sensors and their added value to high-profile eruptions. We also looked forward to 2020 and the promise of integrated networks of ground and orbital IR instruments operating in sensor webs to improve near real-time data collection critical for eruption forecasting and response. By 2010, the EOS-era sensors had provided a long-term record of volcanic activity; however, most were well past their design lifetimes. We noted, "The prospects for accurate volcanology from space in 2020 are therefore less clear, even if the need for such observations has never been more important," which arguably proved true. At the 2020 session, we argued that a paradigm-shift in spaceborne volcanology will only come about if a dedicated orbital IR volcano observatory is launched in the next decade. Critically, the observatory needs to acquire hypertemporal IR data with the improved spectral and spatial resolutions required to analyze ongoing dynamic processes such as the mass flux rates feeding plumes and flows. This mission concept has been proposed and, if selected, would see science returned in time for the next AGU volcanology session in 2030. [ABSTRACT FROM AUTHOR]

The brightness temperature of the Venus disk obtained by Longwave Infrared Camera (LIR) on board Akatsuki shows clear limb darkening at low and middle latitudes. The profile of limb brightness reflects the vertical distributions of atmospheric temperature and the optical thickness of the cloud particles. Horizontal distributions of brightness temperature obtained by LIR during ∼5.8 Venusian years were analyzed to investigate the vertical structure of the brightness temperature distribution above the cloud tops based on the emission angle dependence of the sensing altitude. Emission angles were converted to sensing altitudes by a radiative transfer calculation with nominal temperature and cloud particle distributions based on past observations. We show a local time‐altitude cross section of the brightness temperature deviation above the cloud tops for three latitudinal zones. The derived vertical amplitude distribution of the diurnal and semidiurnal tides above ∼68 km is mostly explained by the classical theory of thermal tides. A semidiurnal tide in which the phase shifts upstream with altitude is clearly seen in the equatorial region. By applying the dispersion relation of the internal gravity wave to the observed wave structure, it was found that the zonally averaged zonal wind velocity at altitudes of 66–71 km was approximately the same as the known superrotation velocity. By comparing the observed and simulated vertical phase structures, it is suggested that the tidal wave structure seen in the equatorial cloud tops is an aspect of upward propagation of a gravity wave generated in the upper cloud layer by solar heating. Plain Language Summary: The Japanese Venus orbiter Akatsuki is a meteorological satellite photographing Venus at multiple wavelengths. One of the cameras on board Akatsuki act as a non‐contact infrared radiation thermometer that can measure the temperature distribution at the tops of the thick cloud layer of sulfuric acid that covers the entire globe of Venus. The dependence of sensing depth in the cloud layer on the slant angle at which the camera sees the cloud tops enables us to obtain a vertical distribution of temperature in the cloud layer. We analyzed the data obtained by the infrared camera during 5.8 Venusian years to depict average altitude‐local time cross sections of the temperature distribution around the cloud‐top altitudes in three latitudinal zones. Thermal tides, which are waves generated in the atmosphere by solar radiation heating, are clearly seen in the cross sections. The observed amplitude profiles of the thermal tides agree with the prediction by the classical theory of thermal tides. By comparing the thermal tides reproduced by a numerical simulation with the observed thermal tides, we deduced the altitude range of the solar heating deep in the cloud layer. Key Points: Thermal tides around the cloud tops of Venus are studied using a three‐dimensional temperature distribution obtained by AkatsukiThe observed diurnal and semidiurnal tides are consistent with past observations and numerical simulationsThe thermal tides are most likely generated at altitudes of 60–70 km, where most of the solar power would be deposited [ABSTRACT FROM AUTHOR]

Maps of plagioclase, olivine, and pyroxene at 1 km resolution are derived from a combination of data from the Diviner Lunar Radiometer on the Lunar Reconnaissance Orbiter and the Kaguya Multiband Imager. The Diviner instrument features three infrared bands designed to characterize a spectral feature of lunar soils that is sensitive to the average silica polymerization of the surface called the Christiansen Feature, which is directly sensitive to the presence of plagioclase, the dominant lunar silicate. Existing global mineral maps based on near‐IR data largely infer the presence of plagioclase from the bright mineral's effect on total reflectance, excepting in rare locations where the surface is nearly pure plagioclase and a weak feature in the plagioclase near‐IR spectrum can be relied upon. By integrating both wavelength regions we produced more robust estimates of the abundance of the three dominant minerals. In the process of this work, we also improved the removal of space weathering effects from Christiansen Feature maps, and showed that silica rich compositional anomalies could be reliably detected by decorrelating Christiansen Feature and FeO maps. New silica‐rich locations are reported as are the global abundances of the three major silicates. Plain Language Summary: One of the goals of remote sensing of the Moon is to produce maps of the minerals present on its surface. In this paper, we bring together two infrared spectroscopic data sets to create maps of the minerals plagioclase, pyroxene, and olivine at a resolution of 1 km. One of these infrared data sets defines the wavelength position of a spectral phenomenon called the Christiansen Feature that is sensitive to the presence of unusual silica‐rich minerals that indicate a relatively rare but widespread style of lunar volcanism. Using these new data, we find a few previously unrecognized silica‐rich exposures that extend the spatial range of these features. The Christiansen Feature is also sensitive to rare rock types that may represent outcrops of the lunar mantle at the surface of the Moon, and a promising candidate is revealed at the farside crater Titov. Key Points: The global abundance of plagioclase, pyroxene and olivine are presentedThe effects of space weathering on Christiansen Feature maps are removed with an improved algorithmNew silica‐rich locations are identified in the Oceanus Procellarum region [ABSTRACT FROM AUTHOR]

