the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Jan 2024
The ddeq Python library for point source quantification from remote sensing images (Version 1.0)
Abstract. Anthropogenic emissions from “hotspots”, i.e. cites, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as Gaussian plume inversion, cross sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output as well as tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter Notebooks included in the library, for example, for NO2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO2 and NO2 images from the Copernicus CO2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1153 KB) - Metadata XML
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Supplement
(45766 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2936', Robert Roland Nelson, 29 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-RC1-supplement.pdf
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AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-AC1-supplement.pdf
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AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
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RC2: 'Comment on egusphere-2023-2936', Anonymous Referee #2, 25 Mar 2024
Kuhlmann et al. present an open-source Python library for quantifying point sources of atmospheric trace gases observed by satellites. The library (ddeq) is developed primarily for CO2 and NOx point sources as seen by TROPOMI and the future CO2M mission. It includes tools for data preprocessing (e.g., plume detection and background estimation), emission rate quantification (e.g., CSF, IME, Gaussian plume methods), and data postprocessing (e.g., visualization). The paper is well written and the ddeq library it describes is clearly a valuable contribution to the atmospheric composition remote sensing community. There are several opportunities for continued development of new tools and features, including for other trace gases and satellite missions. I recommend the paper be accepted for publication subject to the minor comments below.
Comments
- L1: It should be clarified that these are atmospheric emissions (as is done in line 13). E.g., “Atmospheric emissions from anthropogenic hotspots…” or “Anthropogenic emissions of air pollutants from hotspots…” or something similar.
- L25: It would be more appropriate to describe Sentinel-2 as “multispectral” or “broadband” (vs. “hyperspectral) given its much broader spectral channels than many other instruments.
- Figure 1: Please mention in the caption what the two different sections of the yellow polygons represent.
- L87-92: I don’t really understand this approach to plume “detection”. Doesn’t it assume an already detected plume? Otherwise, it would “detect” a plume downwind of any assumed source – even though one might not be detectable in reality.
- Is the Gaussian plume method still considered lightweight when optimizing the nonlinear (dispersion) parameters?
- L152-153: How are the line integrals at different distances x combined for a polygon extending from x1 to x2? Is the average used?
- L345-346: The ‘rs_data’ object could use more explanation. I wasn't sure what the ‘variable’ of that dataset might be.
- How is ‘rs_data’ different from ‘datasets’, and winds’’ from ‘wind_folder’?
- L457: The phrasing here assumes DIV is always applied to XCO2.
- L491-493: Perhaps remind the reader that some of the plots from the paper were created with ddeq visualization methods – I believe Fig. 1 and 5?
Typos and errors
- L35: I do not understand this sentence, please clarify: “A prototype system of the European CO2MVS capacity is build in CoCO2 project”
- L52-53: “but they were not included here” is repeated twice in the sentence.
- L76: “approach” -> “approaches” and “image” -> “images”
- L176: “extend” -> “extent”
- L275: sentence is confusing, should it be “used is known” ?
- 391: “constraint” -> “constrain”
- 399: “is” -> “are”
Citation: https://doi.org/10.5194/egusphere-2023-2936-RC2 -
AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-AC1-supplement.pdf
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2936', Robert Roland Nelson, 29 Feb 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-RC1-supplement.pdf
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AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-AC1-supplement.pdf
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AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
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RC2: 'Comment on egusphere-2023-2936', Anonymous Referee #2, 25 Mar 2024
Kuhlmann et al. present an open-source Python library for quantifying point sources of atmospheric trace gases observed by satellites. The library (ddeq) is developed primarily for CO2 and NOx point sources as seen by TROPOMI and the future CO2M mission. It includes tools for data preprocessing (e.g., plume detection and background estimation), emission rate quantification (e.g., CSF, IME, Gaussian plume methods), and data postprocessing (e.g., visualization). The paper is well written and the ddeq library it describes is clearly a valuable contribution to the atmospheric composition remote sensing community. There are several opportunities for continued development of new tools and features, including for other trace gases and satellite missions. I recommend the paper be accepted for publication subject to the minor comments below.
Comments
- L1: It should be clarified that these are atmospheric emissions (as is done in line 13). E.g., “Atmospheric emissions from anthropogenic hotspots…” or “Anthropogenic emissions of air pollutants from hotspots…” or something similar.
- L25: It would be more appropriate to describe Sentinel-2 as “multispectral” or “broadband” (vs. “hyperspectral) given its much broader spectral channels than many other instruments.
- Figure 1: Please mention in the caption what the two different sections of the yellow polygons represent.
- L87-92: I don’t really understand this approach to plume “detection”. Doesn’t it assume an already detected plume? Otherwise, it would “detect” a plume downwind of any assumed source – even though one might not be detectable in reality.
- Is the Gaussian plume method still considered lightweight when optimizing the nonlinear (dispersion) parameters?
- L152-153: How are the line integrals at different distances x combined for a polygon extending from x1 to x2? Is the average used?
- L345-346: The ‘rs_data’ object could use more explanation. I wasn't sure what the ‘variable’ of that dataset might be.
- How is ‘rs_data’ different from ‘datasets’, and winds’’ from ‘wind_folder’?
- L457: The phrasing here assumes DIV is always applied to XCO2.
- L491-493: Perhaps remind the reader that some of the plots from the paper were created with ddeq visualization methods – I believe Fig. 1 and 5?
Typos and errors
- L35: I do not understand this sentence, please clarify: “A prototype system of the European CO2MVS capacity is build in CoCO2 project”
- L52-53: “but they were not included here” is repeated twice in the sentence.
- L76: “approach” -> “approaches” and “image” -> “images”
- L176: “extend” -> “extent”
- L275: sentence is confusing, should it be “used is known” ?
- 391: “constraint” -> “constrain”
- 399: “is” -> “are”
Citation: https://doi.org/10.5194/egusphere-2023-2936-RC2 -
AC1: 'Reply on RC1', Gerrit Kuhlmann, 15 Apr 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2023-2936/egusphere-2023-2936-AC1-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1153 KB) - Metadata XML
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Supplement
(45766 KB) - BibTeX
- EndNote
- Final revised paper