the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jan 2024
Predicting the productivity of Alpine grasslands using remote sensing information
Abstract. Gross primary productivity (GPP) is a crucial variable for ecosystem dynamics, and it can significantly vary on the small spatial scales of vegetation and environmental heterogeneity. This is especially true for mountain ecosystems, which pose severe difficulties to field monitoring. In addition, the specificity of such ecosystems and the extreme abiotic conditions that they experience often make global and regional models unsuited to predictions. In this case, remote sensing products offer the opportunity to explore the productivity of vegetation communities in remote areas such as Alpine grasslands all year round, and empirical models can help in the challenge of modelling Alpine GPP. Along these lines, we took a hybrid approach, blending several remote sensing data sources (such as a high-definition digital terrain model and moderate- and high- resolution satellite products such as MODIS and Sentinel 2) and gridded datasets such as ERA5 with in situ measurements to implement a specific empirical model. The resulting remote-sensing-based model developed here was suited to represent the measured primary productivity in different areas within a high-altitude grassland at the Nivolet plain, in the north-western Italian Alps at 2700–2500 m amsl. A cross-validation approach allowed us to evaluate to what extent a single empirical model could represent diverse communities and different abiotic factors found in these areas. We finally identified the ratio between MCARI2 and MSAVI2 as a good predictor of light use efficiency, a key factor in the empirical model, probably due to its good correlation with the leaves phenological status, inasmuch it estimates the ratio between chlorophyll and the ensemble of leaf pigments.
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RC1: 'Comment on egusphere-2023-2824', Anonymous Referee #1, 04 Mar 2024
General comments
The paper titled “Predicting the productivity of Alpine grasslands using remote sensing information” evaluates different approaches to estimate gross primary production (GPP) of Alpine grasslands using remote sensing data and a Light Use Efficiency (LUE) model. Several remote sensing and climate reanalysis datasets are used to empirically estimate the LUE model input variables and parameters, and in situ GPP data from 5 different plots are used as a reference ground dataset to assess model performances.
The topic is interesting and scientifically relevant, given the ecological complexity of Alpine grasslands and their vulnerability to global environmental changes. However, in its current version, the manuscript has major pitfalls. Both the abstract and the introduction fail in describing clearly the rationale behind the study, the novelty/relevance of the proposed approach with respect to published literature, and the general/specific objectives of the study. Several sentences are generic and often not supported by appropriate citations. The methodology is largely empirical, which can be okay, but would require a more detailed explanation of the reasons behind choosing a specific proxy rather than another to estimate the LUE input variables and parameters. In addition, the field dataset is not described in details and it is difficult to understand how it could be considered representative of highly heterogeneous Alpine grasslands. Finally, discussions are very limited and do not explain clearly the relevance of the results and the limitations of the study (e.g., how the challenge of the different spatial scale between field data and satellite data is addressed).
Specific comments:
l.4: what kind of global and regional models and prediction are the Authors referring to?
l.9: what do the Authors mean by ‘specific empirical model’?
l.11: 2500-2700
l.20: what do the Authors mean by “many types of studies”. I would suggest to be more specific
L.23: average values of productivity?
l.28: in this study empirical models are used to estimate the input variables and parameters of the LUE model (fAPAR, PAR, ε). As such, it seems incorrect to state that different regressive models are used to estimate GPP.
l.29: Rossini et al. 2012 cannot be used as the main citation for the Monteith model. Please use the original reference from Monteith.
l.34: please remove ‘complicated’ as it seems subjective. In addition, if ‘both sources are complicated’, what is the latter source that can be simply calculated? The sentence is unclear.
l.36: what do the Authors mean by “the system”? Can you be more specific?
l.36-39: the sentence is unclear
l.47: another approach. What is the first approach? The sentence at lines 39-46 describes LUE, not a retrieval approach.
l.49: Can the Authors provide references about the method they describe? My understanding is that potential LUE can be estimated for each PFT and that specific functions can be used to consider the LUE reduction caused by non-optimal conditions.
l.52: please add references about the use of PRI for LUE estimation
l.54: spectral feature?
