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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2017) | Viewed by 138305

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Special Issue Editor

Dr. Hartmut Boesch

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Guest Editor

Department of Physics and Astronomy and National Centre for Earth Observation NCEO, University of Leicester, Leicester LE1 7RH, UK
Interests: remote sensing and retrieval methods of greenhouse gases; application of greenhouse gas remote sensing observations; greenhouse gas instrumentation; design of future satellite missions

Special Issue Information

Dear Colleagues,

The global carbon cycle plays a central role in the Earth system, but a consistent description remains one of the pre-eminent challenges in climate science. Studies of uncertainty in future climate projections suggest that “natural” carbon exchange processes are second only to physical climate sensitivity in importance. Understanding of the growth rate of atmospheric carbon dioxide (CO₂) concentrations hence requires an understanding of aggregated anthropogenic emissions and of resulting changes in the balance of natural terrestrial and ocean sinks. The global partitioning between the land and the ocean sinks is well known from measurements, however, on a regional scale, uncertainties on carbon fluxes are large and key questions are not sufficiently addressed, such as the contributions of the northern hemispheric vs. the tropical land to the global land sink. For methane (CH₄), the situations is more complicated, with a large number of diverse anthropogenic and natural sources contributing to the emission of CH₄ to the atmosphere. The partitioning between these sources is poorly quantified, even on a global scale, and their contributions to the observed variations in the atmospheric growth of CH₄ is not well understood.

Progress in addressing these science challenges has been hampered by limitations in observations. Current surface networks provide highly accurate measurements of global atmospheric CO₂ and CH₄ but their distribution is sparse and uneven leaving large regions practically unobserved. Satellite observations of atmospheric CO₂ and CH₄ can complement observations from the surface networks promising new insights into regional carbon budgets. However, the measurements from satellites need extensive processing to extract the relevant variables, careful validation and error characterization, and the development of adequate modelling and data assimilation systems.

This Special Issue invites contributions related to past, current and future satellite missions for CO₂ and CH₄ with a focus on but not limited to retrieval methods, calibration and validation, related studies using aircraft or ground-based data, results from past or current satellite mission, studies using complementary data streams such as carbon monoxide or solar induced fluorescence, surface flux inversion, new satellite missions, and new instrumentation.

Dr. Hartmut Boesch
Guest Editor

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Keywords

Global carbon cycle
Greenhouse gas remote sensing
Greenhouse gas instrumentation
Surface flux inversions
Retrieval algorithms
Satellite validation and calibration

Published Papers (19 papers)

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28 pages, 5909 KiB

Open AccessFeature PaperArticle

On Statistical Approaches to Generate Level 3 Products from Satellite Remote Sensing Retrievals

by Andrew Zammit-Mangion, Noel Cressie and Clint Shumack

Remote Sens. 2018, 10(1), 155; https://doi.org/10.3390/rs10010155 - 22 Jan 2018

Cited by 21 | Viewed by 5557

Abstract

Satellite remote sensing of trace gases such as carbon dioxide (CO₂) has increased our ability to observe and understand Earth’s climate. However, these remote sensing data, specifically Level 2 retrievals, tend to be irregular in space and time, and hence, spatio-temporal prediction is required to infer values at any location and time point. Such inferences are not only required to answer important questions about our climate, but they are also needed for validating the satellite instrument, since Level 2 retrievals are generally not co-located with ground-based remote sensing instruments. Here, we discuss statistical approaches to construct Level 3 products from Level 2 retrievals, placing particular emphasis on the strengths and potential pitfalls when using statistical prediction in this context. Following this discussion, we use a spatio-temporal statistical modelling framework known as fixed rank kriging (FRK) to obtain global predictions and prediction standard errors of column-averaged carbon dioxide based on Version 7r and Version 8r retrievals from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The FRK predictions allow us to validate statistically the Level 2 retrievals globally even though the data are at locations and at time points that do not coincide with validation data. Importantly, the validation takes into account the prediction uncertainty, which is dependent both on the temporally-varying density of observations around the ground-based measurement sites and on the spatio-temporal high-frequency components of the trace gas field that are not explicitly modelled. Here, for validation of remotely-sensed CO₂

_{}

data, we use observations from the Total Carbon Column Observing Network. We demonstrate that the resulting FRK product based on Version 8r compares better with TCCON data than that based on Version 7r, in terms of both prediction accuracy and uncertainty quantification. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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5132 KiB

