the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2024
Investigating the responses of sun-induced chlorophyll fluorescence, gross primary production and their inter-relationship to abiotic factors changes in a temperate deciduous forest
Abstract. Far-red Sun-Induced chlorophyll Fluorescence (SIF) is increasingly used as a proxy of vegetation Gross Primary Production (GPP) across different ecosystems and at spatiotemporal resolutions going from proximal to satellite-based remote sensing measurements. However, the use of SIF to probe variations in GPP in forests is challenged by (1) confounding factors such as canopy structure and sun-canopy geometry, and by (2) leaf physiological and biochemical properties along with abiotic factors (light intensity, temperature, soil water content, atmospheric vapour pressure deficit, etc.) that can influence SIF and GPP in a different way. To provide insights into understanding the complex drivers of GPP and SIF variations and of their relationships, we examined how SIF and GPP changed at daily and seasonal scales and how canopy structure and environmental conditions affected SIF and GPP relationships in a deciduous oak forest. To do so, we combined canopy scale SIF measurements, spectral vegetation indices, environmental variables measurements, including diffuse and direct radiations in the spectral range of the Photosynthetically Active Radiation (PAR), air and canopy temperature, soil water content (SWC), atmospheric Vapour Pressure Deficit (VPD), and GPP estimated from eddy covariance measurements. Canopy chlorophyll fluorescence was also measured using an active system with an artificial light source, referred to as LIF (LED Induced chlorophyll Fluorescence) hereafter. Further, Random Forest (RF) models were used to predict SIF and GPP and to analyse the responses of SIF and GPP to environmental drivers. The results show that both SIF and GPP variations and their relationships were dependent on the temporal scale considered. At the seasonal scale, The data show that leaf and canopy properties variations, seasonal cycle of PAR, and other abiotic factors such as VPD and SWC control not only SIF and GPP variations, but also their relationships. Further, during extreme weather conditions (heatwaves observed in 2022 in: mid-June (DOY: 166-169), mid-July (DOY: 196-199), and early August (DOY: 218-224)), we observed that SIF and reflectance-based Vegetation Indices (VIs), such as Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation index (NIRv), and also SIF and PAR are uncorrelated, while GPP, SIF, passive SIF yield (SIFy) and active chlorophyll fluorescence yield (FyieldLIF) strongly decreased. This indicates that during these severe abiotic conditions SIF stayed a usable proxy of GPP, while VIs cannot be used to track changes in vegetation physiology. This specific response of SIF compared to VIs underlined the interest of SIF to monitor GPP under severe abiotic conditions. At the diurnal timescale, the results also revealed that the saturation of the relationship between GPP and SIF was not only dependent on PAR, but also on the fraction of diffuse to total PAR, as well as on VPD, SWC, and air and canopy temperature. The other key finding was that sun geometry angles had strong effects on GPP and SIF variations. This result highlights that using ground-based SIF measurements to validate satellite measurements at coarse spatial and temporal resolutions can therefore be very difficult, due to confounding factors whose effects are significant and may vary from one site to another, especially in forest canopies.
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-657', Anonymous Referee #1, 30 Apr 2024
This study used ground-based observations to examine the relationship between far-red Sun-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP) across different temporal scales. They aimed to tackle the complexities arising from canopy structures, sun-canopy geometry, and physiological factors affecting SIF and GPP. By integrating canopy-scale SIF measurements with environmental variables, they explored daily and seasonal variations and the impacts of extreme weather events like heatwaves on these relationships. Additionally, they utilized Random Forest models to predict SIF and GPP, providing insights into how these factors interact under different environmental conditions.
Overall, analyzing the SIF-GPP relationship has always been a hot topic, especially the changes in their relationship under different environmental conditions. It is a very interesting topic and has implications for large-scale photosynthesis monitoring. However, the current analysis of this study is far from their research goals. The analyses are relatively not solid enough and the new information that can be provided is limited. My opinion is to reject the manuscript but encourage resubmission. I hope the author will make serious revisions and improve the quality.