Abundant infrared remote sensing images and advanced information processing technologies are used to predict earthquakes. However, most studies only use single long-wave infrared data or its products, and the accuracy of prediction is not high enough. To solve this problem, this paper proposes a statistical method based on connected domain recognition to analyze multi-channel anomalies. We extract pre-seismic anomalies from multi-channel infrared remote sensing images using the relative power spectrum, then calculate positive predictive values, true positive rates and probability gains in different channels. The results show that the probability gain of the single-channel prediction method is extremely low. The positive predictive value of four-channel anomalies is 41.94%, which is higher than that of single-channel anomalies with the same distance threshold of 200 km. The probability gain of the multi-channel method is 2.38, while that of the single-channel method using the data of any channel is no more than 1.26. This study shows the advantages of the multi-channel method to predict earthquakes and indicates that it is feasible to use multi-channel infrared remote sensing images to improve the accuracy of earthquake prediction.

Flow in rivers and the coastal ocean is controlled by the frictional force exerted on the water by riverbed or seabed roughness. The frictional force is typically characterized by a drag coefficient Cd, which is estimated from bulk measurements and often assumed constant. Here, we demonstrate a relationship between bed roughness and water surface turbulence that can be used to make remote estimates of Cd. We observe that regions with larger bed roughness result in greater turbulent kinetic energy (TKE), which is transported upward by river boils to the water surface. We present a relationship between surface TKE and Cd, and validate this relationship by comparing remotely sensed estimates of Cd to those from in situ measurements. Thus, our results provide an approach for estimating bottom roughness and Cd based entirely on remotely sensed data, including their spatial variability, which can improve modeling of river discharge and morphodynamics in data‐poor regions. Plain Language Summary: Water flow in rivers, estuaries and shallow coastal areas is controlled by the roughness of the riverbed or seabed. Flow over rocks, sand grains, or sand dunes on the bottom becomes turbulent and the turbulence spreads upwards to the surface. In a river, the largest scales of turbulence take the form of boils, and can be observed on the surface with an infrared camera. We observe stronger boils over areas of the river where the bed roughness is high. Here, we use an empirical equation of open channel flow to relate the boils observed on the water surface to the riverbed roughness and drag coefficient. Our results demonstrate that information about bottom roughness propagates upwards through the water column and therefore the drag coefficient can be estimated from remotely sensed videos of the water surface. The mathematical connection we derived between the drag coefficient and surface turbulence will be useful for improving river discharge and gas exchange models. Key Points: We derived an equation for the drag coefficient based on turbulence at the water surfaceRemotely sensed surface turbulent kinetic energy is used to estimate the riverbed drag coefficientThese are the first remote measurements of the riverbed drag coefficient that have been validated with in situ measurements [ABSTRACT FROM AUTHOR]