Open AccessArticle

TCCON Philippines: First Measurement Results, Satellite Data and Model Comparisons in Southeast Asia

by Voltaire A. Velazco, Isamu Morino, Osamu Uchino, Akihiro Hori, Matthäus Kiel, Beata Bukosa, Nicholas M. Deutscher, Tetsu Sakai, Tomohiro Nagai, Gerry Bagtasa, Toshiharu Izumi, Yukio Yoshida and David W. T. Griffith

Remote Sens. 2017, 9(12), 1228; https://doi.org/10.3390/rs9121228 - 28 Nov 2017

Cited by 21 | Viewed by 7858

Abstract

The Total Carbon Column Observing Network (TCCON) is a global network dedicated to the precise and accurate measurements of greenhouse gases (GHG) in the atmosphere. The TCCON station in Burgos, Ilocos Norte, Philippines was established with the primary purpose of validating the upcoming Greenhouse gases Observing SATellite-2 (GOSAT-2) mission and in general, to respond to the need for reliable ground-based validation data for satellite GHG observations in the region. Here, we present the first 4 months of data from the new TCCON site in Burgos, initial comparisons with satellite measurements of

C O_{2}

and model simulations of

C O

. A nearest sounding from Japan’s GOSAT as well as target mode observations from NASA’s Orbiting Carbon Observatory 2 (OCO-2) showed very good consistency in the retrieved column-averaged dry air mole fractions of

C O_{2}

, yielding TCCON - satellite differences of 0.86 ± 1.06 ppm for GOSAT and 0.83 ± 1.22 ppm for OCO-2. We also show measurements of enhanced

C O

, probably from East Asia. GEOS-Chem model simulations were used to study the observed

C O

variability. However, despite the model capturing the pattern of the

C O

variability, there is an obvious underestimation in the

C O

magnitude in the model. We conclude that more measurements and modeling are necessary to adequately sample the variability over different seasons and to determine the suitability of current inventories. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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2761 KiB

Open AccessArticle

Comparison of Retrieved L2 Products from Four Successive Versions of L1B Spectra in the Thermal Infrared Band of TANSO-FTS over the Arctic Ocean

by Sébastien Payan, Claude Camy-Peyret and Jérôme Bureau

Remote Sens. 2017, 9(11), 1167; https://doi.org/10.3390/rs9111167 - 14 Nov 2017

Cited by 5 | Viewed by 3444

Abstract

This paper concentrates on the calibration/validation of the Thermal and Near Infrared Sensor for Carbon Observation (TANSO)–Fourier Transform Spectrometer (FTS) spectra in the thermal infrared (TIR) spectral region (B4 band) over the Arctic Ocean. We have performed inter-comparisons of the retrieved L2 products from four successive versions of L1B products (V150, V160, V201, V203) to check the differences and the improvement in the spectral and radiometric calibration of TANSO-FTS spectra in the narrow spectral domain of 940–980 cm⁻¹ covering CO₂ lines of the so-called laser band in the rather clear 10.4 μm atmospheric window, allowing sounding down to the lowest atmospheric layers. To our knowledge, this is the first attempt to retrieve XCO₂ from this spectral region. The period covered is the summer months (July, August, September) and the years from 2009 to 2015. Internal comparisons of L1B TANSO-FTS spectra, as well as comparisons of retrieved L2 products, i.e., T_surf (sea surface temperature or SST) and the retrieved column-averaged dry air volume mixing ratio XCO₂ derived with the same algorithm are presented. The overall trend in the CO₂ column-averaged VMR is well captured over the six year period for Green-house Gases Observing Satellite (GOSAT), but calibration issues are still hindering the use of TANSO-FTS TIR spectra for accurate and stable XCO₂ and T_surf products. However, an internal comparison of the successive L1B versions is possible and helpful to make progress with respect to the radiometric and spectral calibration TIR spectra collected by TANSO-FTS on GOSAT. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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21365 KiB

Open AccessArticle

Reduced Methane Emissions from Santa Barbara Marine Seeps

by Thomas Krings, Ira Leifer, Sven Krautwurst, Konstantin Gerilowski, Markus Horstjann, Heinrich Bovensmann, Michael Buchwitz, John P. Burrows, Richard W. Kolyer, Haflidi H. Jonsson and Matthew M. Fladeland

