the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Oct 2024
Dynamic island mass effect from space. Part I: detecting the extent
Abstract. In the vast Pacific Ocean, remote islands and atolls induce mesoscale and sub-mesoscale processes that significantly impact the surrounding oligotrophic ocean, collectively referred to as the Island Mass Effect (IME). These processes include nutrient upwelling and phytoplankton biomass enhancement around islands, creating spatial and temporal heterogeneity in biogeochemical properties. Previous algorithms developed for detecting IME using satellite data are based on monthly or longer averages of satellite derived chlorophyll concentrations. As such, they tend to underestimate the true extent of this phenomenon because they do not take into account sub-mesoscale and short term temporal variations and because of the sensitivity of the detection algorithm to single pixel variability. Here we present a new approach that enhances satellite data recovery by merging products from multiple sensors and applying the POLYMER atmospheric correction. By integrating modelled surface currents with higher temporal resolution satellite observations, we dynamically track chlorophyll enhancements associated with IME and the advection of detached patches and filaments over distances exceeding 1000 km from their source. Our findings, applied to four island groups in the South Pacific, suggest that the ecological influence of IME on the oligotrophic ocean is much larger than previously recognized. This work provides a foundation for improved mechanistic understanding of IME and suggests broader implications for ocean ecology in subtropical regions. The approach developed here could be also be applied in studies on biological responses to other mesoscale and sub-mesoscale processes in other parts of the world's oceans.
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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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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2670', Anonymous Referee #1, 23 Nov 2024
Dynamics of island mass effect from space. Part I: detecting the extent
Guillaume Bourdin, Lee Karp-Boss, Fabien Lombard, Gabriel Gorsky, and Emmanuel Boss
Summary
The manuscript titled “Dynamics of island mass effect. Part 1: detecting the extent” describes updated algorithms to detect mesoscale and sub-mesoscale processes near remote islands and atolls, termed the “Island Mass Effect” (IME) using satellite remote sensing data. The authors state that existing algorithms for detecting the IME (Messie et al., 2022 underestimate the effect due to using low temporal and spatial resolution satellite data. This study utilizes remote sensing data from multiple sensors to increase temporal resolution and apply a different atmospheric correction scheme (POLYMER) that results in more data. These updated IME algorithms are applied to merged satellite data collected over four island groups in the South Pacific. The results indicate the ecological influence of the IME near these islands is more significant and dynamic than previously thought. The results indicate large phytoplankton blooms that can be advected 1000 km away from their source, seeding the nearby oligotrophic ocean. The overall results of this study indicate that the IME has a greater impact on food web dynamics and biogeochemical processes for waters in close proximity to these remote islands. The authors recommend future studies use higher temporal and spatial resolution satellite products and modeled surface currents to better identify and track sub-mesoscale filaments and eddies associated with the IME around remote islands.
Major comments
Introduction
- No major comments
Methods
- The Methods need substantial reorganizing and clarification:
- More details should be added to the POLYMER atmospheric correction description. Why does it improve data recovery in areas impacted by glint and adjacency effect? What version did you use? Where was it downloaded from? What was it run on? What flags were used? What ancillary data was used?
- You should also be citing this paper as well: François Steinmetz and Didier Ramon "Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER", Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 107780E (30 October 2018); https://doi.org/10.1117/12.2500232
- The headings in the Methods section seem disorganized to me. For Section 2.1 Level-3 Multi-satellite composites, you start with an intro paragraph and then have several subheadings. Consider merging the intro paragraph into the 2.1.1 section. The 2.1.2 In situ data sub-section seems out of place in this section, consider adding a new section just for in situ data and matchups. Perhaps this organization with just two sections?
- More details should be added to the POLYMER atmospheric correction description. Why does it improve data recovery in areas impacted by glint and adjacency effect? What version did you use? Where was it downloaded from? What was it run on? What flags were used? What ancillary data was used?
