Estimation of an Aerosol Plume Mass Balance from Plume Property Retrievals Computed by the Combination of the Sentinel-2 Data with Hyperspectral Data Coupled with an Optimal Estimation Method

2021 ◽  
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-Francois Leon
Keyword(s):  
Mass Balance ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Hyperspectral Data ◽  
Aerosol Plume ◽  
Sentinel 2

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

scholarly journals On the limit to resolution and information on basal properties obtainable from surface data on ice streams

2008 ◽  
Vol 2 (2) ◽  
pp. 167-178 ◽  
Author(s):  
G. H. Gudmundsson ◽  
M. Raymond
Keyword(s):  
Flow Line ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Surface Flow ◽  
Ice Thickness ◽  
Ice Streams ◽  
Statistical Estimates ◽  
Surface Data ◽  
Highly Sensitive ◽  
Surface Measurements

Abstract. An optimal estimation method for simultaneously determining both basal slipperiness and basal topography from variations in surface flow velocity and topography along a flow line on ice streams and ice sheets is presented. We use Bayesian inference to update prior statistical estimates for basal topography and slipperiness using surface measurements along a flow line. Our main focus here is on how errors and spacing of surface data affect estimates of basal quantities and on possibly aliasing/mixing between basal slipperiness and basal topography. We find that the effects of spatial variations in basal topography and basal slipperiness on surface data can be accurately separated from each other, and mixing in retrieval does not pose a serious problem. For realistic surface data errors and density, small-amplitude perturbations in basal slipperiness can only be resolved for wavelengths larger than about 50 times the mean ice thickness. Bedrock topography is well resolved down to horizontal scale equal to about one ice thickness. Estimates of basal slipperiness are not significantly improved by accurate prior estimates of basal topography. However, retrieval of basal slipperiness is found to be highly sensitive to unmodelled errors in basal topography.

scholarly journals First application of the optimal estimation method to retrieve temperature from pure rotational raman scatter lidar measurements

2018 ◽  
Vol 176 ◽  
pp. 01011
Author(s):  
S. Mahagammulla Gamage ◽  
A. Haefele ◽  
R.J. Sica
Keyword(s):  
Estimation Method ◽  
Optimal Estimation ◽  
Forward Model ◽  
Temperature Calibration ◽  
Lidar Measurements ◽  
Raman Scatter ◽  
Calibration Coefficients

We present the application of the Optimal Estimation Method (OEM) to retrieve atmospheric temperatures from pure rotational Raman (PRR) backscatter lidar measurements. A forward model (FM) is developed to retrieve temperature and tested using synthetic measurements. The OEM offers many advantages for this analysis, including eliminating the need to determine temperature calibration coefficients.

scholarly journals Solar occultation with SCIAMACHY: algorithm description and first validation

2005 ◽  
Vol 5 (1) ◽  
pp. 17-66 ◽  
Author(s):  
J. Meyer ◽  
A. Bracher ◽  
A. Rozanov ◽  
A. C. Schlesier ◽  
H. Bovensmann ◽  
...  
Keyword(s):  
Vertical Profile ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Trace Gas ◽  
Transmission Spectra ◽  
Atmospheric Transmission ◽  
Gas Concentration ◽  
Solar Occultation ◽  
First Results ◽  
The Vertical Distribution

Abstract. This presentation concentrates on solar occultation measurements with the spaceborne spectrometer SCIAMACHY in the UV-Vis wavelength range. Solar occultation measurements provide unique information about the vertical distribution of atmospheric constituents. For retrieval of vertical trace gas concentration profiles, an algorithm has been developed based on the optimal estimation method. The forward model is capable to simulate the extinction signals of different species as they occur in atmospheric transmission spectra obtained from occultation measurements. Furthermore, correction algorithms have been implemented to address shortcomings of the tangent height pre-processing and inhomogeneities of measured solar spectra. First results of O3 and NO2 vertical profile retrievals have been validated with data from ozone sondes and satellite based occultation instruments. The validation shows very promising results for SCIAMACHY O3 and NO2 values between 15 to 35 km with errors in the order of 10% and 15%, respectively.