The five main comments correspond to the five figures in the results section
- Analyzing the seasonality of various variables, as shown in Figure 1, is fundamental. Two minor suggestions: 1. Can the description of variable relationships be more quantitative? 2. Regarding short-term heatwave events, first, please delineate the heatwave periods with circles in the figure, and second, I recommend that the author examines the changes in variables during heatwaves (possibly even hourly) in more detail, as currently, we rely almost exclusively on visual inspection to capture information.
- Could there be a more quantitative and detailed analysis of the relationship between GPP-SIF and environmental factors? Currently, the author has simply used the same graph with different density plots of environmental variables. For instance, could the influence of variables on the relationship be explored based on different bins? Additionally, it is difficult to distinguish between sunny and cloudy conditions.
- Fig. 3 is somewhat confusing. Is it meant to show the relationship between daily GPP and daily SIF? It appears more like an analysis of the relationship between half-hourly or hourly GPP and SIF over more than 100 days during the growing season. Additionally, could you use a boxplot to more clearly demonstrate the relationship between R2 and environmental variables? From figure d, it is not clear how soil moisture specifically affects the relationship.
- This analysis appears quite abruptly. First, why choose the two MODIS overpass times; second, how does the author conclude that the afternoon data is more non-linear, based solely on R2? In fact, using linear fitting, the R2 for the afternoon data might also be higher. Since the author tended to attribute this to physiological regulation, could this analysis include some specific environmental variables? Currently, it's unclear what conclusions this analysis specifically supports.
- I have significant reservations about the analysis in Figure 5. Firstly, what time scale is being analyzed? Secondly, when building the SIF prediction model, why use several similar vegetation indices simultaneously? It is hard to understand the conclusion that SWC is most important for SIF, as from the seasonal variations shown in Figure 1, SWC and SIF seem unrelated, whereas NIRv changes are similar. Similar doubts apply to the GPP model, such as the negligible contribution from NIRv, which is likely obscured by SIF and other vegetation indices. Is it possible to analyze the contribution of different variables under varying environmental conditions, such as during a heatwave?
Other comments
- The manuscript contains several typographical errors, such as capital letters following commas (Line 30), GPP misspelled as 'GP'.. Please conduct a thorough review of the entire document for such errors.
- I recommend conducting a separate analysis for heatwave events, such as how half-hourly or hourly variables change during these periods. Currently, the mention of this is too brief.
- In the Introduction, I suggest more directly highlighting the innovative aspects of this study and how it differs from previous research. Currently, some content, such as advancements in random forests, seems somewhat unrelated to the core focus of this study.
- The contributions of canopy structure and physiology on SIF/GPP or SIF-GPP relationship the authors emphasized earlier seem to be under-discussed in the formal analysis. For instance, under heatwave or non-heatwave conditions, how much of the SIF or GPP variability is explained by NDVI or other vegetation indices? How much is explained by environmental factors?
- In the manuscript, there are numerous mentions of "significantly," such as on Line 261. I wonder if the difference in the slope of the SIF-GPP relationship between cloudy and sunny conditions has undergone statistical significance testing?
- From the discussion section, it seems that much of the analysis is consistent with previous studies (e.g., Line 370-375). What is the novelty of this study? Given the abundance of studies on the SIF-GPP relationship, the current analysis appears weak. Additionally, does this research have implications for large-scale analysis and what are its limitations?