Remote Sens. 2017, 9(11), 1162; https://doi.org/10.3390/rs9111162 - 13 Nov 2017

Cited by 1 | Viewed by 4277

Abstract

Airborne in situ and remote sensing measurements of methane were performed over the marine seeps in the Santa Barbara Channel close to the Coal Oil Point in California on two days in June and August 2014 with the aim to re-assess their methane emissions. During this period, methane column averaged dry air mole fractions derived from airborne remote sensing measurements in the short-wave infrared and airborne in situ measurements of methane indicate that emissions are 2–6 kt CH

_{4}

^{- 1}

, significantly lower than expected from previous publications. This is also confirmed by the on ground in situ measurement time series recorded at the onshore West Campus Monitoring Station in Santa Barbara. Using a time series of methane data, a decline in methane concentrations between 2008 and 2015 of more than a factor of two was derived for air masses originating from the seep field direction. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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2528 KiB

Open AccessArticle

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 1: Radiative Transfer and a Potential OCO-2 XCO₂ Retrieval Setup

by Maximilian Reuter, Michael Buchwitz, Oliver Schneising, Stefan Noël, Vladimir Rozanov, Heinrich Bovensmann and John P. Burrows

Remote Sens. 2017, 9(11), 1159; https://doi.org/10.3390/rs9111159 - 11 Nov 2017

Cited by 25 | Viewed by 5786

Abstract

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO

_{2}

(XCO

_{2}

) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O

_{2}

and CO

_{2}

absorption bands can help to answer important questions about the [...] Read more.

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO

_{2}

(XCO

_{2}

) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O

_{2}

and CO

_{2}

absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO

_{2}

data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO

_{2}

retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO

_{2}

monitoring satellites will produce at least an order of magnitude more data. Here we introduce the Fast atmOspheric traCe gAs retrievaL FOCAL including a scalar RT model which approximates multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer and a Lambertian surface. The computational performance is similar to an absorption only model and currently determined by the convolution of the simulated spectra with the instrumental line shape function (ILS). We assess FOCAL’s quality by confronting it with accurate multiple scattering vector RT simulations using SCIATRAN. The simulated scenarios do not cover all possible geophysical conditions but represent, among others, some typical cloud and aerosol scattering scenarios with optical thicknesses of up to 0.7 which have the potential to survive the pre-processing of a XCO

_{2}

algorithm for real OCO-2 measurements. Systematic errors of XCO

_{2}

range from −2.5 ppm (−6.3‰) to 3.0 ppm (7.6‰) and are usually smaller than ±0.3 ppm (0.8‰). The stochastic uncertainty of XCO

_{2}

is typically about 1.0 ppm (2.5‰). FOCAL simultaneously retrieves the dry-air column-average mole fraction of H

_{2}

O (XH

_{2}

O) and the solar induced chlorophyll fluorescence at 760 nm (SIF). Systematic and stochastic errors of XH

_{2}

O are most times smaller than ±6 ppm and 9 ppm, respectively. The systematic SIF errors are always below 0.02 mW/m

^{2}

/sr/nm, i.e., it can be expected that instrumental or forward model effects causing an in-filling of the used Fraunhofer lines will dominate the systematic errors when analyzing actually measured data. The stochastic uncertainty of SIF is usually below 0.3 mW/m

^{2}

/sr/nm. Without understating the importance of analyzing synthetic measurements as presented here, the actual retrieval performance can only be assessed by analyzing measured data which is subject to part 2 of this publication. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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10401 KiB

Open AccessArticle

The Cross-Calibration of Spectral Radiances and Cross-Validation of CO₂ Estimates from GOSAT and OCO-2

by Fumie Kataoka, David Crisp, Thomas E. Taylor, Chris W. O’Dell, Akihiko Kuze, Kei Shiomi, Hiroshi Suto, Carol Bruegge, Florian M. Schwandner, Robert Rosenberg, Lars Chapsky and Richard A. M. Lee