- 1 Level-3 satellite products computation
- (This section will include the writing in the opening paragraph. 2.1.1, 2.1.4, 2.1.5)
- 2 In situ data and matchups
- (This section will include section 2.1.2 and 2.1.3)
- (This section will include section 2.1.2 and 2.1.3)
- The description of running POLYMER and l2gen should be in same paragraph/section. Right now, you have text on l2gen in the in situ and satellite matchups section which seems out of place.
- In Section 2.1.1, there is no description of how data was processed to Level-3 format. It seems to stop at L2.
- Figure 1 seems okay in the Methods because it is a figure of the workflow. However, should Figures 2-3 be in the “Assessment” section since it is showing the results of the workflow? I don’t think you should be referencing results figures in the Methods, save that for the Results (or “Assessment”).
Assessment
- Some of the text in the Assessment section would better belong in the Methods such as the description of merging and binning and the chl iteration step size
Minor comments
The title on the preprint PDF is different than what is in the system.
Line 5: Consider adding a after chlorophyll. Same on Line 20.
Line 8: Define POLYMER
Line 18: The way this sentence is written makes it seem like “their wake downstream..” refers to the winds and currents. Reword to make this more clear.
Line 28: Consider adding the citation to the end of this sentence.
Line 35: Consider changing “They” to “The authors”
Line 77: Should define these satellite mission acronyms
Line 79: More information on the POLYMER atmospheric correction scheme should be included here. See major comment above.
Line 84: Include the time frame of data collected. What is “all”?
Line 84: Why did you download L1A data instead of L1B? Review differences here: https://oceancolor.gsfc.nasa.gov/resources/docs/product-levels/#:~:text=Level%201B%20data%20are%20Level,had%20instrument%2Fradiometric%20calibrations%20applied.&text=Level%202%20data%20consist%20of,the%20source%20Level%201%20data.
Line 85: What Copernicus repository? Provide link(s).
Line 88: What did you use to project the satellite data onto a plate-carre reference grid using NN interpolation?
Line 91: Confused on how this is surface-integrated chla when you’re just summing chla concentration in each pixels by the area? Where does depth come into play?
Line 96: Not sure you need to hyphenate hyperspectral
Line 110: Capitalize Python
Line 112: You describe how all satellite data is processed to Level-3 using same scheme as aforementioned but this was never described.. You don’t introduce the terms reprojecting, nudging, or merging until now. What is nudging?
Line 114: OCSSW stands for Ocean Color Science Software
Line 119: Consider adding the satellite overpass times for each sensor. How do they match up with the 10:30am local time for in situ data collection?
Line 120: The sentence about recommended Level-2 masks needs a citation. Masks or flags? Did you use recommended L2 or L3 flags? https://oceancolor.gsfc.nasa.gov/resources/atbd/ocl2flags/
Line 120: Are you working with Rrs or nLw? Are these both included when running l2gen and POLYMER?
Line 136: What is GlobColour?
Line 138: Why would this described merging strategy require simulation of 510 nm band?
Line 162: This sentence should have a citation
Line 168: Did you use the 300m spatial resolution of OLCI?
Section 2.1.4: Did you merge data from all 6 satellite sensors? What spatial resolution did you use for merged product? If 1km, then OLCI data was “upsampled”?
Line 174: Keep consistent- change to 1 kmLine 175: Need citation
Line 176: Arc-seconds seems like a weird unit here.. can you convert to degrees or m?
Section 2.2.1: What did you use to create masks and “manually correct” discrepancies? Python? GIS?
Line 207: This needs a citation
Line 247: Are the equations in the paranthesis supposed to be exactly the same?
Line 249: What does SEM stand for here?
Lines 307-310: Do these sentences belong in the Methods?
Line 311: I don’t think these figures are considered time-series? They are just snapshots, right?
Line 435: Change to [chla]- keep consistent
Figure 2: I wonder if labeling the islands on the map will help orient the readers?
Figure 4: What does “average or properties within the IME” mean?
