A two-camera instrument for highly resolved Gas Correlation Spectroscopy measurements of NO2

Gas Cell ◽

Band Pass ◽

Spatio Temporal ◽

Spectral Channels

Imaging of atmospheric trace gases is becoming an increasingly important field of remote sensing. Conventional methods (like imaging-DOAS) typically use dispersive elements and wavelength mapping (at moderate to high spectral resolution) and need intricate optical setup. Therefore, they are limited in spatio-temporal resolution.Some atmospheric trace gases can, however, be detected only by using a few carefully selected spectral channels, specific to the selected trace gas. These can be filtered using non-dispersive spectral filters without spatial mapping of continuous spectra, vastly increasing the spatio-temporal resolution. This has become a routine in volcanic SO2 flux analysis, where band-pass filters provide the spectral filtering.We propose fast imaging of spatial Nitrogen Dioxide (NO2) distributions employing Gas Correlation Spectroscopy (GCS) in the visible wavelength range. Two spectral channels are used, one with a gas cell that is filled with a high amount of NO2 in the light path and one without. An additional band-pass filter preselects a wavelength range containing structured and strong NO2 absorption (e.g. 430 - 450 nm). The NO2 containing gas cell serves as a NO2 specific spectral filter, almost blocking the light at wavelengths of the strong NO2 absorption bands within the preselected wavelength range. Absorption by atmospheric NO2 has therefore a lower impact on the channel with gas cell compared to the channel without gas cell. This difference is used to generate NO2 images.NO2&#160;plays a major role in urban air pollution, where it is primarily emitted by point sources (power plants, vehicle internal combustion engines), before undergoing chemical conversions. The corresponding spatial gradients can neither be resolved with the established in-situ techniques nor with the widely used DOAS remote sensing method.Recent advances in the physical implementation of a GCS-based NO2 camera suggest, that the quality of the measurement may be vastly enhanced in a two-detector (two-camera) set-up. Here, individual cameras are used for the two spectral channels. Not only does this double the photon budget available, but it also allows for synchronized exposure in both channels. This is critical for the quality of the measurement, since dynamic gas or intensity features on time scales smaller than the exposure delay of a one-camera system can induce strong false signals.A proof of concept measurement was carried out, where test cells with NO2 column densities ranging from 1E16 to 4E18 molecules cm-2 were measured both with DOAS and our camera. The results coincided within their uncertainties and allow for camera calibration based on an instrument forward model.

The NO2 camera based on Gas Correlation Spectroscopy

10.5194/amt-2021-298 ◽

2021 ◽

Author(s):

Leon Kuhn ◽

Jonas Kuhn ◽

Thomas Wagner ◽

Ulrich Platt

Keyword(s):

Temporal Resolution ◽

Sensor Arrays ◽

Visible Spectral Range ◽

High Concentration ◽

Gas Cell ◽

Signal Ratio ◽

Large Power ◽

Spectral Window ◽

Abstract. Monitoring of NO2 is in the interest of public health, because NO2 contributes to the decline of air quality in many urban regions. Its abundance can be a direct cause of asthmatic and cardiovascular diseases and plays a significant part in forming other pollutants such as ozone or particulate matter. Spectroscopic methods have proven to be reliable and of high selectivity by utilizing the characteristic spectral absorption signature of trace gasses such as NO2. However, they typically lack the spatio-temporal resolution required for real-time imaging measurements of NO2 emissions. We propose imaging measurements of NO2 in the visible spectral range using a novel instrument, an NO2 camera based on the principle of Gas Correlation Spectroscopy (GCS). For this purpose two gas cells (cuvettes) are placed in front of two camera modules. One gas cell is empty, while the other is filled with a high concentration of the target gas. The filled gas cell operates as a non-dispersive spectral filter to the incoming light, maintaining the two-dimensional imaging capability of the sensor arrays. NO2 images are generated on the basis of the signal ratio between the two images in the spectral window between 430 and 445 nm, where the NO2 absorption cross section is strongly structured. The capabilities and limits of the instrument are investigated in a numerical forward model. The predictions of this model are verified in a proof-of-concept measurement, in which the column densities in specially prepared reference cells were measured with the NO2 camera and a conventional DOAS instrument. Finally, results from measurements at a large power plant, the Großkraftwerk Mannheim (GKM), are presented. NO2 column densities of the plume emitted from a GKM chimney are quantified at a spatio-temporal resolution of 1/6 frames per second (FPS) and 0.92 m × 0.92 m. A detection limit of 1.89 · 1016 molec cm−2 was reached. An NO2 mass flux of Fm = (7.41 ± 4.23) kg h−1 was estimated on the basis of momentary wind speeds obtained from consecutive images. The camera results are verified by comparison to NO2 slant column densities obtained from elevation scans with a MAX-DOAS instrument. The instrument prototype is highly portable and cost-efficient at building costs of below 2,000 Euro.