Remote Sens. 2017, 9(11), 1158; https://doi.org/10.3390/rs9111158 - 11 Nov 2017

Cited by 21 | Viewed by 6670

Abstract

The Greenhouse gases Observing SATellite (GOSAT) launched in January 2009 has provided radiance spectra with a Fourier Transform Spectrometer for more than eight years. The Orbiting Carbon Observatory 2 (OCO-2) launched in July 2014, collects radiance spectra using an imaging grating spectrometer. Both sensors observe sunlight reflected from Earth’s surface and retrieve atmospheric carbon dioxide (CO₂) concentrations, but use different spectrometer technologies, observing geometries, and ground track repeat cycles. To demonstrate the effectiveness of satellite remote sensing for CO₂ monitoring, the GOSAT and OCO-2 teams have worked together pre- and post-launch to cross-calibrate the instruments and cross-validate their retrieval algorithms and products. In this work, we first compare observed radiance spectra within three narrow bands centered at 0.76, 1.60 and 2.06 µm, at temporally coincident and spatially collocated points from September 2014 to March 2017. We reconciled the differences in observation footprints size, viewing geometry and associated differences in surface bidirectional reflectance distribution function (BRDF). We conclude that the spectral radiances measured by the two instruments agree within 5% for all bands. Second, we estimated mean bias and standard deviation of column-averaged CO₂ dry air mole fraction (XCO₂) retrieved from GOSAT and OCO-2 from September 2014 to May 2016. GOSAT retrievals used Build 7.3 (V7.3) of the Atmospheric CO₂ Observations from Space (ACOS) algorithm while OCO-2 retrievals used Version 7 of the OCO-2 retrieval algorithm. The mean biases and standard deviations are −0.57 ± 3.33 ppm over land with high gain, −0.17 ± 1.48 ppm over ocean with high gain and −0.19 ± 2.79 ppm over land with medium gain. Finally, our study is complemented with an analysis of error sources: retrieved surface pressure (P_surf), aerosol optical depth (AOD), BRDF and surface albedo inhomogeneity. We found no change in XCO₂ bias or standard deviation with time, demonstrating that both instruments are well calibrated. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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1780 KiB

Open AccessArticle

Potential of Spaceborne Lidar Measurements of Carbon Dioxide and Methane Emissions from Strong Point Sources

by Christoph Kiemle, Gerhard Ehret, Axel Amediek, Andreas Fix, Mathieu Quatrevalet and Martin Wirth

Remote Sens. 2017, 9(11), 1137; https://doi.org/10.3390/rs9111137 - 08 Nov 2017

Cited by 17 | Viewed by 5160

Abstract

Emissions from strong point sources, primarily large power plants, are a major portion of the total CO₂ emissions. International climate agreements will increasingly require their independent monitoring. A satellite-based, double-pulse, direct detection Integrated Path Differential Absorption (IPDA) Lidar with the capability to actively target point sources has the potential to usefully complement the current and future GHG observing system. This initial study uses simple approaches to determine the required Lidar characteristics and the expected skill of spaceborne Lidar plume detection and emission quantification. A Gaussian plume model simulates the CO₂ or CH₄ distribution downstream of the sources. A Lidar simulator provides the instrument characteristics and dimensions required to retrieve the emission rates, assuming an ideal detector configuration. The Lidar sampling frequency, the footprint distance to the emitting source and the error of an individual measurement are of great importance. If wind speed and direction are known and environmental conditions are ideal, an IPDA Lidar on a 500-km orbit with 2 W average power in the 1.6 µm CO₂ absorption band, 500 Hz pulse repetition frequency, 50 m footprint at sea level and 0.7 m telescope diameter can be expected to measure CO₂ emission rates of 20 Mt/a with an average accuracy better than 3% up to a distance of 3 km away from the source. CH₄ point source emission rates can be quantified with comparable skill if they are larger than 10 kt/a, or if the Lidar pulse repetition frequency is augmented. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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6256 KiB

Open AccessArticle

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO₂ Retrievals from OCO-2

by Maximilian Reuter, Michael Buchwitz, Oliver Schneising, Stefan Noël, Heinrich Bovensmann and John P. Burrows

Remote Sens. 2017, 9(11), 1102; https://doi.org/10.3390/rs9111102 - 28 Oct 2017

Cited by 24 | Viewed by 6727

Abstract

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO

_{2}

(XCO

_{2}

) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O

_{2}

and CO

_{2}

absorption bands can help to answer important questions about the [...] Read more.

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO

_{2}

(XCO

_{2}

) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O

_{2}

and CO

_{2}

absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO

_{2}

data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO

_{2}

_{2}

monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO

_{2}

data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA’s operational OCO-2 XCO

_{2}

product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL’s average reported uncertainty of 1.2 ppm. The large scale XCO

_{2}

patterns of FOCAL and NASA’s operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL’s preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO

_{2}

data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction). Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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4961 KiB

Open AccessArticle

The Methane Isotopologues by Solar Occultation (MISO) Nanosatellite Mission: Spectral Channel Optimization and Early Performance Analysis

by Damien Weidmann, Alex Hoffmann, Neil Macleod, Kevin Middleton, Joe Kurtz, Simon Barraclough and Doug Griffin

