Abstract
The radiative power (MW) output of a gas flare is a useful metric from which the rate of methane combustion and carbon dioxide emission can be inferred for inventorying purposes and regular global surveys based on such assessments are now being used to keep track of global gas flare reduction efforts. Several multispectral remote sensing techniques to estimate gas flare radiative power output have been developed for use in such surveys and single band approaches similar to those long used for the estimation of landscape fire radiative power output (FRP) can also be applied. The MIR-Radiance method, now used for FRP retrieval within the MODIS active fire products, is one such single band approach—but its applicability to gas flare targets (which are significantly hotter than vegetation fires) has not yet been assessed. Here we show that the MIR-Radiance approach is in fact not immediately suitable for retrieval of gas flare FRP due to their higher combustion temperatures but that switching to use data from a SWIR (rather than MWIR) spectral channel once again enables the method to deliver unbiased FRP retrievals. Over an assumed flaring temperature range of 1600–2200 K we find a maximum FRP error of ±13.6% when using SWIR observations at 1.6 µm and ±6.3% when using observations made at 2.2 µm. Comparing these retrievals to those made by the multispectral VIIRS ‘NightFire’ algorithm (based on Planck Function fits to the multispectral signals) we find excellent agreement (bias = 0.5 MW, scatter = 1.6 MW). An important implication of the availability of this new SWIR radiance method for gas flare analysis is the potential to apply it to long time-series from older and/or more spectrally limited instruments, unsuited to the use of multispectral algorithms. This includes the ATSR series of sensors operating between 1991–2012 on the ERS-1, ERS-2 and ENVISAT satellites and such long-term data can be used with the SWIR-Radiance method to identify key trends in global gas flaring that have occurred over the last few decades.
Highlights
Introduction and BackgroundThe rate of radiative heat release from landscape fire events is typically estimated using remotely sensed retrievals of Fire Radiative Power (FRP) e.g., [1,2,3]
We have evaluated for the first time the efficacy of single channel satellite Earth observation methods of fire radiative power (FRP) retrieval from flaring sites
FRP-retrieval approaches like the middle infrared (MIR) radiance method, originally designed for use at sites of landscape fires [1,2], can operate without the constraints of ancillary data requirements and are insensitive to inter-channel spatial mis-registration effects
Summary
The rate of radiative heat release from landscape fire events is typically estimated using remotely sensed retrievals of Fire Radiative Power (FRP) e.g., [1,2,3]. Evaluating the application of single-band methods such as the MIR-Radiance approach will provide information useful for assessing the accuracy of the fixed temperature assumptions previously employed by the NightFire algorithm in the case of smaller, less radiant, gas flares–where the Planck-function fits cannot be achieved–and if shown applicable to gas flares, a single-band method (avoiding any restrictive or inaccurate temperature assumptions) would significantly widen the range of situations and EO instruments that could be used to assess global gas flaring FRP This is because multi-spectral algorithms require sensors delivering multiple bands of data over gas flare targets at night, whereas for example the ATSR series of sensors for example has a record of night-time observations back to the mid- to-early 1990’s but only in the SWIR, MIR and LWIR [36] (though the LWIR contribution from gas flares is typically small and difficult to isolate [20]). Re-processing of long-term EO data archives such as these might provide further evidence of the success or otherwise of global gas flaring reduction efforts, should appropriate algorithms with data requirements less stringent than those of the NightFire-type multispectral spectral fitting approach become available [33]
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