End of review
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AC1: 'Reply on RC1', Guillaume Bourdin, 22 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2670/egusphere-2024-2670-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-2670', Anonymous Referee #2, 02 Dec 2024
This contribution investigates the Island Mass Effect (IME) in the Pacific Ocean, highlighting its potential significant impact on biogeochemical processes in oligotrophic waters surrounding remote islands and atolls. The study expands on the limitations of traditional remote sensing approaches that rely on L3 data with maximum resolution above 4km, which often fail to capture the full extent of sub-mesoscale and short-term temporal variations. It proposes an alternative enhanced approach that merges multi-sensor satellite data at a higher spatial resolution and integrates modelled surface currents to dynamically track chlorophyll enhancements associated with IME. The methodology is applied to four South Pacific island groups, suggesting that the ecological influence of IME may be larger than previously recognized, with important implications for broader ocean ecological studies.
Strengths of the Contribution
- Innovative Methodology: The research introduces a layered and carefully crafted methodology, combining ocean color data processing with modeled dynamic tracking of chlorophyll patches and filaments, as well as in situ calibration and validation measurements. This custom approach, it is proposed, addresses the limitations of legacy IME detection algorithms and significantly improves the spatial resolution of feature retrieval.
- Broader Implications: By showcasing the potential broader ecological influence of IME, the study advances our understanding of global oceanic biogeochemistry. It provides a valuable foundation for investigating these processes in strongly stratified systems, such as the tropical western Pacific, where islands and submerged topography, as defined here, may cause significant perturbations that lead to enhanced productivity. This is particularly well articulated in L377, where the authors underscore the importance of IME. These regional results should be further evaluated in the context of global biogeochemical cycles, as well as in other island systems where background ocean biogeochemistry creates more eutrophic conditions (e.g., Galapagos, Ascension, Azores, etc.).
Areas for Improvement
While the paper is a worthy contribution, some areas could benefit from refinement:
- General study design (sensor choice justification): The study effectively combines data from multiple sensors, including MODIS Terra, to detect changes in chlorophyll associated with the Island Mass Effect. While this approach is justified given the study's focus on detecting relative changes rather than establishing absolute chlorophyll concentrations, the authors should include a brief discussion of the known issues with MODIS Terra. Specifically, acknowledging its calibration challenges, and potential limitations for climate-quality data would enhance the transparency and robustness of the methodology. This acknowledgment would reassure readers that these factors have been considered and appropriately addressed in the study's design.
- Section 3 (Assessment): The results section contains substantial material (e.g., L315-320) that is methodological in nature. For clarity and better flow, this information should be moved to the methods section. This reorganization will help strengthen the distinction between methodology and results.
Citation: https://doi.org/10.5194/egusphere-2024-2670-RC2 -
AC2: 'Reply on RC2', Guillaume Bourdin, 22 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2670/egusphere-2024-2670-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-2670', Anonymous Referee #1, 23 Nov 2024
Dynamics of island mass effect from space. Part I: detecting the extent
Guillaume Bourdin, Lee Karp-Boss, Fabien Lombard, Gabriel Gorsky, and Emmanuel Boss
Summary
The manuscript titled “Dynamics of island mass effect. Part 1: detecting the extent” describes updated algorithms to detect mesoscale and sub-mesoscale processes near remote islands and atolls, termed the “Island Mass Effect” (IME) using satellite remote sensing data. The authors state that existing algorithms for detecting the IME (Messie et al., 2022 underestimate the effect due to using low temporal and spatial resolution satellite data. This study utilizes remote sensing data from multiple sensors to increase temporal resolution and apply a different atmospheric correction scheme (POLYMER) that results in more data. These updated IME algorithms are applied to merged satellite data collected over four island groups in the South Pacific. The results indicate the ecological influence of the IME near these islands is more significant and dynamic than previously thought. The results indicate large phytoplankton blooms that can be advected 1000 km away from their source, seeding the nearby oligotrophic ocean. The overall results of this study indicate that the IME has a greater impact on food web dynamics and biogeochemical processes for waters in close proximity to these remote islands. The authors recommend future studies use higher temporal and spatial resolution satellite products and modeled surface currents to better identify and track sub-mesoscale filaments and eddies associated with the IME around remote islands.
Major comments
Introduction
- No major comments
Methods
- The Methods need substantial reorganizing and clarification:
- More details should be added to the POLYMER atmospheric correction description. Why does it improve data recovery in areas impacted by glint and adjacency effect? What version did you use? Where was it downloaded from? What was it run on? What flags were used? What ancillary data was used?
