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Peter Gavrilov
Peter Gavrilov

Satellite Tracks Carbon Polluters From Space €? IEEE Spectrum \/\/FREE\\\\

That dynamic started to change within the past decade, as broader industry demands drove the miniaturization of electronics and shrank the costs of rocket launches. This made it possible to develop smaller, cheaper satellites that carry sensors capable of zooming in on individual sites to capture high-resolution methane measurements. Companies and one environmental group have leaped at harnessing such satellite capabilities for industries and policymakers eager to pinpoint individual local methane sources. But governments and large aerospace companies, encumbered by lengthy planning processes, have been slower to pivot away from a focus on measuring methane emissions on a regional and global scale. In 2016 the Montreal-based company GHGSat was the first to get off the ground with a proof-of-concept satellite called Claire, which successfully detected methane emissions from specific sites.

Satellite Tracks Carbon Polluters From Space – IEEE Spectrum

Space-based measurements of carbon dioxide (CO2) are used to help answer questions about Earth's carbon cycle. There are a variety of active and planned instruments for measuring carbon dioxide in Earth's atmosphere from space. The first satellite mission designed to measure CO2 was the Interferometric Monitor for Greenhouse Gases (IMG) on board the ADEOS I satellite in 1996. This mission lasted less than a year. Since then, additional space-based measurements have begun, including those from two high-precision (better than 0.3% or 1 ppm) satellites (GOSAT and OCO-2). Different instrument designs may reflect different primary missions.

Instrumental remote sensing techniques dedicated to the monitoring of atmospheric gases concentrations have greatly improved in the past few decades. They now allow the probing into the lowest layers of the troposphere and contribute to air quality assessment. For atmospheric composition, satellite observational tools are mainly in the form of spectrometers with high spectral resolution ranging from the thermal infrared (IR) to the ultraviolet (UV) spectral range. Combined with ground-based measurements and atmospheric models, space observations are now essential and used in most scientific studies and many atmospheric applications to improve our knowledge about the physical and chemical processes from the global to the local scale.

To observe atmospheric constituents, passive remote sensing instruments from satellites measure atmospheric spectra resulting from the interaction between radiation (solar or emitted by the Earth or the atmosphere) and molecules. The exploitation of these signals containing specific features or signatures of the different molecules allows retrieving their concentrations at different altitudes (for strong absorbers) or their total column concentration (integrated along the vertical, for weak absorbers). Each molecular absorption/emission line (in the IR) or cross section (in the UV) in a spectrum has a characteristic signature: The position indicates the identity of the molecule, and the intensity infers its atmospheric concentration. To properly retrieve atmospheric concentrations from any raw satellite spectrum, other parameters are required, such as characteristics of the instrument (detector, optics, etc.), spectroscopic data, auxiliary data such as temperature or air mass factors, and an atmospheric radiative transfer algorithm.

In summary, ocean oil pollutions are mainly coming from illegal discharges and one of the best tools for detecting them is the imaging radar on board satellites. But SAR instruments cannot, alone, achieve the level of recognition expected by organizations such as European Maritime Safety Agency (EMSA). More evidences are required, and we showed here that by adding informations directly derived from the SAR image (ship detection, wind speed and direction, image interpreters) coupled with external ones (met conditions, AIS positions, drift model), we are able to accumulate enough proofs for having a significant impact on the polluters, including prosecution (a set of evidences can be accepted as a proof).

Recent laboratory measurements of the isoprene cross-section in the thermal infrared (IR)15 have provided the spectroscopic parameters needed for the remote sensing of isoprene from space. Here, we apply these spectroscopic data and demonstrate the detection of the isoprene spectral signature in space-borne radiance measurements from the Cross-track Infrared Sounder (CrIS), an imaging Fourier transform spectrometer onboard the Suomi National Polar-Orbiting Partnership (NPP) satellite. We present a full-physics algorithm for retrieving atmospheric isoprene columns from the CrIS data, and use the two-step retrieval strategy to quantify the isoprene distribution over Amazonia, a major source region. Finally, we discuss the information content and uncertainty characteristics for these new isoprene measurements, and compare the satellite observations with in situ data and model predictions for the same region.