Validation of TROPOMI nadir ozone profile retrievals: Methodology and first results

2021 ◽  
Author(s):  
Arno Keppens ◽  
Jean-Christopher Lambert ◽  
Daan Hubert ◽  
Steven Compernolle ◽  
Tijl Verhoelst ◽  
...  
Keyword(s):  
Near Infrared ◽  
Stratospheric Ozone ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Data Retrieval ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Validation Data ◽  
Ozone Profile ◽  
Early Afternoon

<p>Part of the space segment of EU’s Copernicus Earth Observation programme, the Sentinel-5 Precursor (S5P) mission is dedicated to global and European atmospheric composition measurements of air quality, climate and the stratospheric ozone layer. On board of the S5P early afternoon polar satellite, the imaging spectrometer TROPOMI (TROPOspheric Monitoring Instrument) performs nadir measurements of the Earth radiance within the UV-visible and near-infrared spectral ranges, from which atmospheric ozone profile data are retrieved. Developed at the Royal Netherlands Meteorological Institute (KNMI) and based on the optimal estimation method, TROPOMI’s operational ozone profile retrieval algorithm has recently been upgraded. With respect to early retrieval attempts, accuracy is expected to have improved significantly, also thanks to recent updates of the TROPOMI Level-1b data product. This work reports on the initial validation of the improved TROPOMI height-resolved ozone data in the troposphere and stratosphere, as collected both from the operational S5P Mission Performance Centre/Validation Data Analysis Facility (MPC/VDAF) and from the S5PVT scientific project CHEOPS-5p. Based on the same validation best practices as developed for and applied to heritage sensors like GOME-2, OMI and IASI (Keppens et al., 2015, 2018), the validation methodology relies on the analysis of data retrieval diagnostics – like the averaging kernels’ information content – and on comparisons of TROPOMI data with reference ozone profile measurements. The latter are acquired by ozonesonde, stratospheric lidar, and tropospheric lidar stations performing network operation in the context of WMO's Global Atmosphere Watch and its contributing networks NDACC and SHADOZ. The dependence of TROPOMI’s ozone profile uncertainty on several influence quantities like cloud fraction and measurement parameters like sun and scan angles is examined and discussed. This work concludes with a set of quality indicators, enabling users to verify the fitness-for-purpose of the S5P data.</p>

scholarly journals Improvements to a long-term Rayleigh-scatter lidar temperature climatology by using an optimal estimation method

2018 ◽  
Vol 11 (11) ◽  
pp. 6043-6058 ◽  
Author(s):  
Ali Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele
Keyword(s):  
Traditional Method ◽  
Middle Atmosphere ◽  
Density Measurement ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Temperature Profiles ◽  
Vertical Resolution ◽  
Order Of Magnitude ◽  
Lidar Equation ◽  
Model Temperature

Abstract. Hauchecorne and Chanin (1980) developed a robust method to calculate middle-atmosphere temperature profiles using measurements from Rayleigh-scatter lidars. This traditional method has been successfully used to greatly improve our understanding of middle-atmospheric dynamics, but the method has some shortcomings regarding the calculation of systematic uncertainties and the vertical resolution of the retrieval. Sica and Haefele (2015) have shown that the optimal estimation method (OEM) addresses these shortcomings and allows temperatures to be retrieved with confidence over a greater range of heights than the traditional method. We have calculated a temperature climatology from 519 nights of Purple Crow Lidar Rayleigh-scatter measurements using an OEM. Our OEM retrieval is a first-principle retrieval in which the forward model is the lidar equation and the measurements are the level-0 count returns. It includes a quantitative determination of the top altitude of the retrieved temperature profiles, the evaluation of nine systematic plus random uncertainties, and the vertical resolution of the retrieval on a profile-by-profile basis. Our OEM retrieval allows for the vertical resolution to vary with height, extending the retrieval in altitude 5 to 10 km higher than the traditional method. It also allows the comparison of the traditional method's sensitivity to two in-principle equivalent methods of specifying the seed pressure: using a model pressure seed versus using a model temperature combined with the lidar's density measurement to calculate the seed pressure. We found that the seed pressure method is superior to using a model temperature combined with the lidar-derived density. The increased altitude capability of our OEM retrievals allows for a comparison of the Rayleigh-scatter lidar temperatures throughout the entire altitude range of the sodium lidar temperature measurements. Our OEM-derived Rayleigh temperatures are shown to have improved agreement relative to our previous comparisons using the traditional method, and the agreement of the OEM-derived temperatures is the same as the agreement between existing sodium lidar temperature climatologies. This detailed study of the calculation of the new Purple Crow Lidar temperature climatology using the OEM establishes that it is both highly advantageous and practical to reprocess existing Rayleigh-scatter lidar measurements that cover long time periods, during which time the lidar may have undergone several significant equipment upgrades, while gaining an upper limit to useful temperature retrievals equivalent to an order of magnitude increase in power-aperture product due to the use of an OEM.