• A high-precision ML-based SST retrieval framework is proposed for offshore waters. • The selection of features should be strictly optimized during model construction. • Adding time-related features and initial temperature improves the model accuracy. • RF outperforms the traditional physical model and DNN in SST retrieval accuracies. • The proposed framework based on RF proved to be portable for independent samples. High-precision sea surface temperature (SST) data is essential for monitoring and management of offshore environment. SST retrievals from thermal infrared remote sensing have the advantages in time frequency and space coverage over measurements from ships and buoys. However, traditional SST retrieval models are not fully applicable for offshore waters where seawater conditions change a lot, due to simple functional forms and empirical fitting of model parameters. Machine learning (ML) is introduced because it doesn't need to define specific model rules or unknown parameters, which is more intelligent to deal with complex retrieval problems in a data-driven way. But the applicability of ML has not been investigated in the North Yellow Sea of China, and the selection of driving features for model construction has not been fully discussed. Here, we designed a high-precision SST retrieval framework based on random forest (RF) with optimal feature combination using MODIS data and in-situ SST measurements during 2013–2020 in the northern part of the North Yellow Sea. The performances of different feature combinations were assessed based on model testing accuracies and the RF output feature importance. Compared with previous ML-based SST retrieval studies, we added two time-related features (i.e., day of the year and month) and one feature as initial temperature (i.e., SST retrieval from split-window algorithm), which reduced the testing error by around 15 % and 11 %, respectively. The retrieval results were validated with in-situ measurements, showing that the RF model achieved better accuracies (R2 = 0.987, SD = 0.842 °C, MAE = 0.675 °C, RMSE = 0.841 °C) than the improved split-window algorithm (named as ISW) and deep neural network. Compared with ISW, the spatial distribution of RF retrieval map exhibited similar variation across four seasons, mainly differed in the coastal areas or at the formation of temperature fronts. Note that the constructed RF model was still reliable when applied to the independent samples in 2021. This study contributes to improve the accuracy of offshore SST retrieval under different seawater conditions and provides references for SST monitoring. [ABSTRACT FROM AUTHOR]

We describe NASA’s Applied Sciences Disasters Program, which is a collaborative project between the Direct Readout Laboratory (DRL), ozone processing team, Jet Propulsion Laboratory, Geographic Information Network of Alaska (GINA), and Finnish Meteorological Institute (FMI), to expedite the processing and delivery of direct readout (DR) volcanic ash and sulfur dioxide (SO2) satellite data. We developed low-latency quantitative retrievals of SO2 column density from the solar backscattered ultraviolet (UV) measurements using the Ozone Mapping and Profiler Suite (OMPS) spectrometers as well as the thermal infrared (TIR) SO2 and ash indices using Visible Infrared Imaging Radiometer Suite (VIIRS) instruments, all flying aboard US polar-orbiting meteorological satellites. The VIIRS TIR indices were developed to address the critical need for nighttime coverage over northern polar regions. Our UV and TIR SO2 and ash software packages were designed for the DRL’s International Planetary Observation Processing Package (IPOPP); IPOPP runs operationally at GINA and FMI stations in Fairbanks, Alaska, and Sodankylä, Finland. The data are produced within 30 min of satellite overpasses and are distributed to the Alaska Volcano Observatory and Anchorage Volcanic Ash Advisory Center. FMI receives DR data from GINA and posts composite Arctic maps for ozone, volcanic SO2, and UV aerosol index (UVAI, proxy for ash or smoke) on its public website and provides DR data to EUMETCast users. The IPOPP-based software packages are available through DRL to a broad DR user community worldwide.

Developing countries with a paucity of direct rainfall measuring systems on the ground, satellite remote sensing is increasingly being relied upon as a means through which national governmental response and future strategies to manage natural disasters, such as drought and flooding are decided upon. The aim of this study was to assess the performance of the Tropical Applications of Meteorology Using Satellite Data and Ground-Based Observations (TAMSAT) monthly rainfall dataset for southern Africa by comparing it to two widely used stable global rainfall datasets, the Global Precipitation Climatology Centre (GPCC) and the Global Precipitation Climatology Project (GPCP) datasets. Using rainfall records for the period 1984–2010, four statistical measures namely, the correlation coefficient, the Root Mean Square Error (RMSE), relative bias, mean absolute deviation and the Nash Sutcliffe Efficiency index were calculated to facilitate pair-wise comparisons. This inter-product comparison study found a good agreement between TAMSAT satellite rainfall and the two global rainfall products with regard to the spatial and temporal representation of monthly rainfall over the region. However, it was found that TAMSAT consistently provides lower monthly rainfall estimates compared to both GPCC and GPCP rainfall estimates over southern Africa. The relative bias between TAMSAT and the two global rainfall products (GPCC and GPCP) was much lower (≤10%) during the austral summer months (November–March) compared to winter when the relative bias surpassed 70%. These findings indicate TAMSAT is geared more towards convective precipitation brought about by the inter-tropical convergence zone in summer. The observed large discrepancy between TAMSAT estimates and those from two mature global rainfall products during winter raises a key concern regarding the use of TAMSAT as a tool for monitoring rainfall patterns and volumes across the southern reaches of the African continent. Overall, the findings of this study demonstrate the diminished ability for TAMSAT to "see" non-convective precipitation from frontal systems as well as orographic rainfall due to its reliance upon the observation of the cold cloud duration and convection characteristics of tropical rainfall systems. This calls for the need to further optimise the TAMSAT algorithm using gauge data so that TAMSAT is better able to characterise different precipitation events in those regions where convective rainfall is far from the only major source of rainfall. • Rainfall is a key determinant of vegetation and crop growth over southern Africa. • Correlation amongst the rainfall estimates was higher during summer. • TAMSAT consistently provides lower monthly rainfall estimates compared to both GPCC and GPCP. • TAMSAT algorithm should be optimized using gauge data to minimize systematic bias. [ABSTRACT FROM AUTHOR]