Remote Sens. 2017, 9(10), 1073; https://doi.org/10.3390/rs9101073 - 21 Oct 2017

Cited by 31 | Viewed by 6118

Abstract

MISO is an in-orbit demonstration mission that focuses on improving the representation of the methane distribution throughout the upper troposphere and stratosphere, to complement and augment the nadir- and zenith-looking methane observing system for a better understanding of the methane budget. MISO also aims to raise to space mission readiness the concept of laser heterodyne spectro-radiometry (LHR) and associated miniaturization technologies, through demonstration of Doppler-limited atmospheric transmittance spectroscopy of methane from a nanosatellite platform suitable for future constellation deployment. The instrumental and engineering approach to MISO is briefly presented to demonstrate the technical feasibility of the mission. LHR operates using narrow spectral coverage (<1 cm⁻¹) focusing on a few carefully chosen individual ro-vibrational transitions. A line-by-line spectral channel selection methodology is developed and used to optimize spectral channel selection relevant to methane isotopologue sounding from co-registered thermal infrared and short-wave infrared LHR. One of the selected windows is then used to carry out a first performance analysis of methane retrievals based on measurement noise propagation. This preliminary analysis of a single observation demonstrates an ideal instrumental precision of <1% for altitudes in the range 8–20 km, <5% for 20–30 km and <10% up to 37 km on a single isotopologue profile, which leaves a significant reserve for real-world error budget degradation and bodes well for the mission feasibility. MISO could realistically demonstrate methane limb sounding at Doppler-limited spectral resolution, even from a cost-effective 6 dm³ nanosatellite. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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4307 KiB

Open AccessArticle

Comparison of Satellite-Observed XCO₂ from GOSAT, OCO-2, and Ground-Based TCCON

by Ailin Liang, Wei Gong, Ge Han and Chengzhi Xiang

Remote Sens. 2017, 9(10), 1033; https://doi.org/10.3390/rs9101033 - 10 Oct 2017

Cited by 62 | Viewed by 9069

Abstract

CO₂ is one of the most important greenhouse gases. Its concentration and distribution in the atmosphere have always been important in studying the carbon cycle and the greenhouse effect. This study is the first to validate the XCO₂ of satellite observations with total carbon column observing network (TCCON) data and to compare the global XCO₂ distribution for the passive satellites Orbiting Carbon Observatory-2 (OCO-2) and Greenhouse Gases Observing Satellite (GOSAT), which are on-orbit greenhouse gas satellites. Results show that since GOSAT was launched in 2009, its mean measurement accuracy was −0.4107 ppm with an error standard deviation of 2.216 ppm since 2009, and has since decreased to −0.62 ppm with an error standard deviation of 2.3 ppm during the past two more years (2014–2016), while the mean measurement accuracy of the OCO-2 was 0.2671 ppm with an error standard deviation of 1.56 ppm from September 2014 to December 2016. GOSAT observations have recently decreased and lagged behind OCO-2 on the ability to monitor the global distribution and monthly detection of XCO₂. Furthermore, the XCO₂ values gathered by OCO-2 are higher by an average of 1.765 ppm than those by GOSAT. Comparison of the latitude gradient characteristics, seasonal fluctuation amplitude, and annual growth trend of the monthly mean XCO₂ distribution also showed differences in values but similar line shapes between OCO-2 and GOSAT. When compared with the NOAA statistics, both satellites’ measurements reflect the growth trend of the global XCO₂ at a low and smooth level, and reflect the seasonal fluctuation with an absolutely different line shape. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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2242 KiB

Open AccessArticle

Field Validation of Remote Sensing Methane Emission Measurements

by Tom Gardiner, Jon Helmore, Fabrizio Innocenti and Rod Robinson

Remote Sens. 2017, 9(9), 956; https://doi.org/10.3390/rs9090956 - 14 Sep 2017

Cited by 15 | Viewed by 5117

Abstract

Area sources are a key contributor to overall greenhouse gas emissions but present a particular challenge to emission measurement techniques due to the heterogeneous nature of the sources. A new Controlled Release Facility (CRF) has been developed that is able to recreate in the field both the distribution and rate of emissions seen in actual industrial applications. The results of a series of field validation experiments involving this facility and an infrared differential absorption Lidar (DIAL) facility are presented, which have demonstrated the capability of the CRF to generate controlled methane emissions from 1.8 kg/h to 11 kg/h with a typical expanded (k = 2) uncertainty of ~0.3 kg/h, and established that any underlying systematic uncertainty in the DIAL measurements across this range of methane emissions is less than 4% (or 0.2 kg/h). Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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6710 KiB