scholarly journals A practical information-centered technique to remove a priori information from lidar optimal-estimation-method retrievals

2019 ◽  
Vol 12 (7) ◽  
pp. 3943-3961 ◽  
Author(s):  
Ali Jalali ◽  
Shannon Hicks-Jalali ◽  
Robert J. Sica ◽  
Alexander Haefele ◽  
Thomas von Clarmann
Keyword(s):  
Water Vapor ◽  
A Priori ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Mixing Ratio ◽  
A Priori Information ◽  
Fine Grid ◽  
Large Power ◽  
Priori Information ◽  
Water Vapor Mixing Ratio

Abstract. Lidar retrievals of atmospheric temperature and water vapor mixing ratio profiles using the optimal estimation method (OEM) typically use a retrieval grid with a number of points larger than the number of pieces of independent information obtainable from the measurements. Consequently, retrieved geophysical quantities contain some information from their respective a priori values or profiles, which can affect the results in the higher altitudes of the temperature and water vapor profiles due to decreasing signal-to-noise ratios. The extent of this influence can be estimated using the retrieval's averaging kernels. The removal of formal a priori information from the retrieved profiles in the regions of prevailing a priori effects is desirable, particularly when these greatest heights are of interest for scientific studies. We demonstrate here that removal of a priori information from OEM retrievals is possible by repeating the retrieval on a coarser grid where the retrieval is stable even without the use of formal prior information. The averaging kernels of the fine-grid OEM retrieval are used to optimize the coarse retrieval grid. We demonstrate the adequacy of this method for the case of a large power-aperture Rayleigh scatter lidar nighttime temperature retrieval and for a Raman scatter lidar water vapor mixing ratio retrieval during both day and night.

scholarly journals Retrieval of temperature and water vapor profiles from radio occultation refractivity and bending angle measurements using an Optimal Estimation approach: a simulation study

2005 ◽  
Vol 5 (6) ◽  
pp. 1665-1677 ◽  
Author(s):  
A. von Engeln ◽  
G. Nedoluha
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
A Priori ◽  
Estimation Method ◽  
Lower Troposphere ◽  
Optimal Estimation ◽  
Pronounced Difference ◽  
Bending Angle ◽  
Advantages And Disadvantages ◽  
The Impact

Abstract. The Optimal Estimation Method is used to retrieve temperature and water vapor profiles from simulated radio occultation measurements in order to assess how different retrieval schemes may affect the assimilation of this data. High resolution ECMWF global fields are used by a state-of-the-art radio occultation simulator to provide quasi-realistic bending angle and refractivity profiles. Both types of profiles are used in the retrieval process to assess their advantages and disadvantages. The impact of the GPS measurement is expressed as an improvement over the a priori knowledge (taken from a 24h old analysis). Large improvements are found for temperature in the upper troposphere and lower stratosphere. Only very small improvements are found in the lower troposphere, where water vapor is present. Water vapor improvements are only significant between about 1 km to 7 km. No pronounced difference is found between retrievals based upon bending angles or refractivity. Results are compared to idealized retrievals, where the atmosphere is spherically symmetric and instrument noise is not included. Comparing idealized to quasi-realistic calculations shows that the main impact of a ray tracing algorithm can be expected for low latitude water vapor, where the horizontal variability is high. We also address the effect of altitude correlations in the temperature and water vapor. Overall, we find that water vapor and temperature retrievals using bending angle profiles are more CPU intensive than refractivity profiles, but that they do not provide significantly better results.

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