Citation: https://doi.org/10.5194/egusphere-2024-657-RC1 -
RC2: 'Comment on egusphere-2024-657', Anonymous Referee #2, 07 May 2024
The authors present an analysis of sun-induced chlorophyll fluorescence (SIF) data, eddy-covariance based gross primary productivity (GPP), and reflectance-based vegetation indices measured from a temperate deciduous forest site. They also include various meteorological and other ancillary datasets from across the season of measurements, as well as measurements of chlorophyll fluorescence yield using an active blue-light LED-induced fluorescence (LIF) system. They present seasonal trends in these parameters clearly, and identify some drivers of variability in the relationship between temporally averaged SIF and GPP. They additionally investigate the variability in SIF and GPP individually using random forest models that include different combinations of their supporting datasets. The presentation of the field-collected data is mostly clear and addresses the authors’ first goal of understanding SIF and GPP dynamics.
However, the second half of the manuscript does not present a fully convincing case for the potential of the authors' random forest model approach to offer new insights based on forest structure and physiology, the authors’ second goal. Although the authors use their random forest models to identify some key variables driving SIF and GPP separately, their analysis does not present a clear path for using this information in a novel way. There are also some cases where the authors make assertions that are not clearly supported by their data, such as their suggestion of stronger satellite-measured SIF-GPP relationships during afternoon overpasses than morning overpasses. In sum, I recommend that the authors make major revisions to this manuscript, including removing some sections entirely and expanding others. Line-by-line recommendations follow below:
Lines 103-104: It could be argued that there is more to learn about the relationships among fluorescence and photosynthesis efficiencies and non-photosynthetic quenching (NPQ), but this certainly has previously been studied. These relationships are addressed in papers cited within this manuscript, including Wang et al. 2020, Martini et al. 2021, and others. I suggest this sentence be rephrased and some discussion of the current state of the literature on this topic be added here.
Line 106: Define "PRI" here at first use, and cite Gamon et al. 1992 (full citation below).
Gamon J.A., Peñuelas J., Field C.B. (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41: 35-44.
Lines 116-118: The abstract does not suggest that this manuscript is focused on finding vegetation index proxies for SIF. The rest of this paragraph is well reasoned, but I would recommend removing or reworking this sentence, as it presents a "problem" that the authors are not aiming to solve here.
Lines 151-155: It would be useful to include more detail on the configuration of the SIF3 system. For example, how were the sun irradiance and vegetation radiance measurements collected (e.g. the use of split or multiple fiber optic cables, the use of any other fore-optics to achieve the stated 25° field of view, the use of diffusive panels, etc.)? Additionally, how was the spectrometer calibrated? What was its spectral range? What was its signal-to-noise ratio? What was the distance between the canopy and sensor? These details would be useful to the reader when evaluating the SIF retrievals presented here.
Lines 162-163: Please expand on how FyieldLIF was interpreted and measured. It may be useful to draw comparisons (or contrasts) with the pulse-amplitude modulated (PAM) fluorescence literature here. For example, should FyieldLIF be interpreted similarly to fluorescence yield (ΦF) measured with a PAM fluorometer?
Line 172: What does LNIR mean here? Additionally, a “)” and period are missing after “LNIR.”
Line 250 / Figure 1: Solar azimuth angle (SAA) comes up frequently in the results and discussion sections of this manuscript. It would be worth adding a panel to this figure showing the daily and seasonal variations in SAA alongside these other variables.
Lines 270-272: Consider reformulating this sentence. It seems that the authors are pointing out the higher R2 values of nonlinear SIF-GPP relationships using data from days with a higher ratio of diffuse:direct illumination, but I am unsure how to interpret, “…The R2 between SIF and GPP increased on contrasting days of clear and overcast skies.”
Line 280 / Figure 2: This figure presents interesting data, but is difficult to read. I recommend making the plotted points partially transparent or using an alternative color ramp to improve the visibility of the overlaid symbols indicating sunny and cloudy observations. Decreasing the point opacity would also improve the reader’s ability to visualize the day of year trend the authors show in Panel 2a; as it is now, points from earlier in the year are covered by those from later in the year.
Line 290 / Figure 3: The interpretability of this figure would be improved by removing Panel 3b and changing the point symbols to indicate sunny vs cloudy days as in Figure 2. While this information can be partially inferred from Panel 3b as it is now, this would save the reader from cross-referencing across panels to interpret the presented data.