Open AccessArticle

Differential Absorption Lidar (DIAL) Measurements of Landfill Methane Emissions

by Fabrizio Innocenti, Rod Robinson, Tom Gardiner, Andrew Finlayson and Andy Connor

Remote Sens. 2017, 9(9), 953; https://doi.org/10.3390/rs9090953 - 14 Sep 2017

Cited by 61 | Viewed by 10967

Abstract

Methane is one of the most important gaseous hydrocarbon species for both industrial and environmental reasons. Understanding and quantifying methane emissions to atmosphere is therefore an important element of climate change research. Range-resolved infrared differential absorption Lidar (DIAL) measurements provide the means to map and quantify a wide range of different methane sources. This paper describes the DIAL measurement technique and reports the application of an infrared DIAL system to field measurements of methane emissions from active and closed landfill sites. This paper shows how the capability of the DIAL to measure the spatial distribution of methane plumes enables DIAL vertical scans to spatially separate and independently quantify emissions from different sources. It also allows DIAL horizontal scans carried out above the surface to identify emission hot-spots. An overview of the landfill emission surveys carried out over the last decade by the National Physical Laboratory (NPL) DIAL system is presented. These surveys were part of research projects and commercial works aimed to validate the method and to provide reliable information on the methane emissions measuring the total site and area-specific emissions from active areas, capped areas, and gas engine stacks. This work showed that methane emissions are significantly higher for active sites than closed sites due to the methane emitted directly to air from the uncapped active areas. On active sites, the operational tipping areas generally have higher emission levels than the capped areas, although there is considerably variation in the emission from different capped areas. The information obtained with DIAL measurements allow site operators to identify significant fugitive emission sources and validate emissions estimates, and they allow the regulators to revise and update the emission inventories. Operators’ remediation actions driven by DIAL measurements have also been shown to considerably decreased total site methane emission. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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3732 KiB

Open AccessArticle

Influence of Droughts on Mid-Tropospheric CO₂

by Xun Jiang, Angela Kao, Abigail Corbett, Edward Olsen, Thomas Pagano, Albert Zhai, Sally Newman, Liming Li and Yuk Yung

Remote Sens. 2017, 9(8), 852; https://doi.org/10.3390/rs9080852 - 17 Aug 2017

Cited by 3 | Viewed by 4505

Abstract

Using CO₂ data from the Atmospheric Infrared Sounder (AIRS), it is found for the first time that the mid-tropospheric CO₂ concentration is ~1 part per million by volume higher during dry years than wet years over the southwestern USA from June to September. The mid-tropospheric CO₂ differences between dry and wet years are related to circulation and CO₂ surface fluxes. During drought conditions, vertical pressure velocity from NCEP2 suggests that there is more rising air over most regions, which can help bring high surface concentrations of CO₂ to the mid-troposphere. In addition to the circulation, there is more CO₂ emitted from the biosphere to the atmosphere during droughts in some regions, which can contribute to higher concentrations of CO₂ in the atmosphere. Results obtained from this study demonstrate the significant impact of droughts on atmospheric CO₂ and therefore on a feedback cycle contributing to greenhouse gas warming. It can also help us better understand atmospheric CO₂, which plays a critical role in our climate system. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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6510 KiB

Open AccessArticle

Challenges in Methane Column Retrievals from AVIRIS-NG Imagery over Spectrally Cluttered Surfaces: A Sensitivity Analysis