Infrared molecular Faraday imaging filter( MOFIF) is a novel type of filtering device with comb-like discrete transmission spectrum. Its transmission spectrum is determined by the energy level transition of the molecule,which provides both high spectral resolution and high optical stability. A theoretical model of molecular Faraday optical filter( MFOF) is established by studying Faraday optical rotation effect. And the transmission spectrum of MFOF is measured by using a quantum cascade laser spectrum technology. The application of this technology in infrared imaging detection system is discussed.Preliminary experimental study on combustion diagnosis based on infrared MOFIF technology is also conducted. Pure NO images uninfluenced by H2O infrared radiation were captured in the combustion environment. The experimental results show that MFOF has strong practicability and obvious superiority in infrared imaging remote sensing detection,especially in the detection of trace components in combustion systems. [ABSTRACT FROM AUTHOR]

• We propose a new sea surface temperature inversion model. • The model is based on deep neural network. • This model is more accurate than traditional method. • The model is applied to different sea areas and we get good results. The traditional sea surface temperature (SST) inversion model has a complicated parameter fitting process and poor adaptability in different sea areas. This paper presents an infrared remote sensing inversion model of SST based on deep neural network to refine the situation. The training data are the moderate-resolution imaging spectroradiometer (MODIS) infrared remote sensing data on sunny days and measured data from buoy in Bohai. The accuracy of inversion results is analyzed, the determination coefficient of inversion and measured values is 0.98, the standard error is 0.71 °C and the mean absolute deviation is 0.85 °C, the results show good accuracy of the model. The accuracy of Bohai SST inversion results is compared with SST products from the MODIS sensors and the inversion model is applied to other sea areas, demonstrating the credibility and portability of the model. The data experiments in this paper prove the feasibility of the model, which provides ideas for global SST inversion. [ABSTRACT FROM AUTHOR]

Highlights • Using TIR to measure stream temperatures is a powerful hydrologic tool. • Absolute TIR temperatures are inaccurate without proper environmental corrections. • Reflected temperature and emissivity are needed for accurate TIR temperatures. • Empirical correction reduces errors resulting from whole image reflections. • Future hydrologic TIR studies may benefit from multiple or dual port radiometers. Abstract In recent years, thermal infrared (TIR) cameras have improved in resolution and accuracy while their cost has declined. By deploying a ground-based TIR camera to collect time-lapse images, it is now possible to acquire high-resolution stream temperatures through both space and time. However, while ground-based TIR is useful for qualitatively identifying stream temperature differences, acquisition of absolute stream temperatures remains difficult due to interference from reflected radiation. Therefore, improved correction approaches are still needed to extract absolute stream temperatures from ground-based, time-lapse TIR imagery. Using >1100 TIR images acquired every 10 minutes during two field seasons, we assess two methods for correcting time-lapse, ground-based TIR stream temperature data: (1) an analytical method derived from the literature that corrects for atmospheric transmissivity, reflected temperatures and water surface emissivity, which did not improve TIR temperature accuracy, and (2) an empirical approach that uses an offset correction created from in-stream control point temperatures, which reduced the mean absolute temperature difference between the TIR and in-situ stream temperatures. Examination of the analytical method revealed its sensitivity to reflected temperatures from the surrounding environment, a problem that is particularly pronounced in ground-based TIR imagery due to the lower stream emissivity at more oblique viewing angles. Since reflected temperatures and stream surface emissivity can be difficult to quantify and are misrepresented in previous hydrologic literature, the empirical correction approach offers an alternative method for extracting absolute stream temperatures from ground-based TIR imagery affected by these factors. [ABSTRACT FROM AUTHOR]