Towards imaging of atmospheric trace gases using Fabry Perot Interferometer Correlation Spectroscopy in the UV and visible spectral range

10.5194/amt-2018-350 ◽

2018 ◽

Cited By ~ 1

Author(s):

Jonas Kuhn ◽

Ulrich Platt ◽

Nicole Bobrowski ◽

Thomas Wagner

Keyword(s):

Temporal Resolution ◽

State Of The Art ◽

Trace Gases ◽

Visible Spectral Range ◽

Trace Gas ◽

Atmospheric Trace Gases ◽

Fabry Perot ◽

Sensitivity And Selectivity ◽

Abstract. Many processes in the lower atmosphere including transport, turbulent mixing and chemical conversions happen on time scales of the order of seconds (e.g. at point sources). Remote sensing of atmospheric trace gases in the UV and visible spectral range (UV/Vis) commonly uses dispersive spectroscopy (e.g. Differential Optical Absorption Spectroscopy, DOAS). The recorded spectra allow for the direct identification, separation and quantification of narrow band absorption of trace gases. However, these techniques are typically limited to a single viewing direction and limited by the light throughput of the spectrometer setup. While two dimensional imaging is possible by spatial scanning, the temporal resolution remains poor (often several minutes per image). Therefore, processes on time scales of seconds cannot be directly resolved by state of the art dispersive methods. We investigate the application of Fabry-Perot Interferometers (FPIs) for the optical remote sensing of atmospheric trace gases in the UV/Vis. By choosing a FPI transmission spectrum, which is optimised to correlate with narrow band (ideally periodic) absorption structures of the target trace gas, column densities of the trace gas can be determined with a sensitivity and selectivity comparable to dispersive spectroscopy, using only a small number of spectral channels (FPI tuning settings). Different from dispersive optical elements, the FPI can be implemented in full frame imaging setups (cameras), which can reach high spatio-temporal resolution. In principle, FPI Correlation Spectroscopy can be applied for any trace gas with distinct absorption structures in the UV/Vis. We present calculations for the application of FPI Correlation Spectroscopy to SO2, BrO and NO2 for exemplary measurement scenarios. Besides high sensitivity and selectivity we find that the spatio temporal resolution of FPI Correlation Spectroscopy can be more than two orders of magnitude higher than state of the art DOAS measurements. As proof of concept we built a one-pixel prototype implementing the technique for SO2 in the UV. Good agreement with our calculations and conventional measurement techniques are demonstrated and no cross sensitivities to other trace gases are observed.

Towards imaging of atmospheric trace gases using Fabry–Pérot interferometer correlation spectroscopy in the UV and visible spectral range

Atmospheric Measurement Techniques ◽

10.5194/amt-12-735-2019 ◽

2019 ◽

Vol 12 (1) ◽

pp. 735-747 ◽

Cited By ~ 2

Author(s):

Jonas Kuhn ◽

Ulrich Platt ◽

Nicole Bobrowski ◽

Thomas Wagner

Keyword(s):

Temporal Resolution ◽

Spectral Range ◽

Trace Gases ◽

Visible Spectral Range ◽

Trace Gas ◽

Atmospheric Trace Gases ◽

Fabry Perot ◽

Sensitivity And Selectivity ◽

Abstract. Many processes in the lower atmosphere including transport, turbulent mixing and chemical conversions happen on timescales of the order of seconds (e.g. at point sources). Remote sensing of atmospheric trace gases in the UV and visible spectral range (UV–Vis) commonly uses dispersive spectroscopy (e.g. differential optical absorption spectroscopy, DOAS). The recorded spectra allow for the direct identification, separation and quantification of narrow-band absorption of trace gases. However, these techniques are typically limited to a single viewing direction and limited by the light throughput of the spectrometer set-up. While two-dimensional imaging is possible by spatial scanning, the temporal resolution remains poor (often several minutes per image). Therefore, processes on timescales of seconds cannot be directly resolved by state-of-the-art dispersive methods. We investigate the application of Fabry–Pérot interferometers (FPIs) for the optical remote sensing of atmospheric trace gases in the UV–Vis spectral range. By choosing a FPI transmission spectrum, which is optimised to correlate with narrow-band (ideally periodic) absorption structures of the target trace gas, column densities of the trace gas can be determined with a sensitivity and selectivity comparable to dispersive spectroscopy, using only a small number of spectral channels (FPI tuning settings). Different from dispersive optical elements, the FPI can be implemented in full-frame imaging set-ups (cameras), which can reach high spatio-temporal resolution. In principle, FPI correlation spectroscopy can be applied for any trace gas with distinct absorption structures in the UV–Vis range. We present calculations for the application of FPI correlation spectroscopy to SO2, BrO and NO2 for exemplary measurement scenarios. In addition to high sensitivity and selectivity we find that the spatio temporal resolution of FPI correlation spectroscopy can be more than 2 orders of magnitude higher than state-of-the-art DOAS measurements. As proof of concept we built a 1-pixel prototype implementing the technique for SO2 in the UV. Good agreement with our calculations and conventional measurement techniques is demonstrated and no cross sensitivities to other trace gases are observed.