by Minwei Zhang, Ira Leifer and Chuanmin Hu

Remote Sens. 2017, 9(8), 835; https://doi.org/10.3390/rs9080835 - 12 Aug 2017

Cited by 7 | Viewed by 5967

Abstract

A comparison between efforts to detect methane anomalies by a simple band ratio approach from the Airborne Visual Infrared Imaging Spectrometer-Classic (AVIRIS-C) data for the Kern Front oil field, Central California, and the Coal Oil Point marine hydrocarbon seep field, offshore southern California, was conducted. The detection succeeded for the marine source and failed for the terrestrial source, despite these sources being of comparable strength. Scene differences were investigated in higher spectral and spatial resolution collected by the AVIRIS-C successor instrument, AVIRIS Next Generation (AVIRIS-NG), by a sensitivity study. Sensitivity to factors including water vapor, aerosol, planetary boundary layer (PBL) structure, illumination and viewing angle, and surface albedo clutter were explored. The study used the residual radiance method, with sensitivity derived from MODTRAN (MODerate resolution atmospheric correction TRANsmission) simulations of column methane (XCH₄). Simulations used the spectral specifications and geometries of AVIRIS-NG and were based on a uniform or an in situ vertical CH₄ profile, which was measured concurrent with the AVIRIS-NG data. Small but significant sensitivity was found for PBL structure and water vapor; however, highly non-linear, extremely strong sensitivity was found for surface albedo error. For example, a 10% decrease in the surface albedo corresponded to a 300% XCH₄ increase over background XCH₄ to compensate for the total signal, less so for stronger plumes. This strong non-linear sensitivity resulted from the high percentage of surface-reflected radiance in the airborne at-sensor total radiance. Coarse spectral resolution and feedback from interferents like water vapor underlay this sensitivity. Imaging spectrometry like AVIRIS and the Hyperspectral InfraRed Imager (HyspIRI) candidate satellite mission, have the advantages of contextual spatial information and greater at-sensor total radiance. However, they also face challenges due to their relatively broad spectral resolution compared to trace gas specific orbital sensors, e.g., the Greenhouse gases Observing SATellite (GOSAT), which is especially applicable to trace gas retrievals over scenes with high spectral albedo variability. Results of the sensitivity analysis are applicable for the residual radiance method and CH₄ profiles used in the analysis, but they illustrate potential significant challenges in CH₄ retrievals using other approaches. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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Open AccessArticle

Low-Altitude Aerial Methane Concentration Mapping

by Bara J. Emran, Dwayne D. Tannant and Homayoun Najjaran

Remote Sens. 2017, 9(8), 823; https://doi.org/10.3390/rs9080823 - 10 Aug 2017

Cited by 52 | Viewed by 12445

Abstract

Detection of leaks of fugitive greenhouse gases (GHGs) from landfills and natural gas infrastructure is critical for not only their safe operation but also for protecting the environment. Current inspection practices involve moving a methane detector within the target area by a person or vehicle. This procedure is dangerous, time consuming, labor intensive and above all unavailable when access to the desired area is limited. Remote sensing by an unmanned aerial vehicle (UAV) equipped with a methane detector is a cost-effective and fast method for methane detection and monitoring, especially for vast and remote areas. This paper describes the integration of an off-the-shelf laser-based methane detector into a multi-rotor UAV and demonstrates its efficacy in generating an aerial methane concentration map of a landfill. The UAV flies a preset flight path measuring methane concentrations in a vertical air column between the UAV and the ground surface. Measurements were taken at 10 Hz giving a typical distance between measurements of 0.2 m when flying at 2 m/s. The UAV was set to fly at 25 to 30 m above the ground. We conclude that besides its utility in landfill monitoring, the proposed method is ready for other environmental applications as well as the inspection of natural gas infrastructure that can release methane with much higher concentrations. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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Performance Evaluation for China’s Planned CO₂-IPDA

by Ge Han, Xin Ma, Ailin Liang, Tianhao Zhang, Yannan Zhao, Miao Zhang and Wei Gong

Remote Sens. 2017, 9(8), 768; https://doi.org/10.3390/rs9080768 - 26 Jul 2017

Cited by 36 | Viewed by 5291

Abstract

Active remote sensing of atmospheric XCO₂ has several advantages over existing passive remote sensors, including global coverage, a smaller footprint, improved penetration of aerosols, and night observation capabilities. China is planning to launch a multi-functional atmospheric observation satellite equipped with a CO₂-IPDA (integrated path differential absorption Lidar) to measure columnar concentrations of atmospheric CO₂ globally. As space and power are limited on the satellite, compromises have been made to accommodate other passive sensors. In this study, we evaluated the sensitivity of the system’s retrieval accuracy and precision to some critical parameters to determine whether the current configuration is adequate to obtain the desired results and whether any further compromises are possible. We then mapped the distribution of random errors across China and surrounding regions using pseudo-observations to explore the performance of the planned CO₂-IPDA over these regions. We found that random errors of less than 0.3% can be expected for most regions of our study area, which will allow the provision of valuable data that will help researchers gain a deeper insight into carbon cycle processes and accurately estimate carbon uptake and emissions. However, in the areas where major anthropogenic carbon sources are located, and in coastal seas, random errors as high as 0.5% are predicted. This is predominantly due to the high concentrations of aerosols, which cause serious attenuation of returned signals. Novel retrieving methods must, therefore, be developed in the future to suppress interference from low surface reflectance and high aerosol loading. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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Open AccessArticle