Aerosol Optical Depth (AOD) and the Ångström Exponent (AE) have been calculated in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions over a period of one year (May 2019–May 2020) at the high-mountain Izaña Observatory (IZO) from Fourier Transform Infrared (FTIR) solar spectra. The high-resolution FTIR measurements were carried out coincidentally with Cimel CE318-T photometric observations in the framework of the Aerosol Robotic Network (AERONET). A spectral FTIR AOD was generated using two different approaches: by means of the selection of seven narrow FTIR micro-windows (centred at 1020.90, 1238.25, 1558.25, 1636.00, 2133.40, 2192.00, and 2314.20 nm) with negligible atmospheric gaseous absorption, and by using the CE318-AERONET’s response function in the near-coincident bands (1020 nm and 1640 nm) to degrade the high-resolution FTIR spectra. The FTIR system was absolutely calibrated by means of a continuous Langley–Plot analysis over the 1-year period. An important temporal drift of the calibration constant was observed as a result of the environmental exposure of the FTIR’s external optical mirrors (linear degradation rate up to 1.75% month−1). The cross-validation of AERONET-FTIR databases documents an excellent agreement between both AOD products, with mean AOD differences below 0.004 and root-mean-squared errors below 0.006. A rather similar agreement was also found between AERONET and FTIR convolved bands, corroborating the suitability of low-resolution sunphotometers to retrieve high-quality AOD data in the NIR and SWIR domains. In addition, these results demonstrate that the methodology developed here is suitable to be applied to other FTIR spectrometers, such as portable and low-resolution FTIR instruments with a potentially higher spatial coverage. The spectral AOD dependence for the seven FTIR micro-windows have been also examined, observing a spectrally flat AOD behaviour for mineral dust particles (the typical atmospheric aerosols presented at IZO). A mean AE value of 0.53 ± 0.08 for pure mineral dust in the 1020–2314 nm spectral range was retrieved in this paper. A subsequent cross-validation with the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package has ensured the reliability of the FTIR dataset, with AE values between 0.36 to 0.60 for a typical mineral dust content at IZO of 100 cm−3 and water-soluble particle (WASO) content ranging from 600 to 6000 cm−3. The new database generated in this study is believed to be the first long-term time series (1-year) of aerosol properties generated consistently in the NIR and SWIR ranges from ground-based FTIR spectrometry. As a consequence, the results presented here provide a very promising tool for the validation and subsequent improvement of satellite aerosol products as well as enhance the sensitivity to large particles of the existing databases, required to improve the estimation of the aerosols’ radiative effect on climate.

The role, objectives and applications of operating and future space missions for infrared remote sensing of planets and moons are discussed. The review of on-board instruments for infrared survey of planets and moons is presented. The main development trends of on-board instruments for infrared remote sensing of planets and moons were analyzed.

This paper presents a technique to generate cirrus optical depth and particle effective size estimates from the cloud emissivities at 8.5, 11 and 12 μm contained in the Collection-6 (C6) MYD06 cloud product. This technique employs the latest scattering models and scattering radiative transfer approximations to estimate cloud optical depth and particle effective size using efficient analytical formulae. Two scattering models are tested. The first is the same scattering model as that used in the C6 MYD06 solar reflectance products. The second model is an empirical model derived from radiometric consistency. Both models are shown to generate optical depths that compare well to those from constrained CALIPSO retrievals and MYD06. In terms of effective radius retrievals, the results from the radiometric empirical model agree more closely with MYD06 than those from the C6 model. This analysis is applied to AQUA/MODIS data collocated with CALIPSO/CALIOP during January 2010.

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most of the world’s 1500 active volcanoes are not instrumentally monitored, resulting in deadly eruptions which can occur without observation of precursory activity. The new Sentinel missions are now providing freely available imagery with unprecedented spatial and temporal resolutions, with payloads allowing for a comprehensive monitoring of volcanic hazards. We here present the volcano monitoring platform MOUNTS (Monitoring Unrest from Space), which aims for global monitoring, using multisensor satellite-based imagery (Sentinel-1 Synthetic Aperture Radar SAR, Sentinel-2 Short-Wave InfraRed SWIR, Sentinel-5P TROPOMI), ground-based seismic data (GEOFON and USGS global earthquake catalogues), and artificial intelligence (AI) to assist monitoring tasks. It provides near-real-time access to surface deformation, heat anomalies, SO2 gas emissions, and local seismicity at a number of volcanoes around the globe, providing support to both scientific and operational communities for volcanic risk assessment. Results are visualized on an open-access website where both geocoded images and time series of relevant parameters are provided, allowing for a comprehensive understanding of the temporal evolution of volcanic activity and eruptive products. We further demonstrate that AI can play a key role in such monitoring frameworks. Here we design and train a Convolutional Neural Network (CNN) on synthetically generated interferograms, to operationally detect strong deformation (e.g., related to dyke intrusions), in the real interferograms produced by MOUNTS. The utility of this interdisciplinary approach is illustrated through a number of recent eruptions (Erta Ale 2017, Fuego 2018, Kilauea 2018, Anak Krakatau 2018, Ambrym 2018, and Piton de la Fournaise 2018−2019). We show how exploiting multiple sensors allows for assessment of a variety of volcanic processes in various climatic settings, ranging from subsurface magma intrusion, to surface eruptive deposit emplacement, pre/syn-eruptive morphological changes, and gas propagation into the atmosphere. The data processed by MOUNTS is providing insights into eruptive precursors and eruptive dynamics of these volcanoes, and is sharpening our understanding of how the integration of multiparametric datasets can help better monitor volcanic hazards.

