scholarly journals The operational methane retrieval algorithm for TROPOMI

2016 ◽  
Author(s):  
Haili Hu ◽  
Otto Hasekamp ◽  
André Butz ◽  
André Galli ◽  
Jochen Landgraf ◽  
...  
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Short Wave ◽  
Retrieval Algorithm ◽  
Model Errors ◽  
Instrument Calibration ◽  
Atmospheric Scattering ◽  
Accuracy And Precision ◽  
Land Surfaces

Abstract. This work presents the operational methane retrieval algorithm for the Sentinel-5 Precursor (S5-P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOMI spectrometer aboard S5-P. The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. Also, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup. We show that the required accuracy and precision of < 1 % for the XCH4 product are met for clear sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We find further that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the short-wave infrared channel.

scholarly journals The operational methane retrieval algorithm for TROPOMI

2016 ◽  
Vol 9 (11) ◽  
pp. 5423-5440 ◽  
Author(s):  
Haili Hu ◽  
Otto Hasekamp ◽  
André Butz ◽  
André Galli ◽  
Jochen Landgraf ◽  
...  
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Retrieval Algorithm ◽  
Model Errors ◽  
Instrument Calibration ◽  
Atmospheric Scattering ◽  
Accuracy And Precision ◽  
Land Surfaces ◽  
Shortwave Infrared

Abstract. This work presents the operational methane retrieval algorithm for the Sentinel 5 Precursor (S5P) satellite and its performance tested on realistic ensembles of simulated measurements. The target product is the column-averaged dry air volume mixing ratio of methane (XCH4), which will be retrieved simultaneously with scattering properties of the atmosphere. The algorithm attempts to fit spectra observed by the shortwave and near-infrared channels of the TROPOspheric Monitoring Instrument (TROPOMI) spectrometer aboard S5P.The sensitivity of the retrieval performance to atmospheric scattering properties, atmospheric input data and instrument calibration errors is evaluated. In addition, we investigate the effect of inhomogeneous slit illumination on the instrument spectral response function. Finally, we discuss the cloud filters to be used operationally and as backup.We show that the required accuracy and precision of  < 1 % for the XCH4 product are met for clear-sky measurements over land surfaces and after appropriate filtering of difficult scenes. The algorithm is very stable, having a convergence rate of 99 %. The forward model error is less than 1 % for about 95 % of the valid retrievals. Model errors in the input profile of water do not influence the retrieval outcome noticeably. The methane product is expected to meet the requirements if errors in input profiles of pressure and temperature remain below 0.3 % and 2 K, respectively. We further find that, of all instrument calibration errors investigated here, our retrievals are the most sensitive to an error in the instrument spectral response function of the shortwave infrared channel.

scholarly journals New method to determine the instrument spectral response function, applied to TROPOMI-SWIR

2017 ◽  
Author(s):  
Richard M. van Hees ◽  
Paul J. J. Tol ◽  
Sidney Cadot ◽  
Matthijs Krijger ◽  
Stefan T. Persijn ◽  
...  
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Short Wave ◽  
Trace Gas ◽  
Imaging Spectrometer ◽  
Swir Band ◽  
Novel Method ◽  
Single Instrument

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) is the single instrument on board of the ESA Copernicus Sentinel-5 Precursor satellite. TROPOMI is a nadir-viewing imaging spectrometer with bands in the ultraviolet and visible, the near infrared and the short-wave infrared (SWIR). An accurate instrument spectral response function (ISRF) is required in the SWIR band where absorption lines of CO, methane and water vapor overlap. Therefore a novel method for ISRF determination for an imaging spectrometer was developed and applied to the TROPOMI-SWIR band. The ISRF of all detector pixels of the SWIR spectrometer has been measured during an on-ground calibration campaign. The accuracy of the derived ISRF is well within the requirement for accurate trace-gas retrievals. Long-term in-flight monitoring of the ISRF is guaranteed by the presence of five SWIR diode lasers.

scholarly journals Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

2015 ◽  
Vol 8 (12) ◽  
pp. 12663-12707 ◽  
Author(s):  
T. E. Taylor ◽  
C. W. O'Dell ◽  
C. Frankenberg ◽  
P. Partain ◽  
H. Q. Cronk ◽  
...  
Keyword(s):  
Surface Pressure ◽  
Near Infrared ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Screening Methods ◽  
Data Set ◽  
Atmospheric Scattering ◽  
Carbon Dioxide Co2 ◽  
Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of &amp;simeq; 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be &amp;simeq; 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

scholarly journals Applying spectral analysis for identification of alteration zones in north Saveh area, Iran