Characterizing Regional-Scale Combustion Using Satellite Retrievals of CO, NO₂ and CO₂

by Sam J. Silva and A. F. Arellano

Remote Sens. 2017, 9(7), 744; https://doi.org/10.3390/rs9070744 - 19 Jul 2017

Cited by 33 | Viewed by 8351

Abstract

We present joint analyses of satellite-observed combustion products to examine bulk characteristics of combustion in megacities and fire regions. We use retrievals of CO, NO₂ and CO₂ from NASA/Terra Measurement of Pollution In The Troposphere, NASA/Aura Ozone Monitoring Instrument, and JAXA Greenhouse Gases Observing Satellite to estimate atmospheric enhancements of these co-emitted species based on their spatiotemporal variability (spread, σ) within 14 regions dominated by combustion emissions. We find that patterns in σ_XCO/σ_XCO₂ and σ_XCO/σ_XNO₂ are able to distinguish between combustion types across the globe. These patterns show distinct groupings for biomass burning and the developing/developed status of a region that are not well represented in global emissions inventories. We show here that such multi-species analyses can provide constraints on emission inventories, and be useful in monitoring trends and understanding regional-scale combustion. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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Open AccessEditor’s ChoiceReview

MERLIN: A French-German Space Lidar Mission Dedicated to Atmospheric Methane

by Gerhard Ehret, Philippe Bousquet, Clémence Pierangelo, Matthias Alpers, Bruno Millet, James B. Abshire, Heinrich Bovensmann, John P. Burrows, Frédéric Chevallier, Philippe Ciais, Cyril Crevoisier, Andreas Fix, Pierre Flamant, Christian Frankenberg, Fabien Gibert, Birgit Heim, Martin Heimann, Sander Houweling, Hans W. Hubberten, Patrick Jöckel, Kathy Law, Alexander Löw, Julia Marshall, Anna Agusti-Panareda, Sebastien Payan, Catherine Prigent, Patrick Rairoux, Torsten Sachs, Marko Scholze and Martin Wirth Show full author list Hide full author list

Remote Sens. 2017, 9(10), 1052; https://doi.org/10.3390/rs9101052 - 16 Oct 2017

Cited by 101 | Viewed by 14465

Abstract

The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (<3.7 ppb) to significantly improve our knowledge of methane sources from global to regional scales, with emphasis on poorly accessible regions in the tropics and at high latitudes. This paper presents the MERLIN objectives, describes the methodology and the main characteristics of the payload and of the platform, and proposes a first assessment of the error budget and its translation into expected uncertainty reduction of methane surface emissions. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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Open AccessLetter

Assessment of Anthropogenic Methane Emissions over Large Regions Based on GOSAT Observations and High Resolution Transport Modeling

by Rajesh Janardanan, Shamil Maksyutov, Akihiko Ito, Yoshida Yukio and Tsuneo Matsunaga

Remote Sens. 2017, 9(9), 941; https://doi.org/10.3390/rs9090941 - 11 Sep 2017

Cited by 10 | Viewed by 6086

Abstract

Abstract: Methane is an important greenhouse gas due to its high warming potential. While quantifying anthropogenic methane emissions is important for evaluation measures applied for climate change mitigation, large emission uncertainties still exist for many source categories. To evaluate anthropogenic methane emission inventory in various regions over the globe, we extract emission signatures from column-average methane observations (XCH₄) by GOSAT (Greenhouse gases Observing SATellite) satellite using high-resolution atmospheric transport model simulations. XCH₄ abundance due to anthropogenic emissions is estimated as the difference between polluted observations from surrounding cleaner observations. Here, reduction of observation error, which is large compared to local abundance, is achieved by binning the observations over large region according to model-simulated enhancements. We found that the local enhancements observed by GOSAT scale linearly with inventory based simulations of XCH₄ for the globe, East Asia and North America. Weighted linear regression of observation derived and inventory-based XCH₄ anomalies was carried out to find a scale factor by which the inventory agrees with the observations. Over East Asia, the observed enhancements are 30% lower than suggested by emission inventory, implying a potential overestimation in the inventory. On the contrary, in North America, the observations are approximately 28% higher than model predictions, indicating an underestimation in emission inventory. Our results concur with several recent studies using other analysis methodologies, and thus confirm that satellite observations provide an additional tool for bottom-up emission inventory verification. Full article

(This article belongs to the Special Issue Remote Sensing of Greenhouse Gases)

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