We use tropical observations of the water vapor isotopic composition, derived from IASI and TES spaceborne measurements, to show that the isotopic composition of water vapor in the free troposphere is sensitive to both the depth and the intensity of convection. We find that for any given precipitation intensity, vapor associated with deep convection is isotopically depleted relative to vapor associated with shallow convection. The intensity of precipitation also plays a role as for any given depth of convection, the relative enrichment of water vapor decreases as the intensity of precipitation increases. Shallow convection, via the uplifting of enriched boundary layer air into the free troposphere and the convective detrainment, enriches the free troposphere. In contrast, deep convection is associated with processes that deplete the water vapor in the free troposphere, such as rain re-evaporation. The results of this study allow for a better identification of the parameters controlling the isotopic composition of the free troposphere and indicate that the isotopic composition of water vapor can be used to evaluate the relative contributions of shallow and deep convection in global models. [ABSTRACT FROM AUTHOR]

Abstract: Thermal infrared spectra acquired by Cassini/Composite InfraRed Spectrometer (CIRS) in limb‐viewing geometry in 2015 are used to derive 2‐D latitude‐pressure temperature and thermal wind maps. These maps are used to study the vertical structure and evolution of Saturn's equatorial oscillation (SEO), a dynamical phenomenon presenting similarities with the Earth's quasi‐biennal oscillation (QBO) and semi‐annual oscillation (SAO). We report that a new local wind maximum has appeared in 2015 in the upper stratosphere and derive the descent rates of other wind extrema through time. The phase of the oscillation observed in 2015, as compared to 2005 and 2010, remains consistent with a ∼15 year period. The SEO does not propagate downward at a regular rate but exhibits faster descent rate in the upper stratosphere, combined with a greater vertical wind shear, compared to the lower stratosphere. Within the framework of a QBO‐type oscillation, we estimate the absorbed wave momentum flux in the stratosphere to be on the order of ∼7 × 10−6 N m−2. On Earth, interactions between vertically propagating waves (both planetary and mesoscale) and the mean zonal flow drive the QBO and SAO. To broaden our knowledge on waves potentially driving Saturn's equatorial oscillation, we searched for thermal signatures of planetary waves in the tropical stratosphere using CIRS nadir spectra. Temperature anomalies of amplitude 1–4 K and zonal wave numbers 1 to 9 are frequently observed, and an equatorial Rossby (n = 1) wave of zonal wave number 3 is tentatively identified in November 2009. [ABSTRACT FROM AUTHOR]

Pinacea oleracea seeds obtained from a local farmer had been planted using standard procedures on a cultivated arable land in mid tropical summer period in the months of June and July, 2016 in Ede, semi urban western Nigeria. The growth pattern of Pinacea oleracea has been studied via the infrared remote sensing of notable solar-induced physiological parameters of the canopy from germination to flowering stage. The physiological parameters observed were Canopy Reflectance at panchromatic wavelengths, Fluorescence, Fluorescence Intensity, Chlorophyll luminescence, leaf surface Moisture and temperatures. Measurements were taken 6 hourly every day for five days, at Germination, onset of Foliage and Flowering stages. Results showed that the spectral responses varied with cloud covers, time and sunshine hours of the day. Though slightly low at germination results also indicated that solar-induced canopy reflectance increased with the onset of foliage and became fairly sinusoidal at the flowering stage. Results further showed that Canopy Reflectance and fluorescence intensity as well as chlorophyll luminescence exhibited some degradation with time. [ABSTRACT FROM AUTHOR]

Volcanic activity consists of the transfer of heat from the interior of the Earth to the surface. The characteristics of the heat emitted relate directly to the geological processes underway and can be observed from space, using the thermal sensors present on many Earth-orbiting satellites. For over 50 years, scientists have utilised such sensors and are now able to determine the sort of volcanic activity being displayed without hazardous and costly field expeditions. This review will describe the theoretical basis of the discipline and then discuss the sensors available and the history of their use. Challenges and opportunities for future developments are then discussed. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the most important factors in urban water and energy regimes. Because of the extremely high spatial heterogeneity of urban area, accurately measuring ET using conventional methods remains a challenge due to their fetch requirements and low spatial resolution. The goals of this study were to investigate the characteristics of urban ET and its main influencing factors and subsequently to improve a fetch-free, high spatial resolution method for urban ET estimation. The Bowen ratio and the ‘three-temperature model (3T model) + infrared remote sensing (RS)’ methods were used for these purposes. The results of this study are listed in the following lines. (1) Urban ET is mainly affected by solar radiation and the effects of air humidity, wind velocity, and air temperature are very weak; (2) The average daily, monthly, and annual ETs of the urban lawn are 2.70, 60–100, and 990 mm, respectively, which are obvious compared with other landscapes; (3) The ratio of ET to precipitation is 0.65 in the wet season and 2.6 in the dry season, indicating that most of the precipitation is evaporated; (4) The fetch-free approach of ‘3T model + infrared RS’ is verified to be an accurate method for measuring urban ET and it agrees well with the Bowen ratio method (R2 is over 0.93 and the root mean square error is less than 0.04 mm h−1); (5) The spatial heterogeneity of urban ET can also be accurately estimated by the proposed approach. These results are helpful for improving the accuracy of ET estimation in urban areas and are useful for urban water and environmental planning and management. [ABSTRACT FROM AUTHOR]