Line 295: I echo Reviewer 1’s confusion regarding the choice of these satellite overpass times. Is there a SIF-measuring satellite that overpasses at 10:30 AM? Even if so, this manuscript does not present any comparisons with satellite SIF retrievals, so focusing on these times of day does not lend much support to the overall analysis presented here.
Lines 309-311: Is SAA essentially serving as a proxy for time of day in this analysis? Were any models using time of day in place of SAA tested? If SAA and time of day are capturing separate effects, this may suggest sun-sensor geometry issues with the SIF instrumentation that should be examined and corrected.
Line 315 / Figure 5: The results presented up until this section focus mainly on the relationship between SIF and GPP. Here, the focus shifts to predicting magnitudes of SIF and GPP individually. It would be useful to expand, either here or in the Methods section, on the intention behind this shift in focus (e.g. to identify shared drivers of SIF and GPP, as is shown in Figures 6 and 7, or some other rationale). This would improve the focus of this analysis and help guide the reader through this section of the manuscript.
Line 332-333: What does the phrase “color code in green” refer to here? All the lines in Figure 6 are green, and I cannot see any that appear highlighted in Panel 6c where the PAR data are presented. I have the same question regarding the phrase “color code in blue” in reference to Figure 7 in line 334.
Line 335: “GP” should be “GPP.”
Lines 334-336: I do not understand what this sentence means, particularly the phrase “the magnitude of SIF and GPP is comparable for SWC and PAR variables.” Consider rephrasing this.
Line 349: This is the first mention of FyieldLIF since Figure 1. This variable does not seem to be a key part of the overall analysis presented here. While the technique is interesting, it might be worth excluding the LIF data to improve the focus of this manuscript.
Lines 352-353: I do not think that greater variability in PRI and NIRv can be used to assess their independence without further analysis. However, I agree that this suggests that NIRv, and particularly PRI, have different physiological drivers than NDVI or mNDI, and that these drivers vary over different timescales. Consider rephrasing this sentence accordingly.
Lines 378-379: This phrasing is perhaps an oversimplification of the high-frequency shifts in light partitioning happening at the leaf level. Rather than a simple binary, the SIF-GPP relationship resulting from these complex dynamics can fall into several regimes. I suggest that the authors cite Magney et al. 2020 here (see Figure 1d; full citation below), and adjust this phrasing discuss their results in more detail in this context.
Magney, T. S., Barnes, M. L., & Yang, X. (2020). On the covariation of chlorophyll fluorescence and photosynthesis across scales. Geophysical Research Letters, 47, e2020GL091098. https://doi.org/ 10.1029/2020GL091098
Lines 381-383: I am not convinced that the data presented here can be used to draw any generalized conclusions on how to interpret data from satellites with different overpass times. I recommend that this sentence be removed.
Line 397: This is the first mention of stomatal closure in this manuscript, despite the fact that soil water content has been discussed as a key driver of SIF-GPP dynamics throughout. I suggest that the sentences in Lines 394-400 be moved to the introduction section, and that a section on the relationship between air and soil water content and leaf-level gas exchange be added to this manuscript.
Lines 403-404: I would say that low soil water content (SWC) is a driver of plant water stress, rather than an indicator.
Citation: https://doi.org/10.5194/egusphere-2024-657-RC2
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-657', Anonymous Referee #1, 30 Apr 2024
This study used ground-based observations to examine the relationship between far-red Sun-Induced Chlorophyll Fluorescence (SIF) and Gross Primary Production (GPP) across different temporal scales. They aimed to tackle the complexities arising from canopy structures, sun-canopy geometry, and physiological factors affecting SIF and GPP. By integrating canopy-scale SIF measurements with environmental variables, they explored daily and seasonal variations and the impacts of extreme weather events like heatwaves on these relationships. Additionally, they utilized Random Forest models to predict SIF and GPP, providing insights into how these factors interact under different environmental conditions.