l.61-63: sentence is unclear. I would not describe SIF in this way.
l.65: RS drivers?
l.65-69: the objectives and motivations of the study are unclear and very generic. In addition, what do the Authors mean by stating that they try to balance spectral, temporal and spatial resolution?
l.81: why the Authors report the maximum snow depth for an extreme? Wouldn’t be more relevant to indicate the average snow depth and duration of snow cover?
l.97-102: The description of the plots and Figure 2 do not help understanding the ecological representativeness of the study plots. Among others, more information about grassland composition in each plot would be important. Figure 2 is not very informative. It would be valuable to add a general overview of the location of the study area within Aosta Valley and to improve the terrain representation to better capture contours, slope, aspect, shade reliefs, etc.
l.107: The basic formula refers to the Monteith LUE model, not to a remote sensing approach.
l.116: why level 1? What type of level 1? So atmospheric correction was not performed?
l.117: what is the rationale behind choosing these indices?
l.118: can the Author provide a short explanation of this application?
Figure 1. The figure is very unclear and not self-explanatory. Acronyms should be listed in the caption. Symbols should be also explained. BOA generally refers to Bottom of Atmosphere.
l.142. Can the Authors explain what is the rationale behind using a spatial resolution of 6.6m?
Table 1. The resolution of the different products is pretty different and, overall, it is coars,e especially for mountain areas. How this can be comparable with a plot of 10 meters, considering the high heterogeneity and topographic complexity of the study region?
l.193: the approach based on in situ data was not mentioned explicitly earlier. Perhaps it would help to add in the “methods” a short explanation of the different approaches that are compared.
Figure 4. It should be ‘positively’ and ‘negatively’
l.215: what the Authors mean by ‘extra correlations’?
l.239: also the model base on in situ data does not reach 80-90% if I understand correctly
l.236-251: the discussion of the results is very limited. There is no comparative analysis with similar studies nor any discussion of the limitations of the methodology proposed (e.g. issues of resolution, issues of subjective selection of variables, issues of limited representativeness of the dataset, etc.).
Citation: https://doi.org/10.5194/egusphere-2023-2824-RC1 -
RC2: 'Comment on egusphere-2023-2824', Anonymous Referee #2, 23 Mar 2024
Review «Predicting the productivity of Alpine grasslands using remote
sensing information»
Gerneral commets
Dear authors,
Thanks a lot for presenting this interesting study to the scientific community. You address a very important topic and the in-situ observations that – I guess – took a lot of effort to collect are very valuable to address the topic of your manuscript.
Still, based on several shortcomings of the presentation of your manuscript, I think that the manuscript is not yet ready for publication.
- While the introduction nicely describes the challenges related to estimating GPP from EO data, it lacks insights into the body of literature on monitoring grasslands with remote sensing and in general, studies on estimation of GPP in the alpine.
- Also, the aims of the study are not well defined.
- The results part is confusing. This might result from the fact that also in the methods it is not very comprehensible which data were used to create which results. As a starting point, maybe an overview figure would help here.
- The discussion and conclusion part are very short and miss some important aspects.
Thus, I suggest major revisions of the manuscript to allow the readership to fully comprehend the exciting research the authors undertook.
Please find my specific comments below.
Specific comments
Abstract
- Please add information on how you measured GPP in your study.
Introduction
- Line 34 “while this latter can” typo? I don’t understand what this sentence tries to convey.
- Moreover, parts of the introduction already sound like the methods part, e.g. line 60 – 69.
- Also, a specification of the aims or research questions of the study is missing.
Materials and Methods
- I suggest splitting the description of the study site and “in-situ” data
- I suggest splitting the description of “Remote sensing GPP estimates” into multiple parts.
- Line 114: “The introduction lacks insights into the body of literature on monitoring grasslands with remote sensing studies on estimation of GPP in the alpine”. Please cite this literature.
- Line 115: “We disregarded this approach given that light is unlike to be the limiting factor in wild plants”. Please provide evidence or a citation for this statement
- Line 116: Why did you use the Sentinel2 level 1 product instead of the l2?
- You switch between active and passive expressions. Please homogenize.