Presently, parts of an MRV system (i.e., deforestation) can be operated using available satellite and forest inventory data. However, data on quantitative changes associated with forest degradation are generally missing, and in many developing countries there is low capacity for monitoring of, and reporting on emissions from degradation (and removals from regrowth and afforestion) on a national level [31]. Traditional field-based National Forest Inventories (NFI) allow for estimates of change in growing stock and biomass, and do so primarily by periodic field measurement using permanent sample plots (PSPs; [17]). Not all NFIs are initially designed for carbon stock assessments however, and measurements may not extend to all significant carbon pools [31]. Sampling specifications are ideally defined on the basis of the required precision, however, more often than not, are governed by time constraints and labour costs. There are difficulties of access in some areas and it may be more cost-effective to reduce sampling intensity in these areas and concentrate sampling effort on a few select classes. The use of terrestrial laser scanners (TLS) and drones may speed up the process of collecting data from which structural attributes can be estimated. Access to country specific models to estimate forest carbon stocks also presents a significant challenge. The allometric equations used to estimate tree volume and biomass are not available for all tropical forest types and species [17], and additional measurements by destructive harvesting would increase the survey costs [8]. Arguably the greatest challenge faced by countries is the lack of Government endorsed programs that instil a dedicated effort to consistent monitoring of forests at national scale [17]. Maintaining institutional capacity and drive is key to assessing the state of the forest resource with a view to sustainable management. Field inventory should ideally be multi-purpose and collect data to suit a range of stakeholders and so maximise use and investment.

Complementing field-based inventories with EO data allows for greater areal coverage and reduces the burden on field survey. EO data can be acquired wall-to-wall or on a sampling basis (in particular for very high resolution, VHR, data) across the region/nation of interest. Satellite observations can be used to estimate the area of forest classes (including degraded and intact forest states), for which volume and biomass densities can be extrapolated using field-based measurements [17]. Repeat observations of both EO and field data allow for ongoing assessments of changes in forest carbon stocks. Estimates of forest structure and above ground biomass (AGB) are also possible using SAR and LiDAR data. Appropriate satellite EO data that spans several decades is also available at moderate resolution from both optical, and over a shorter time period, SAR sensors. These data allow for longer term assessment of forest dynamics in response to both anthropogenic and natural disturbances. An integrated approach that combines multi-sensor EO and in situ data could form part of a systematic framework for monitoring changes in forest cover and carbon stocks. This would allow the implementation of a more complete MRV system, whereby the disturbance history, i.e., degradation type and long-term loss of carbon stocks in forest land, is needed to account for emissions arising from forest degradation. Offsetting these losses with accounting of long-term carbon gain incurred through afforestation and sustainable management practices may be an important consideration.

The operational readiness of the technology, in terms of satellite data availability, robustness of methods, large-area demonstrations and country operational examples, is evaluated in Table 2. Forest degradation mapping methods are largely considered in an R&D phase [21], with large-scale demonstrations (i.e., sub-national to national level), scaling from project to national level, automation of methods and tuning of algorithms for different forest types needed to pre-/operationalise methods for use in a REDD+ monitoring context. The lack of systematic observations by key EO sensors has hampered methods development; as such, large-scale demonstrations are few. Numerous case studies have, however, demonstrated a high potential for retrieving activity data on forest degradation, as well as uncovering history of land use and other causes of disturbance using EO data. Data fusion can assist in mapping degradation, but obtaining near-coincident data is difficult with little to no coordination of SAR and optical satellite observations by space agencies. Countries need access to low cost, high to VHR data to detect changes in forest cover and carbon stocks and so include estimates of emissions from degradation in their forest inventorys. Access to free high resolution optical data has only recently become available with the launch of Sentinel-2. Other high resolution data, including SAR, are only available from commercial suppliers. With the exception of Sentinel-2, high resolution data are tasked on request, often resulting in fragmented spatial and temporal coverage. There may be a case for using a sample of high to VHR images within a wall-to-wall monitoring system. 350c69d7ab

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