Technological advances to improve the quantification of volcanic emissions

10.5194/egusphere-egu21-1308 ◽

2021 ◽

Author(s):

Christopher Fuchs ◽

Jonas Kuhn ◽

Nicole Bobrowski ◽

Ulrich Platt

Keyword(s):

Remote Sensing ◽

Temporal Resolution ◽

Volcanic Activity ◽

Imaging Techniques ◽

Trace Gas ◽

Optical Absorption Spectroscopy ◽

Integration Time ◽

Volcanic Plume ◽

Flux Measurements

Variations in volcanic trace gas composition and fluxes are a valuable indicator for changes in magmatic systems and therefore allow monitoring of the volcanic activity. An established method to measure trace gas emissions is to use remote sensing techniques like, for example, Differential Optical Absorption Spectroscopy (DOAS) and more recently SO2-cameras, that can quantify volcanic sulphur dioxide (SO2) emissions during quiescent degassing and eruptive phases, making it possible to correlate fluxes with volcanic activity.&#160;We present flux measurements of volcanic SO2 emissions based on the novel remote sensing technique of Imaging Fabry-P&#233;rot Interferometer Correlation Spectroscopy (IFPICS) in the UV spectral range. The basic principle of IFPICS lies in the application of an Fabry-P&#233;rot Interferometer (FPI) as wavelength selective element. The FPIs periodic transmission profile is matched to the periodic spectral absorption features of SO2, resulting in high spectral information for its detection. This technique yields a higher trace gas selectivity and sensitivity than imaging approaches based on interference filters, e.g. SO2-cameras and an increased spatio-temporal resolution over spectroscopic imaging techniques, e.g. imaging DOAS. Hence, IFPICS shows reduced cross sensitivities to broadband absorption (e.g. to ozone, aerosols), which allows the application to weaker volcanic SO2 emitters and increases the range of possible atmospheric conditions. It further raises the possibility to apply IFPICS to other trace gas species like, for example, bromine monoxide, that still can be characterized with a high spatial and temporal resolution (< 1 HZ).In October 2020, we acquired SO2 column density distribution images of Mt Etna volcanic plume with a detection limit of 2x1017 molec cm-2, 1 s integration time, 400x400 pixel spatial, and 0.3 Hz temporal resolution.&#160; We compare the SO2 fluxes retrieved by IFPICS with simultaneous flux measurements using the mutli-axis DOAS technique.

A Soil Moisture Spatial and Temporal Resolution Improving Algorithm Based on Multi-Source Remote Sensing Data and GRNN Model

Remote Sensing ◽

10.3390/rs12030455 ◽

2020 ◽

Vol 12 (3) ◽

pp. 455 ◽

Cited By ~ 5

Author(s):

Yaokui Cui ◽

Xi Chen ◽

Wentao Xiong ◽

Lian He ◽

Feng Lv ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Temporal Resolution ◽

Land Surface ◽

Regional Scale ◽

Remote Sensing Data ◽

The Tibetan Plateau ◽

Sensing Data ◽

Spatio Temporal ◽

Input Variables

Surface soil moisture (SM) plays an essential role in the water and energy balance between the land surface and the atmosphere. Low spatio-temporal resolution, about 25–40 km and 2–3 days, of the commonly used global microwave SM products limits their application at regional scales. In this study, we developed an algorithm to improve the SM spatio-temporal resolution using multi-source remote sensing data and a machine-learning model named the General Regression Neural Network (GRNN). First, six high spatial resolution input variables, including Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), albedo, Digital Elevation Model (DEM), Longitude (Lon) and Latitude (Lat), were selected and gap-filled to obtain high spatio-temporal resolution inputs. Then, the GRNN was trained at a low spatio-temporal resolution to obtain the relationship between SM and input variables. Finally, the trained GRNN was driven by the high spatio-temporal resolution input variables to obtain high spatio-temporal resolution SM. We used the Fengyun-3B (FY-3B) SM over the Tibetan Plateau (TP) to test the algorithm. The results show that the algorithm could successfully improve the spatio-temporal resolution of FY-3B SM from 0.25° and 2–3 days to 0.05° and 1-day over the TP. The improved SM is consistent with the original product in terms of both spatial distribution and temporal variation. The high spatio-temporal resolution SM allows a better understanding of the diurnal and seasonal variations of SM at the regional scale, consequently enhancing ecological and hydrological applications, especially under climate change.