- You should also be citing this paper as well: François Steinmetz and Didier Ramon "Sentinel-2 MSI and Sentinel-3 OLCI consistent ocean colour products using POLYMER", Proc. SPIE 10778, Remote Sensing of the Open and Coastal Ocean and Inland Waters, 107780E (30 October 2018); https://doi.org/10.1117/12.2500232
- The headings in the Methods section seem disorganized to me. For Section 2.1 Level-3 Multi-satellite composites, you start with an intro paragraph and then have several subheadings. Consider merging the intro paragraph into the 2.1.1 section. The 2.1.2 In situ data sub-section seems out of place in this section, consider adding a new section just for in situ data and matchups. Perhaps this organization with just two sections?
- More details should be added to the POLYMER atmospheric correction description. Why does it improve data recovery in areas impacted by glint and adjacency effect? What version did you use? Where was it downloaded from? What was it run on? What flags were used? What ancillary data was used?
- 1 Level-3 satellite products computation
- (This section will include the writing in the opening paragraph. 2.1.1, 2.1.4, 2.1.5)
- 2 In situ data and matchups
- (This section will include section 2.1.2 and 2.1.3)
- (This section will include section 2.1.2 and 2.1.3)
- The description of running POLYMER and l2gen should be in same paragraph/section. Right now, you have text on l2gen in the in situ and satellite matchups section which seems out of place.
- In Section 2.1.1, there is no description of how data was processed to Level-3 format. It seems to stop at L2.
- Figure 1 seems okay in the Methods because it is a figure of the workflow. However, should Figures 2-3 be in the “Assessment” section since it is showing the results of the workflow? I don’t think you should be referencing results figures in the Methods, save that for the Results (or “Assessment”).
Assessment
- Some of the text in the Assessment section would better belong in the Methods such as the description of merging and binning and the chl iteration step size
Minor comments
The title on the preprint PDF is different than what is in the system.
Line 5: Consider adding a after chlorophyll. Same on Line 20.
Line 8: Define POLYMER
Line 18: The way this sentence is written makes it seem like “their wake downstream..” refers to the winds and currents. Reword to make this more clear.
Line 28: Consider adding the citation to the end of this sentence.
Line 35: Consider changing “They” to “The authors”
Line 77: Should define these satellite mission acronyms
Line 79: More information on the POLYMER atmospheric correction scheme should be included here. See major comment above.
Line 84: Include the time frame of data collected. What is “all”?
Line 84: Why did you download L1A data instead of L1B? Review differences here: https://oceancolor.gsfc.nasa.gov/resources/docs/product-levels/#:~:text=Level%201B%20data%20are%20Level,had%20instrument%2Fradiometric%20calibrations%20applied.&text=Level%202%20data%20consist%20of,the%20source%20Level%201%20data.
Line 85: What Copernicus repository? Provide link(s).
Line 88: What did you use to project the satellite data onto a plate-carre reference grid using NN interpolation?
Line 91: Confused on how this is surface-integrated chla when you’re just summing chla concentration in each pixels by the area? Where does depth come into play?
Line 96: Not sure you need to hyphenate hyperspectral
Line 110: Capitalize Python
Line 112: You describe how all satellite data is processed to Level-3 using same scheme as aforementioned but this was never described.. You don’t introduce the terms reprojecting, nudging, or merging until now. What is nudging?
Line 114: OCSSW stands for Ocean Color Science Software
Line 119: Consider adding the satellite overpass times for each sensor. How do they match up with the 10:30am local time for in situ data collection?
Line 120: The sentence about recommended Level-2 masks needs a citation. Masks or flags? Did you use recommended L2 or L3 flags? https://oceancolor.gsfc.nasa.gov/resources/atbd/ocl2flags/
Line 120: Are you working with Rrs or nLw? Are these both included when running l2gen and POLYMER?
Line 136: What is GlobColour?
Line 138: Why would this described merging strategy require simulation of 510 nm band?