2020 ◽  
Author(s):  
Kazem Rangzan ◽  
Somayeh Beyranvand ◽  
Hoshang Pourkaseb ◽  
Hojjatollah Ranjbar ◽  
Alireza Zarasvandi
Keyword(s):  
Hydrothermal Alteration ◽  
Near Infrared ◽  
Thermal Emission ◽  
Spectral Response ◽  
Volcanic Rocks ◽  
Short Wave ◽  
Rapid Identification ◽  
Spectral Angle Mapper ◽  
Alteration Zones ◽  
The North

An extensive series of volcanic rocks are exposed in the north of Saveh city, Iran, which consist of phyllic, argillic and propylitic hydrothermal alteration types. For the purpose of the investigation, a FieldSpec3® spectroradiometer was used to measure the spectral response of the mineral content of these rocks. The spectral analyses of reflectance curve by The Spectral Geologist (TSG) software could discriminate kaolinite and montmorillonite (argillic), illite, muscovite, phengite and paragonite (phyllic), hornblende and chlorite, siderite (propylitic), hematite and goethite from the gossans. It also detected gypsum of hydrothermal alteration zones. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image, which was used for mapping the hydrothermal alteration minerals, contains the Visible and Near Infrared (VNIR) wavelengths between 0.52 µm and 0.86 µm, Short Wave Infrared (SWIR) wavelengths between 1.6 µm and 2.43 µm and Thermal Infrared (TIR) wavelengths between 8.125 µm and 11.65 µm with 15, 30 and 90 m spatial resolutions, respectively. For calibration of the ASTER images, the extracted spectra of different rocks and minerals were used for atmospheric and radiometric corrections. Mixture tuned matched filtering (MTMF) and Spectral Angle Mapper (SAM) were applied on ASTER data to map the hydrothermal alteration of minerals. The use of the spectroradiometry techniques in conjunction with other data exhibits the ability of these new methods for non-destructive and rapid identification of mineral types for more detailed investigation. The results show that the area has undergone different levels of hydrothermal alteration, so much so that phyllic, argillic and propylitic types of hydrothermal alteration are present in the study area. This may point to high potential and promising zones for the exploration of porphyry mineralisation.

scholarly journals Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data

2016 ◽  
Vol 9 (3) ◽  
pp. 973-989 ◽  
Author(s):  
Thomas E. Taylor ◽  
Christopher W. O'Dell ◽  
Christian Frankenberg ◽  
Philip T. Partain ◽  
Heather Q. Cronk ◽  
...  
Keyword(s):  
Surface Pressure ◽  
Near Infrared ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Screening Methods ◽  
Atmospheric Scattering ◽  
Initial Comparison ◽  
Carbon Dioxide Co2 ◽  
Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set.To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of  ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be  ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations.No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

SPECTRAL RESPONSE MEASUREMENTS OF SHORT WAVE INFRARED DETECTORS (SWIR)

2002 ◽  
Vol 12 (02) ◽  
pp. 541-550 ◽  
Author(s):  
TAMER F. REFAAT ◽  
M. NURUL ABEDIN ◽  
UPENDRA N. SINGH
Keyword(s):  
Bias Voltage ◽  
Infrared Detectors ◽  
Near Infrared ◽  
Spectral Response ◽  
Short Wave ◽  
Electrical Signal ◽  
External Bias ◽  
Short Wave Infrared ◽  
Critical Components ◽  
Processing And Storage

Quantum detectors are critical components in infrared lidar receivers. They convert the optical return signal into electrical signal compatible with electronic data processing and storage devices. The detectors used in this study comprise InGaAs PIN diodes and InGaAsSb avalanche photodiodes (APDs) for short wave infrared applications and Si APDs, with different structures, for near-infrared applications. The spectral response of these infrared detectors utilized for lidar receivers was studied with respect to operating temperature and external bias voltage. Variation of these spectral responses as a function of bias voltage and temperature was determined. This variation is employed to estimate errors in the detected lidar return signal. Results of this research finding are reported in this article.

scholarly journals Determination of the TROPOMI-SWIR instrument spectral response function

2018 ◽  
Vol 11 (7) ◽  
pp. 3917-3933 ◽  
Author(s):  
Richard M. van Hees ◽  
Paul J. J. Tol ◽  
Sidney Cadot ◽  
Matthijs Krijger ◽  
Stefan T. Persijn ◽  
...  
Keyword(s):  
Response Function ◽  
Near Infrared ◽  
Spectral Response ◽  
Monochromatic Light ◽  
Imaging Spectrometer ◽  
Swir Band ◽  
Shortwave Infrared ◽  
Single Instrument