Extraction of Eucalyptus Planting Area Based on High Spatio-Temporal Resolution Visible Remote Sensing Data

Advances in Geosciences ◽

10.12677/ag.2019.911123 ◽

2019 ◽

Vol 09 (11) ◽

pp. 1167-1174

Author(s):

其换成

Keyword(s):

Remote Sensing ◽

Temporal Resolution ◽

Remote Sensing Data ◽

Sensing Data ◽

Quantitative imaging of volcanic SO2 plumes using Fabry–Pérot interferometer correlation spectroscopy

Atmospheric Measurement Techniques ◽

10.5194/amt-14-295-2021 ◽

2021 ◽

Vol 14 (1) ◽

pp. 295-307

Author(s):

Christopher Fuchs ◽

Jonas Kuhn ◽

Nicole Bobrowski ◽

Ulrich Platt

Keyword(s):

Temporal Resolution ◽

Cross Sections ◽

Imaging Techniques ◽

Quantitative Imaging ◽

Trace Gases ◽

High Specificity ◽

Volcanic Plume ◽

Model Calculations ◽

Fabry Perot

Abstract. We present first measurements with a novel imaging technique for atmospheric trace gases in the UV spectral range. Imaging Fabry–Pérot interferometer correlation spectroscopy (IFPICS) employs a Fabry–Pérot interferometer (FPI) as the wavelength-selective element. Matching the FPI's distinct, periodic transmission features to the characteristic differential absorption structures of the investigated trace gas allows us to measure differential atmospheric column density (CD) distributions of numerous trace gases with high spatial and temporal resolution. Here we demonstrate measurements of sulfur dioxide (SO2), while earlier model calculations show that bromine monoxide (BrO) and nitrogen dioxide (NO2) are also possible. The high specificity in the spectral detection of IFPICS minimises cross-interferences to other trace gases and aerosol extinction, allowing precise determination of gas fluxes. Furthermore, the instrument response can be modelled using absorption cross sections and a solar atlas spectrum from the literature, thereby avoiding additional calibration procedures, e.g. using gas cells. In a field campaign, we recorded the temporal CD evolution of SO2 in the volcanic plume of Mt. Etna, with an exposure time of 1 s and 400×400 pixel spatial resolution. The temporal resolution of the time series was limited by the available non-ideal prototype hardware to about 5.5 s. Nevertheless, a detection limit of 2.1×1017 molec cm−2 could be reached, which is comparable to traditional and much less selective volcanic SO2 imaging techniques.

A Dynamic Spatio-Temporal Analysis of Urban Expansion and Pollutant Emissions in Fujian Province

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17020629 ◽

2020 ◽

Vol 17 (2) ◽

pp. 629 ◽

Cited By ~ 1

Author(s):

Shen Zhao ◽

Guanpeng Dong ◽

Yong Xu

Keyword(s):

Remote Sensing ◽

Temporal Resolution ◽

Urban Expansion ◽

Living Environment ◽

Pollutant Emissions ◽

Pollutant Emission ◽

Fujian Province ◽

Emission Data ◽

Emissions Intensity ◽

Urbanization processes at both global and regional scales are taking place at an unprecedent pace, leading to more than half of the global population living in urbanized areas. This process could exert grand challenges on the human living environment. With the proliferation of remote sensing and satellite data being used in social and environmental studies, fine spatial- and temporal-resolution measures of urban expansion and environmental quality are increasingly available. This, in turn, offers great opportunities to uncover the potential environmental impacts of fast urban expansion. This paper investigated the relationship between urban expansion and pollutant emissions in the Fujian province of China by building a Bayesian spatio-temporal autoregressive model. It drew upon recently compiled pollutant emission data with fine spatio-temporal resolution, long temporal coverage, and multiple sources of remote sensing data. Our results suggest that there was a significant relationship between urban expansion and pollution emission intensity—urban expansion significantly elevated the PM2.5 and NOx emissions intensity in Fujian province during 1995–2015. This finding was robust to different measures of urban expansion and retained after controlling for potential confounding effects. The temporal evolution of pollutant emissions, net of covariate effects, presented a fluctuation pattern rather than a consistent trend of increasing or decreasing. Spatial variability of the pollutant emissions intensity among counties was, however, decreasing steadily with time.