Line 162: This sentence should have a citation
Line 168: Did you use the 300m spatial resolution of OLCI?
Section 2.1.4: Did you merge data from all 6 satellite sensors? What spatial resolution did you use for merged product? If 1km, then OLCI data was “upsampled”?
Line 174: Keep consistent- change to 1 kmLine 175: Need citation
Line 176: Arc-seconds seems like a weird unit here.. can you convert to degrees or m?
Section 2.2.1: What did you use to create masks and “manually correct” discrepancies? Python? GIS?
Line 207: This needs a citation
Line 247: Are the equations in the paranthesis supposed to be exactly the same?
Line 249: What does SEM stand for here?
Lines 307-310: Do these sentences belong in the Methods?
Line 311: I don’t think these figures are considered time-series? They are just snapshots, right?
Line 435: Change to [chla]- keep consistent
Figure 2: I wonder if labeling the islands on the map will help orient the readers?
Figure 4: What does “average or properties within the IME” mean?
End of review
-
AC1: 'Reply on RC1', Guillaume Bourdin, 22 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2670/egusphere-2024-2670-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-2670', Anonymous Referee #2, 02 Dec 2024
This contribution investigates the Island Mass Effect (IME) in the Pacific Ocean, highlighting its potential significant impact on biogeochemical processes in oligotrophic waters surrounding remote islands and atolls. The study expands on the limitations of traditional remote sensing approaches that rely on L3 data with maximum resolution above 4km, which often fail to capture the full extent of sub-mesoscale and short-term temporal variations. It proposes an alternative enhanced approach that merges multi-sensor satellite data at a higher spatial resolution and integrates modelled surface currents to dynamically track chlorophyll enhancements associated with IME. The methodology is applied to four South Pacific island groups, suggesting that the ecological influence of IME may be larger than previously recognized, with important implications for broader ocean ecological studies.
Strengths of the Contribution
- Innovative Methodology: The research introduces a layered and carefully crafted methodology, combining ocean color data processing with modeled dynamic tracking of chlorophyll patches and filaments, as well as in situ calibration and validation measurements. This custom approach, it is proposed, addresses the limitations of legacy IME detection algorithms and significantly improves the spatial resolution of feature retrieval.
- Broader Implications: By showcasing the potential broader ecological influence of IME, the study advances our understanding of global oceanic biogeochemistry. It provides a valuable foundation for investigating these processes in strongly stratified systems, such as the tropical western Pacific, where islands and submerged topography, as defined here, may cause significant perturbations that lead to enhanced productivity. This is particularly well articulated in L377, where the authors underscore the importance of IME. These regional results should be further evaluated in the context of global biogeochemical cycles, as well as in other island systems where background ocean biogeochemistry creates more eutrophic conditions (e.g., Galapagos, Ascension, Azores, etc.).
Areas for Improvement
While the paper is a worthy contribution, some areas could benefit from refinement:
- General study design (sensor choice justification): The study effectively combines data from multiple sensors, including MODIS Terra, to detect changes in chlorophyll associated with the Island Mass Effect. While this approach is justified given the study's focus on detecting relative changes rather than establishing absolute chlorophyll concentrations, the authors should include a brief discussion of the known issues with MODIS Terra. Specifically, acknowledging its calibration challenges, and potential limitations for climate-quality data would enhance the transparency and robustness of the methodology. This acknowledgment would reassure readers that these factors have been considered and appropriately addressed in the study's design.
- Section 3 (Assessment): The results section contains substantial material (e.g., L315-320) that is methodological in nature. For clarity and better flow, this information should be moved to the methods section. This reorganization will help strengthen the distinction between methodology and results.
Citation: https://doi.org/10.5194/egusphere-2024-2670-RC2 -
AC2: 'Reply on RC2', Guillaume Bourdin, 22 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2670/egusphere-2024-2670-AC2-supplement.pdf
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Guillaume Bourdin
Lee Karp-Boss
Fabien Lombard
Gabriel Gorsky
Emmanuel Boss
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(11124 KB) - Metadata XML
-
Supplement
(41918 KB) - BibTeX
- EndNote
- Final revised paper