Overall, analyzing the SIF-GPP relationship has always been a hot topic, especially the changes in their relationship under different environmental conditions. It is a very interesting topic and has implications for large-scale photosynthesis monitoring. However, the current analysis of this study is far from their research goals. The analyses are relatively not solid enough and the new information that can be provided is limited. My opinion is to reject the manuscript but encourage resubmission. I hope the author will make serious revisions and improve the quality.
The five main comments correspond to the five figures in the results section
- Analyzing the seasonality of various variables, as shown in Figure 1, is fundamental. Two minor suggestions: 1. Can the description of variable relationships be more quantitative? 2. Regarding short-term heatwave events, first, please delineate the heatwave periods with circles in the figure, and second, I recommend that the author examines the changes in variables during heatwaves (possibly even hourly) in more detail, as currently, we rely almost exclusively on visual inspection to capture information.
- Could there be a more quantitative and detailed analysis of the relationship between GPP-SIF and environmental factors? Currently, the author has simply used the same graph with different density plots of environmental variables. For instance, could the influence of variables on the relationship be explored based on different bins? Additionally, it is difficult to distinguish between sunny and cloudy conditions.
- Fig. 3 is somewhat confusing. Is it meant to show the relationship between daily GPP and daily SIF? It appears more like an analysis of the relationship between half-hourly or hourly GPP and SIF over more than 100 days during the growing season. Additionally, could you use a boxplot to more clearly demonstrate the relationship between R2 and environmental variables? From figure d, it is not clear how soil moisture specifically affects the relationship.
- This analysis appears quite abruptly. First, why choose the two MODIS overpass times; second, how does the author conclude that the afternoon data is more non-linear, based solely on R2? In fact, using linear fitting, the R2 for the afternoon data might also be higher. Since the author tended to attribute this to physiological regulation, could this analysis include some specific environmental variables? Currently, it's unclear what conclusions this analysis specifically supports.
- I have significant reservations about the analysis in Figure 5. Firstly, what time scale is being analyzed? Secondly, when building the SIF prediction model, why use several similar vegetation indices simultaneously? It is hard to understand the conclusion that SWC is most important for SIF, as from the seasonal variations shown in Figure 1, SWC and SIF seem unrelated, whereas NIRv changes are similar. Similar doubts apply to the GPP model, such as the negligible contribution from NIRv, which is likely obscured by SIF and other vegetation indices. Is it possible to analyze the contribution of different variables under varying environmental conditions, such as during a heatwave?
Other comments
- The manuscript contains several typographical errors, such as capital letters following commas (Line 30), GPP misspelled as 'GP'.. Please conduct a thorough review of the entire document for such errors.
- I recommend conducting a separate analysis for heatwave events, such as how half-hourly or hourly variables change during these periods. Currently, the mention of this is too brief.
- In the Introduction, I suggest more directly highlighting the innovative aspects of this study and how it differs from previous research. Currently, some content, such as advancements in random forests, seems somewhat unrelated to the core focus of this study.
- The contributions of canopy structure and physiology on SIF/GPP or SIF-GPP relationship the authors emphasized earlier seem to be under-discussed in the formal analysis. For instance, under heatwave or non-heatwave conditions, how much of the SIF or GPP variability is explained by NDVI or other vegetation indices? How much is explained by environmental factors?
- In the manuscript, there are numerous mentions of "significantly," such as on Line 261. I wonder if the difference in the slope of the SIF-GPP relationship between cloudy and sunny conditions has undergone statistical significance testing?
- From the discussion section, it seems that much of the analysis is consistent with previous studies (e.g., Line 370-375). What is the novelty of this study? Given the abundance of studies on the SIF-GPP relationship, the current analysis appears weak. Additionally, does this research have implications for large-scale analysis and what are its limitations?