Resolving spatial dynamics at polar latitudes using the GROMOS-C radiometer on Svalbard and the Nordic meteor radar cluster

10.5194/egusphere-egu2020-9822 ◽

2020 ◽

Author(s):

Gunter Stober ◽

Franziska Schranz ◽

Chris Hall ◽

Alexander Kozlovsky ◽

Mark Lester ◽

...

Keyword(s):

Remote Sensing ◽

Temporal Resolution ◽

Lower Thermosphere ◽

Winter Season ◽

Spatial Characteristic ◽

Small Scale ◽

Meteor Radar ◽

Atmospheric Waves ◽

Spatio Temporal ◽

Remote Sensing Techniques

The middle polar atmosphere dynamics is driven by atmospheric waves from the planetary scale to small scale perturbation due to gravity waves. The different atmospheric waves are characterized by their temporal and spatial variability posing challenges to ground-based remote sensing techniques to disentangle and resolve the spatio-temporal ambiguity. Here we present two ground-based remote sensing techniques to resolving spatio-temporal variability at the polar middle atmosphere.Since 2017 the GROMOS-C radiometer measures ozone and winds at Ny&#197;lesund (78.9&#176;N, 11.9&#176;E) on Svalbard. The radiometer employs four beams in the cardinal directions at 22.5&#176; elevation angle to retrieve ozone profiles and winds at altitudes between 30-75 km. the temporal resolution of the ozone retrievals is 30 minutes. Further, we obtain daily mean winds. Due to the high polar latitude the spatial separation between the beams at stratospheric altitudes covers several degrees in longitude to infer spatial gradients in the ozone densities and their perturbation due to planetary waves.Another recently established ground-based remote sensing approach to retrieve the spatial characteristic at the mesosphere and lower thermosphere (MLT) is provided by the Nordic meteor radar cluster consisting of the meteor radars at Troms&#248;, Alta, Esrange, Sodankyl&#228; and on Svalbard. Since October 2019 horizontally resolved winds are obtained using a 3DVAR approach with a temporal resolution of 30 minutes and a vertical resolution of 2 km. Here we present preliminary results to infer horizontal wavelength spectra, the tidal variability as well as gravity activity of the winter season 2019/20.Both datasets are of high value for data assimilation into weather forecast and reanalysis models or for cross-comparisons and validation of meteorological analysis systems (e.g. NAVGEM-HA).

Requirements for multispectral remote sensing data used for the detection of arable land colonization by tree and shrubbery vegetation

Computer Optics ◽

10.18287/2412-6179-2019-43-5-846-856 ◽

2019 ◽

Vol 43 (5) ◽

pp. 846-856 ◽

Cited By ~ 2

Author(s):

A.Y. Denisova ◽

A.A. Egorova ◽

V.V. Sergeyev ◽

L.M. Kavelenova

Keyword(s):

Remote Sensing ◽

Plant Communities ◽

Spatial Resolution ◽

Arable Land ◽

Remote Sensing Data ◽

Fallow Land ◽

Land Colonization ◽

Multispectral Remote Sensing ◽

Spectral Channels

We discuss requirements for the multispectral remote sensing (RS) data utilized in the author's technique for estimating plant species concentration to detect arable land colonization by tree and shrubbery vegetation. The study is carried out using available high-resolution remote sensing data of two arable land plots. The paper considers the influence of resolution, combinations of spectral channels of RS data, as well as the season RS data is acquired on the quality of identification of elementary vegetation classes that form the basis of the plant community – a fallow land. A fallow land represents a piece of arable land that has not been cultivated for a long time. The study was conducted using a technology that is based on image superpixel segmentation. We found out that for determining tree and shrub vegetation, it is preferable to use RS data acquired in autumn, namely, in late September. The combination of red and blue spectral channels turned out to be the best for the analysis of tree-shrub vegetation against the background of grassy plant communities, and the presence of a near-infrared channel is necessary to range the various grassy plant communities in different classes. RS data with a spatial resolution of 2.5 m can be used to define tree-shrub plant communities with a high closeness of crowns (90 % or more), but cannot be used to classify isolated trees. Trees and shrubs (with a height of 8 m) can be classified in images with a spatial resolution of 0.8 m. An increase in spatial resolution does not improve the quality of the classification. The highest accuracies achieved for the land areas studied are 90 % and 83 %. Therefore, the suggested technology can be used in arable land expertise.