- Line 122: “Then, using a cloud filtering pipeline, pixels identified as cloud were marked as "data not available", while snow pixels were marked as "zero", given that fAPAR is null under the snow cover.” Please provide more detail on that since it is a sensitive part of the process.
- Line 123: “Nivolet time-series features” Including the name Nivolet in the description of the time series is a bit confusing since one might think of it as a special method (if the name of the study site is forgotten. Thus, I suggest removing the name here.
- Line 129: “Anci are the predictors of LUE” please mention that these are spelled out below.
- Figure 1: Text is blurry.
- Table 1: The first ¦ seems to be between two lines. Which line is referred to?
- Nice approaches for the cross-validation. Still, could you state how many observations were used for each train/test split with each cross-validation approach, please.
- And more generally: Why did you choose to go for a linear fixed model for your estimation of GPP? Have you tried a more flexible model such as random forest or are the number of observations to limited for such approaches? Please provide your reasoning.
Results
- Figure 2 and 3: For me these figures show mainly the input data and not results from your research. I thus suggest moving them to a supplementary material section.
- What I would rather like to see are some scatterplots for your results instead of only correlation coefficients, i.e. the data behind table 4.
- Along the same lines: I would like to know the spread of results in the cross-validation in addition to the single R2 values (e.g. in table 4, but also generally).
- Also, I would like to see some more information on the extracted vegetation indices. You state that “In total, 394 images from the beginning of 2017 … were stacked”. I am missing some information on how this time series data is used. In the presentation of the results, it seems that individual, non continues data was used in the models. Is this correct?
- Table 4: In the line of R2: are these the values without cross-validation?
- Figure 4 and the methodology behind it. In a study by Merz et al. (2022) it was shown that some parameters such as Temperature and VPD had a lag time compared to GPP estimates of a flux tower when looking at the data in high temporal detail. Did you investigate such effects?
- Figure 4: I get a bit confused from this figure. In the text you mention somewhere that you also measure meteo variables along with the GPP in your in-situ dataset. Then you say that these are only complete for 73 observations (line 191). Then I read the statement somewhere that ERA5 and in-situ measurements do not correlate to well (line 195 ff). But then in figure 4 you have meteo variables on the remote and in situ site – but as far as I understand these are the once from ERA5, right? For me, this is a confusing way of presenting things. I suggest at least improving the caption of figure 4 to clearly state that all meteo variables are from ERA5 (if this is true).
- The confusion continues in table 4: Here I wonder if now the meteo variables used in the models are still the ERA5 data or in part the in-situ meteo data is used. This confusion is fuelled by your statement somewhere in the text that you have build models based solely on the in-situ data (at least if I got this right?).
- Table 4: Please highlight which of the models use in-situ data and which not. It can be comprehended by switching back and forth between text and table, but it would that a considerable overhead for the reader given that this information is essential to interpret the results.
- Is Figure 5 referred to in the text? I could not find it. Also, is that plot essential for the message the manuscript wants to convey? If yes, please elaborate. If not, move the figure to the appendix. Moreover, is air temperature measured?
- As a general comment to the captions of the figures and tables: In my opinion it makes sense to spell out abbreviations in the captions of the figure to easy their interpretation by the reader. Less thinking on such things gives more capacity to focus on the science. But I leave it to the authors to decide (and look up the guidelines of the journal if there are any on this).
- Lines 220 -234: I find that part hard to follow. Maybe a figure would help to explain the combinations and which data was used where to generate which results.
- Overall, I find the presentation of the results confusion and advice to rewrite / eventually restructure it in a more comprehensible way.
- Figure 6: Some text in the caption is blue … 😉
Discussion and conclusion
- This is neither a sufficient discussion nor a conclusion.
- Please put your results in the context of (more) other published works.
- Reflect on your methodology, what was good, where are shortcomings.
- How and to which extend are the research questions (that where not explicitly defined) answered?
- Write up a proper conclusion on the main findings of your research.
Appendix:
Caption of figure a6 is confusing. Also the figures lack a label on the y axis.
Citation: https://doi.org/10.5194/egusphere-2023-2824-RC2
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