Citation: https://doi.org/10.5194/egusphere-2024-657-RC1 -
RC2: 'Comment on egusphere-2024-657', Anonymous Referee #2, 07 May 2024
The authors present an analysis of sun-induced chlorophyll fluorescence (SIF) data, eddy-covariance based gross primary productivity (GPP), and reflectance-based vegetation indices measured from a temperate deciduous forest site. They also include various meteorological and other ancillary datasets from across the season of measurements, as well as measurements of chlorophyll fluorescence yield using an active blue-light LED-induced fluorescence (LIF) system. They present seasonal trends in these parameters clearly, and identify some drivers of variability in the relationship between temporally averaged SIF and GPP. They additionally investigate the variability in SIF and GPP individually using random forest models that include different combinations of their supporting datasets. The presentation of the field-collected data is mostly clear and addresses the authors’ first goal of understanding SIF and GPP dynamics.
However, the second half of the manuscript does not present a fully convincing case for the potential of the authors' random forest model approach to offer new insights based on forest structure and physiology, the authors’ second goal. Although the authors use their random forest models to identify some key variables driving SIF and GPP separately, their analysis does not present a clear path for using this information in a novel way. There are also some cases where the authors make assertions that are not clearly supported by their data, such as their suggestion of stronger satellite-measured SIF-GPP relationships during afternoon overpasses than morning overpasses. In sum, I recommend that the authors make major revisions to this manuscript, including removing some sections entirely and expanding others. Line-by-line recommendations follow below:
Lines 103-104: It could be argued that there is more to learn about the relationships among fluorescence and photosynthesis efficiencies and non-photosynthetic quenching (NPQ), but this certainly has previously been studied. These relationships are addressed in papers cited within this manuscript, including Wang et al. 2020, Martini et al. 2021, and others. I suggest this sentence be rephrased and some discussion of the current state of the literature on this topic be added here.
Line 106: Define "PRI" here at first use, and cite Gamon et al. 1992 (full citation below).
Gamon J.A., Peñuelas J., Field C.B. (1992) A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sensing of Environment 41: 35-44.
Lines 116-118: The abstract does not suggest that this manuscript is focused on finding vegetation index proxies for SIF. The rest of this paragraph is well reasoned, but I would recommend removing or reworking this sentence, as it presents a "problem" that the authors are not aiming to solve here.
Lines 151-155: It would be useful to include more detail on the configuration of the SIF3 system. For example, how were the sun irradiance and vegetation radiance measurements collected (e.g. the use of split or multiple fiber optic cables, the use of any other fore-optics to achieve the stated 25° field of view, the use of diffusive panels, etc.)? Additionally, how was the spectrometer calibrated? What was its spectral range? What was its signal-to-noise ratio? What was the distance between the canopy and sensor? These details would be useful to the reader when evaluating the SIF retrievals presented here.
Lines 162-163: Please expand on how FyieldLIF was interpreted and measured. It may be useful to draw comparisons (or contrasts) with the pulse-amplitude modulated (PAM) fluorescence literature here. For example, should FyieldLIF be interpreted similarly to fluorescence yield (ΦF) measured with a PAM fluorometer?
Line 172: What does LNIR mean here? Additionally, a “)” and period are missing after “LNIR.”
Line 250 / Figure 1: Solar azimuth angle (SAA) comes up frequently in the results and discussion sections of this manuscript. It would be worth adding a panel to this figure showing the daily and seasonal variations in SAA alongside these other variables.
Lines 270-272: Consider reformulating this sentence. It seems that the authors are pointing out the higher R2 values of nonlinear SIF-GPP relationships using data from days with a higher ratio of diffuse:direct illumination, but I am unsure how to interpret, “…The R2 between SIF and GPP increased on contrasting days of clear and overcast skies.”
