the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jul 2026
Plume Emission Rate Estimation using Gaussian Plumes in Remote Imaging Spectroscopy with SPIIRE
Abstract. The detection and quantification of methane and other trace gases emissions using remote sensing with imaging spectroscopy is an increasingly important tool for understanding local, regional, and global emissions. Traditional algorithms for quantifying the emission rate often rely on user-determined, instrument-dependent parameters and arbitrary plume boundaries. In this paper we present a new algorithm to estimate the emission rate based on a statistical inversion of a physical plume model that does not depend on such parameter choices or plume boundaries, termed SPIIRE. The method fully exploits instrument noise models, incorporates surface-albedo and atmospheric effects present in gas enhancement imagery, and is well suited for operational use. We present simulation results showing improved noise resilience, demonstrate performance against controlled-release experimental data using AVIRIS-3 and EMIT, and provide multiple examples using EMIT. We then show how the model can be extended to complex scenes having overlapping plumes and to spatially-distributed sources like landfills.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-3771', Anonymous Referee #1, 19 Aug 2026
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RC2: 'Comment on egusphere-2026-3771', Anonymous Referee #2, 27 Aug 2026
The manuscript introduces a novel method to quantify methane emissions. Not only individual point source emissions, but also overlapping plumes and diffuse emissions. Results show good agreement with controlled releases for the AVIRIS-3 instrument, which supports the validity of this method. The removal of user-defined parameters such as those needed in other quantification strategies - i.e. the IME method - allows for a more unambiguous methodology pipeline that could further benefit the automatization from ML models. The flexibility of the method to adapt to a large variety of plume shapes and distributions is remarkable and the corrections after accounting for the sensitivity metric seem very important. I want to congratulate the authors for this work, which I consider that introduces an important added-value to the field. However, before publication there are important (and not so important) comments that the authors should address before I recommend this work for publication.
Abstract
- Please, add some information about whether SPIIRE outperforms state-of-the-art methods (IME) for quantification.
Introduction
L19 - The concept of 'gas enhancement imagery' should be rephrase to reach a broader audience (express it more plainly). Please, include a little explanation of what you mean with this concept.
L32 - The effective wind speed is not only accounting for errors from plume length. It is an empirical way for also accounting for other aspects such as the acquisition angular configuration (Gorroño et al., 2026), turbulence effects, plume mask criteria, etc. Please, take into account in the text the broader role of the effective wind speed.
L36 - Please take also into account the instrument-scene angular configuration (Gorroño et al., (2026).
L52 - What about those plumes that cannot be modeled as a Gaussian plume? Limitations should be explained later in the text.
L55 - This could not be true. At this point, I think the author aims to remove the arbitrary and user-defined parameters to provide a more systematic basis for plume quantification. In this context, it is more objective. However, it is assumed that the emission can be described as a Gaussian plume, and this is not always true. Therefore, 'objective' is true within the author's proposed framework, but this limitation should be mentioned from the beginning.
L58 - 'Highly accurate' seems something that must be proven. I'd talk about the advantages of this method later on the discussion, after showing the results.
L61 – ‘variations in wind velocity’ - However, previously in the text, it was said that Gaussian plume models typically assume constant wind speed. Please, clarify.
L63 - Again, as 'highly accurate' (line 58), 'precise' is something that still has to be evaluated. Please, consider to limit the success of your method only after showing the results.
L63 - What happens if the whole plume is affected by noisy, low-signal pixels? This should be later explained in the text.
L75 - I'm skeptical about this, since landfill (example of area source) pixels might be highly biased due to surface sensitivity/artifacts.
L79 - Again, 'excellent fits' is an appreciation of the methodology that should be acknowledge after showing the results.
L85 - I'd write 'further discussion and conclusions' because discussions go first
Plume Model Inversion
L101 - d=1000 m. Why? This is then motivated from some reference?
L101 - Not sure what you mean with 'Pasquill-Gifford'. Please, clarify.
L102 - the spreading model we proposed... Adding 'we proposed' clarifies that the wind dependence of your selected spreading model does not exist.
L107 – ‘concentration length’ - Please, specify units.
L108 - 'at hand'. What do you mean? Please, clarify.
Eq.3 - Isn't the integration of the term dependent of z (assuming 0 - surface- to infinity - TOA) equal to 1/2? If you need to integrate from -infinity to + infinity to obtain the integral = 1, please, clarify why.
L111 - Please. clarify what you mean with ‘the data are noisy’. I'd rephrase it as sth like 'the model also needs to account for the uncertainty of the data'.
L115 - Please, explain the concepts of sensitivity and uncertainty with further detail. It must be clear why you build the probability distribution function in that way.
L121 - Maybe you should comment something about double plumes to emphasize the limitations of this assumption (Gorroño, 2026; Sanchez-Garcia, 2022)
L129 - So, a previous step is to manually delineate the plume. This first involves identifying the plume (detection). Please, explain the whole process in a more step-to-step manner, so the reader can better understand the algorithm.
L133 - Then, you previously set a per-pixel noise threshold? This has not been previously explained. Please, clarify.
L135 - This is not the emission rate
L141 - This assumption only allows for variations in wind direction, not wind speed value. Allowing variation of direction, but not in the value is something consistent? Please, clarify.
Section 2 - Please, consider a more structured explanation of the methodology.
L152 - Please, could you explain why this happens?
L159 - This is very important. If the underestimation is quite large, we cannot rely on this methodology. Please, address this point.