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) is the single instrument on board the ESA Copernicus Sentinel-5 Precursor satellite. TROPOMI is a nadir-viewing imaging spectrometer with bands in the ultraviolet and visible, the near infrared and the shortwave infrared (SWIR). An accurate instrument spectral response function (ISRF) is required in the SWIR band where absorption lines of CO, methane and water vapor overlap. In this paper, we report on the determination of the TROPOMI-SWIR ISRF during an extensive on-ground calibration campaign. Measurements are taken with a monochromatic light source scanning the whole detector, using the spectrometer itself to determine the light intensity and wavelength. The accuracy of the resulting ISRF calibration key data is well within the requirement for trace-gas retrievals. Long-term in-flight monitoring of SWIR ISRF is achieved using five on-board diode lasers.

scholarly journals Identificación de perturbaciones en el bosque húmedo tropical colombiano usando series temporales de imágenes satelitales Landsat mediante el algoritmo Landtrendr

2019 ◽  
pp. 25
Author(s):  
L. Hurtado ◽  
I. Lizarazo
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Response ◽  
Short Wave ◽  
Forest Vegetation ◽  
Landsat Images ◽  
Spectral Bands

<p>Time series analysis of satellite images for detection of deforestation and forest disturbances at specific dates has been a subject of research over the last few years. There are many limitations to identify the exact date of deforestation due mainly to the large volume of data and the criteria required for its correct characterization. A further limitation in the analysis of multispectral time series is the identification of true deforestation considering that forest vegetation may undergo different changes over time. This study analyzes deforestation in a zone within the Colombian Amazon using the Normalized Difference Vegetation Index (NDVI) based on semestral median mosaics generated from Landsat images collected from 2000 to 2017. Several samples representing trends of change over the time series were extracted and classified according to their degree of change and persistence in the series, using four categories: (i) deforestation, (ii) degradation, (iii) forest plantation, and (iv) regeneration. Specific deforestation samples were analyzed in the same way using the soil-adjusted vegetation index (SAVI) to reduce the effect of spectral response variations due to soil reflectance changes. It is concluded that the two indices used, together with the near infrared (NIR) and short-wave infrared (SWIR 1) spectral bands, allow to extract values and intervals where the change produced by deforestation on forest vegetation is identified with acceptable accuracy. The analysis of time series using the Landtrendr algorithm confirmed a reliable change detection in each of the forest disturbance categories.</p>

Multi-Sensor Retrieval Algorithm for Daytime Total Column Water Vapor from Passive Imagers

2021 ◽  
Author(s):  
Jan Riad El Kassar ◽  
Cintia Carbajal Henken ◽  
Rene Preusker ◽  
Jürgen Fischer
Keyword(s):  
Water Vapor ◽  
Land Surface ◽  
Near Infrared ◽  
Weather Prediction ◽  
Retrieval Algorithm ◽  
Water Vapor Absorption ◽  
Convective Initiation ◽  
Land Surfaces ◽  
Work Done ◽  
Total Column

&lt;p&gt;A novel algorithm for total column water vapor (TCWV) retrieval, which uses a combination of satellite-based measurements in the near-infrared (NIR) and infrared (IR) spectrum, is presented. The algorithm is built with a modular approach so that it can be used for a wide array of passive sensors. It is based on a fast forward model for NIR and IR bands at the water vapor absorption peaks in use on current and future instruments.&amp;#160;&lt;/p&gt;&lt;p&gt;An Ocean Land &amp; Colour Imager (OLCI) TCWV retrieval for land surfaces has been developed, building on earlier work done for MERIS and MODIS, including extensive validation exercises using well-established ground-based TCWV observations as reference. The retrieval is extended to a synergy with IR measurements at 11 and 12um from the Sea and Land Surface Temperature Radiometer (SLSTR), also onboard the Sentinel-3 satellites. This allows more accurate TCWV retrievals over dark water surfaces.&amp;#160;&lt;/p&gt;&lt;p&gt;Moreover, support is planned for the polar-orbiting meteorological satellite instruments such as METimage on Metop - Second Generation (Metop-SG) and geostationary instruments such as the Flexible Combined Imager (FCI) onboard Meteosat Third Generation (MTG).&amp;#160;&lt;/p&gt;&lt;p&gt;Application examples of the newly derived TCWV observations include studying the potential of assimilating OLCI&amp;#8217;s high spatial resolution TCWV fields in Numerical Weather Prediction (NWP) as well as detection of convective initiation in TCWV fields before the onset of clouds and precipitation within the German project RealPEP.&lt;/p&gt;

scholarly journals Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring

2020 ◽  
Vol 88 (6) ◽  
pp. 493-507
Author(s):  
Alexander Jenal ◽  
Ulrike Lussem ◽  
Andreas Bolten ◽  
Martin Leon Gnyp ◽  
Jürgen Schellberg ◽  
...  
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Short Wave ◽  
Data Sets ◽  
Camera System ◽  
Ground Truth Data ◽  
Calibration Methods

AbstractRemote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.

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