The paper has developed and proposed a statistical analysis method based on the Robust Satellite data analysis technique to detect seismic anomalies within the NOAA OLR dataset based on spatial/temporal continuity analysis. The proposed methods has been applied to statistical analyze about 3376 earthquake cases from September 01, 2007 to May 23, 2015. For statistical purposes, all these events have been divided into different types on the basis of the seismic parameters, including Southern or Northern Hemisphere earthquakes, earthquakes at different magnitude levels, earthquakes at different depth levels. The results show that the intensity of the anomalies increased with the magnitude increasing; anomalies are more easily observed during shallow earthquakes than the deep ones; more obvious anomalies could be detected for the earthquakes occurring in the Northern Hemisphere and the anomalies significant increases near the epicenter one day before and on the day of the earthquake. A similar anomaly shows that there are anomalies near the epicenters before earthquakes and the anomalies have some relation with the earthquake preparation on all seismic activity. All these statistical results can help create a better understanding of the preparation process of the earthquakes. [ABSTRACT FROM AUTHOR]

• SRResNet-based preprocessing significantly improves precision of detection network. • STPHs promote understanding of contextual semantic connections and reduce false alarms. • Coordinate attention raises attention on important parts of global spatial features. • The shallowest feature map increases low-level location information in feature fusion. • A combination of data augmentation enriches the few-sample training dataset. Infrared remote sensing imaging has a wide range of military and civilian applications. The detection of dim small targets is one of the most valuable research topics in this field. However, model-driven methods are not robust enough to noise, target size and contrast in images, and the currently proposed deep learning methods have insufficient ability to process and fuse important features, resulting in more missed detections and false alarms in these methods. To solve these problems, in this paper, a detection method based on super-resolution and deep learning is proposed. First, we use super-resolution preprocessing and multiple data augmentation on the input images. Secondly, based on the characteristics of infrared small target, we propose a new deep learning network named YOLOSR-IST. This network is based on a series of improvements on YOLOv5, including adding Coordinate Attention to backbone, introducing a high-resolution feature map P2 in the feature fusion, and replacing bottleneck layer of the C3 module in the head of the network with Swin Transformer Blocks. The proposed method achieves mAP@0.5 of 99.2% and 94.6% on two public datasets respectively, and solves the problem of missed detections and false alarms more effectively compared with current advanced data-driven detection methods. [ABSTRACT FROM AUTHOR]

The evapotranspiration (ET) of urban hedges has been assumed to be an important component of the urban water budget and energy balance for years. However, because it is difficult to quantify the ET rate of urban hedges through conventional evapotranspiration methods, the ET rate, characteristics, and the cooling effects of urban hedges remain unclear. This study aims to measure the ET rate and quantify the cooling effects of urban hedges using the ‘three-temperature model + infrared remote sensing (3T + IR)’, a fetch-free and high-spatiotemporal-resolution method. An herb hedge and a shrub hedge were used as field experimental sites in Shenzhen, a subtropical megacity. After verification, the ‘3T + IR’ technique was proven to be a reasonable method for measuring the ET of urban hedges. The results are as follows. (1) The ET rate of urban hedges was very high. The daily average rates of the herb and shrub hedges were 0.38 mm·h−1 and 0.33 mm·h−1, respectively, on the hot summer day. (2) Urban hedges had a strong ability to reduce the air temperature. The two hedges could consume 68.44% and 60.81% of the net radiation through latent heat of ET on the summer day, while their cooling rates on air temperature were 1.29 °C min−1 m−2 and 1.13 °C min−1 m−2, respectively. (3) Hedges could also significantly cool the urban underlying surface. On the summer day, the surface temperatures of the two hedges were 19 °C lower than that of the asphalt pavement. (4) Urban hedges had markedly higher ET rates (0.19 mm·h−1 in the summer day) and cooling abilities (0.66 °C min−1 m−2 for air and 9.14 °C for underlying surface, respectively) than the lawn used for comparison. To the best of our knowledge, this is the first research to quantitatively measure the ET rate of urban hedges, and our findings provide new insight in understanding the process of ET in urban hedges. This work may also aid in understanding the ET of urban vegetation.