2.3. - Here the authors explain the IME method as some method where the user must first decide which is the downwind direction and then extract the corss-wind transects for the summatory. Typically, this isn't a requirement in the IME formulation. However, you could mention that this is somewhat equivalent to the IME formulation.
L174 - I think how the variables and their correspoding units are expressed is somewhat confusing. k1D must convert Q/u (kg/m) into ppm·m units. However, it is said that k1D converts the line concentration (which I expect to be li) into concentration length. However, the text says that the line concentration has units of kg/m2, which I think it's false.
Eq9. Please, explain with more detail how you obtained this expression. I expect this to be the part of the log-likelihood that is used for optimization in order to extract the the Q. This should be better explained. Moreover, you use the log-likelihood because it is simpler for optimization? This should also be explained.
Section 2. In general, I'd recommend the authors to invest more time in improving Section 2, since it is key to understand why this method works.
L175. How is the fetch length determined. Please, explain.
Results
L185 – ‘other gases’ - However, there might be exceptions. For instance, ammonia and ethylene plumes, which can also be detected with EMIT-like instruments, are somewhat different from methane plumes. These 2 gases exhibit a much lower atmospheric lifetime and they react more rapidly. This also happens with NO2. Despite of this difference, is this method still valid?
L193 - What do you mean with superior noise performance? Please, explain. In Eq. 7, it was previously commented that the uncertainty of Q following this model is underestimated, which I think is the major flaw from this work.
L195 - Please, use the units of CQ and also justify why you selected this range. I guess this range was selected since it satisfied emission rates that were enough to detect the related emision from EMIT.
L197 - How did you inject the simulations into the retrieval data? Simulations are first introduced into L1 data end then the retrieval is carried out? Or are they simply added to the retrieval? If the latter, then you should comment the limitations and risks of doing this.
L199 - As it is mentioned in the text, this improvement in CQ characterization must be interpreted carefully. In my opinion, this comparison should have been done with plumes that are not coming from a Gaussian model, since it is exactly the model that SPIIRE assumes. In contrast, there are publicly available WRF-LES simulations (see Gorroño, 2026) with plumes with more different shapes, accounting for realistic turbulence, changing winds, etc. Repeating this comparison with this kind of plumes would add more confidence in the improvement of SPIIRE in reference to the IME method.
Fig1. What can you say about the dispersion of values in reference to the 1:1 line? Why is not perfect for SPIIRE? Please, extend the analysis in this regard.
L119 - Why only 30 s? Is this time enough to be representative of the plume that is captured by the instrument? Please, clarify.
* There is also the CSF method. Does SPIIRE also outperform this method? I think this is important because, if so, it would add a very nice added-value.
L227 - This is a very important point. One of the assumptions is the steady-state which implies constant Q and u, right? These conditions are probably not met in real cases. Please, address this point.
L239 - One of the point of this paper is the improvement of SPIIRE in reference to the IME method. However, the comparison with the EMIT instrument has not been carried out. The authors explain that this cannot be done since the controlled releases were not enough to tuned the IME parameters for EMIT. However, there are other ways of tuning this parameters such as simulations. In this regard, I also saw in the CRs paper that the JPL team submitted emission quantification based on Simple IME with EMIT for this very same emissions. It'd be nice to compare with these values.
L246 - I'm not sure whether you can confirm this. You can say that the sensitivity correction yields a plume that can be fit into a Gaussian plume that assumes constant Q. However, this is only a possibility. Please, clarify.
L249 - Please, better refer to Q instead of CQ.
L251 - 'excellent' might be too much since the transects from Figure 5 do not follow a perfect match. Again, accounting for the uncertainty due to the mismatch is very important here.
3.3. In this regard, it is very important to first contrain the uncertainty of your emission estimate (see Eq.7). Once you can constrain the uncertainty related to the mismatch you would be able to really analyze the quantification using SPIIRE.
* Please, at some point clarify all the limitations of SPIIRE.
* Consider including somewhere in the text, what happens when there is overlap of plume and artifact, or a surrounding artifact.
4.3. The resulting plume from the model seems to match the original retrieval. However, I'm skeptical whether this methodology can be trusted. Using each landfill pixel as the origin of a Gaussian plume and then do the combined inversion might provide an unstable result, meaning that e.g. including/removing one pixel as one of the sources would yield a totally different result. Maybe you can use some reference data: Madrid (MEDUSA project) or Romania landfills? If it is not feasible to access real reference data from landfills, you might do an analysis about the stability of your method by removing/adding pixels and see the stability of your result, or similar. As you suggest, IME-based quantification (or even CSF) is not really good for landfills, so this method could be a very good alternative. Moreover, what can you say about the potential artifacts arising from the landfill surface?
Citation: https://doi.org/10.5194/egusphere-2026-3771-RC2
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Overall assessment. This paper presents a new Gaussian plume method to infer the normalized emission rate Q/u from point sources using remote sensing observations of plumes, also including sensitivity/uncertainty (S/U) information for individual pixels. The method is a valuable addition to current methods, the use of S/U is an important new advance, and the presentation is clear. I have two general concerns expressed in my specific comments that can easily be corrected to the extent that the authors deem appropriate: (1) the authors overstate the success of their method over the IME method, which they also seem to confuse with the cross-sectional flux method; (2) the references are very JPL-centric and ignore or shortchange a lot of literature on inferring emissions from point sources including by Gaussian plume methods.
Specific comments