Line 280 / Figure 2: This figure presents interesting data, but is difficult to read. I recommend making the plotted points partially transparent or using an alternative color ramp to improve the visibility of the overlaid symbols indicating sunny and cloudy observations. Decreasing the point opacity would also improve the reader’s ability to visualize the day of year trend the authors show in Panel 2a; as it is now, points from earlier in the year are covered by those from later in the year.
Line 290 / Figure 3: The interpretability of this figure would be improved by removing Panel 3b and changing the point symbols to indicate sunny vs cloudy days as in Figure 2. While this information can be partially inferred from Panel 3b as it is now, this would save the reader from cross-referencing across panels to interpret the presented data.
Line 295: I echo Reviewer 1’s confusion regarding the choice of these satellite overpass times. Is there a SIF-measuring satellite that overpasses at 10:30 AM? Even if so, this manuscript does not present any comparisons with satellite SIF retrievals, so focusing on these times of day does not lend much support to the overall analysis presented here.
Lines 309-311: Is SAA essentially serving as a proxy for time of day in this analysis? Were any models using time of day in place of SAA tested? If SAA and time of day are capturing separate effects, this may suggest sun-sensor geometry issues with the SIF instrumentation that should be examined and corrected.
Line 315 / Figure 5: The results presented up until this section focus mainly on the relationship between SIF and GPP. Here, the focus shifts to predicting magnitudes of SIF and GPP individually. It would be useful to expand, either here or in the Methods section, on the intention behind this shift in focus (e.g. to identify shared drivers of SIF and GPP, as is shown in Figures 6 and 7, or some other rationale). This would improve the focus of this analysis and help guide the reader through this section of the manuscript.
Line 332-333: What does the phrase “color code in green” refer to here? All the lines in Figure 6 are green, and I cannot see any that appear highlighted in Panel 6c where the PAR data are presented. I have the same question regarding the phrase “color code in blue” in reference to Figure 7 in line 334.
Line 335: “GP” should be “GPP.”
Lines 334-336: I do not understand what this sentence means, particularly the phrase “the magnitude of SIF and GPP is comparable for SWC and PAR variables.” Consider rephrasing this.
Line 349: This is the first mention of FyieldLIF since Figure 1. This variable does not seem to be a key part of the overall analysis presented here. While the technique is interesting, it might be worth excluding the LIF data to improve the focus of this manuscript.
Lines 352-353: I do not think that greater variability in PRI and NIRv can be used to assess their independence without further analysis. However, I agree that this suggests that NIRv, and particularly PRI, have different physiological drivers than NDVI or mNDI, and that these drivers vary over different timescales. Consider rephrasing this sentence accordingly.
Lines 378-379: This phrasing is perhaps an oversimplification of the high-frequency shifts in light partitioning happening at the leaf level. Rather than a simple binary, the SIF-GPP relationship resulting from these complex dynamics can fall into several regimes. I suggest that the authors cite Magney et al. 2020 here (see Figure 1d; full citation below), and adjust this phrasing discuss their results in more detail in this context.
Magney, T. S., Barnes, M. L., & Yang, X. (2020). On the covariation of chlorophyll fluorescence and photosynthesis across scales. Geophysical Research Letters, 47, e2020GL091098. https://doi.org/ 10.1029/2020GL091098
Lines 381-383: I am not convinced that the data presented here can be used to draw any generalized conclusions on how to interpret data from satellites with different overpass times. I recommend that this sentence be removed.
Line 397: This is the first mention of stomatal closure in this manuscript, despite the fact that soil water content has been discussed as a key driver of SIF-GPP dynamics throughout. I suggest that the sentences in Lines 394-400 be moved to the introduction section, and that a section on the relationship between air and soil water content and leaf-level gas exchange be added to this manuscript.
Lines 403-404: I would say that low soil water content (SWC) is a driver of plant water stress, rather than an indicator.
Citation: https://doi.org/10.5194/egusphere-2024-657-RC2
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