The scanning infrared focal plane array (IRFPA) suffers from stripe-like non-uniformity due to the usage of many detectors, especially when working with a large time scale. Typical calibration systems tend to block the sensor aperture and expose the detectors to an on-board blackbody calibration source. They may also point at deep space. Full aperture calibration sources of this type tend to be large and expensive. To address these problems, a dynamic non-uniformity correction (NUC) method is proposed based on a modulated internal calibration device. By employing the on-board calibration device to generate a dynamic scene and fully integrating the system characteristics of the scanning IRFPA into the scene-based non-uniformity correction (SBNUC) algorithm, on-orbit high dynamic range NUC is achieved without blocking the field of view. Here we simulate an internal calibration system alternative, where a dynamic calibration signal is superimposed on the normal imagery, thus requiring no mechanisms and a smaller size. This method using this type of calibrator shows that when the sensor is pointing at deep space for calibration, it provides an effective non-uniformity correction of the imagery. After performing the proposed method, the NU of the two evaluation images was reduced from the initial 12.99% and 8.72% to less than 2%. Compared to other on-board NUC methods that require an extended reference blackbody source, this proposed approach has the advantages of miniaturization, a short calibration time, and strong adaptability.

Evaluation of the environmental impact of gas plumes from stack emissions at the local level requires precise knowledge of the spatial development of the cloud, its evolution over time, and quantitative analysis of each gaseous component. With extensive developments, remote-sensing ground-based technologies are becoming increasingly relevant to such an application. The difficulty of determining the exact 3-D thickness of the gas plume in real time has meant that the various gas components are mainly expressed using correlation coefficients of gas occurrences and path concentration (ppm.m). This paper focuses on a synchronous and non-expensive multi-angled approach combining three high-resolution visible cameras (GoPro-Hero3) and a scanning infrared (IR) gas system (SIGIS, Bruker). Measurements are performed at a NH3 emissive industrial site (NOVACARB Society, Laneuveville-devant-Nancy, France). Visible data images were processed by a first geometrical reconstruction gOcad® protocol to build a 3-D envelope of the gas plume which allows estimation of the plume’s thickness corresponding to the 2-D infrared grid measurements. NH3 concentration data could thereby be expressed in ppm and have been interpolated using a second gOcad® interpolation algorithm allowing a precise volume visualization of the NH3 distribution in the flue gas steam.

• Satellite remote sensing of stratospheric sulfate aerosols by Atmospheric Chemistry Experiment (ACE) mission. • Infrared atmospheric spectra recorded by solar occultation Fourier transform spectroscopy. • Size and composition of volcanic aerosols from Tonga, Raikoke and Nabro eruptions. • Empirical equation to determine sulfate aerosol composition. Large volcanic eruptions create an enhanced layer of sulfate aerosols in the stratosphere. These sulfuric acid droplets persist for many months, altering the climate and stratospheric chemistry. Sulfate aerosols scatter sunlight back to space, cooling the surface of the Earth and absorb outgoing thermal radiation, heating the stratosphere. The calculation of the climate impact of sulfate aerosols depends on their physical properties such as droplet size and chemical composition. These properties are not well known, and this uncertainty contributes to the errors in climate model predictions. Here we derive the first empirical formula that predicts the composition of stratospheric sulfate aerosols from volcanic eruptions from the air temperature and water vapor pressure. Measurements of atmospheric infrared transmittance of the Hunga Tonga-Hunga Ha'apai sulfate aerosol plume by the Atmospheric Chemistry Experiment (ACE) satellite were analyzed to determine composition (weight percent of sulfuric acid) and median particle radius. These data are supplemented by measurements of the Raikoke and Nabro eruptions. Our analysis allows the properties of volcanic aerosols in the stratosphere to be predicted reliably in atmospheric models. [ABSTRACT FROM AUTHOR]

This paper presents a technique to generate cirrus optical depth and particle effective size estimates from the cloud emissivities at 8.5, 11 and 12 μm contained in the Collection-6 (C6) MYD06 cloud product. This technique employs the latest scattering models and scattering radiative transfer approximations to estimate cloud optical depth and particle effective size using efficient analytical formulae. Two scattering models are tested. The first is the same scattering model as that used in the C6 MYD06 solar reflectance products. The second model is an empirical model derived from radiometric consistency. Both models are shown to generate optical depths that compare well to those from constrained CALIPSO retrievals and MYD06. In terms of effective radius retrievals, the results from the radiometric empirical model agree more closely with MYD06 than those from the C6 model. This analysis is applied to AQUA/MODIS data collocated with CALIPSO/CALIOP during January 2010. [ABSTRACT FROM AUTHOR]