Predictions of Aboveground Herbaceous Production from Satellite-Derived APAR Are More Sensitive to Ecosite than Grazing Management Strategy in Shortgrass Steppe

Round 1

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

This study assessed the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR and found that APAR could be used to better predict ANHP in the shortgrass steppe compared to the Rangeland Analysis Platform model. This is an interesting and meaningful work, but there are some issues that need further improvement:

1. The title needs further revision. The study only used the relationship between ground-observed ANHP and satellite-derived APAR. This does not lead to the conclusion that satellite-based prediction of aboveground net herbaceous production are not sensitive to grazing management, as there are many models for prediction.

2. The abstract needs to be further condensed and logically organized.

（1）line68-72, there are a large number of coarse resolution GPP or APAR was used to evaluate the progress of ANHP, however, there are already more detailed APAR. So these introduction are unnecessary；

（2）line73-75, why is ANHP being emphasized again, and why is it not placed at the beginning of the introduction;

（3）line155-157，what is this sentence trying to demonstrate? I can't see its role in the introduction;

（4）line133-142, these specific introductions are more suitable for methods rather than introductions;

（5）line142，what is “prior work”?

（6）In the introduction, it is necessary to further summarize the objectives of this study;

3.line 372-381, the meanings of these parameters need to be supplemented and explained；

4. What does the random effect in Figure 5 specifically refer to；

5. The significance test of the differences needs to be supplemented in the results of Figure 6；

 

4. It is necessary to carefully check the entire text and correct some details, eg.line268-269, what is N?

Comments on the Quality of English Language

It is necessary to carefully check the entire text and correct some details

Author Response

This study assessed the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR and found that APAR could be used to better predict ANHP in the shortgrass steppe compared to the Rangeland Analysis Platform model. This is an interesting and meaningful work, but there are some issues that need further improvement:

1. The title needs further revision. The study only used the relationship between ground-observed ANHP and satellite-derived APAR. This does not lead to the conclusion that satellite-based prediction of aboveground net herbaceous production are not sensitive to grazing management, as there are many models for prediction.

We have changed the title to read: Satellite-based predictions of aboveground net herbaceous production are more sensitive to ecosite than grazing management strategy in shortgrass steppe.

 

2. The abstract needs to be further condensed and logically organized.

Previous reviewers requested more information to be added, we believe the current length is condensed while still providing enough information. Could the reviewer suggest a better way to organize the abstract? We start by setting up why this is important and then follow with a brief overview of our analysis, and then end with results and importance.

（1）line68-72, there are a large number of coarse resolution GPP or APAR was used to evaluate the progress of ANHP, however, there are already more detailed APAR. So these introduction are unnecessary；

We think the first part of the paragraph is important, and introduces the topic of deriving GPP and ANHP from satellites has been around for a long time.

（2）line73-75, why is ANHP being emphasized again, and why is it not placed at the beginning of the introduction;

ANHP is introduced initially at the beginning of the introduction but is further clarified in this section. Earlier, we introduced ANHP as a concept, and here we further clarify why it is important and how it can be used.

（3）line155-157，what is this sentence trying to demonstrate? I can't see its role in the introduction;

This sentence highlights that previous research from our group has explored a different angle to a similar question.

（4）line133-142, these specific introductions are more suitable for methods rather than introductions;

A big part of this study is looking into ecological sites, we believe briefly introducing the ecological sites we are interested in helps the reader get a sense of the type of environment we are working in. We have more detailed information in the methods section.

（5）line142，what is “prior work”?

We added a citation Gaffney et al 2018 to this sentence to further clarify which work we are referring to.

（6）In the introduction, it is necessary to further summarize the objectives of this study;

Lines 151 – 167, which are included in the introduction, summarize the two objectives of this study. It would be very helpful if the reviewer could clarify what needs further summarization.

3.line 372-381, the meanings of these parameters need to be supplemented and explained；

Are you referring to iAPAR:Sandy Plains notation? We state before or in every sentence that this is an interaction coefficient, we added (“:” denotes interaction) to line 378 to further clarify

4. What does the random effect in Figure 5 specifically refer to；

We explain what this means in the figure caption: The analysis involved assessing the slopes of the linear relationships, after allowing random intercept’s to vary by year and plot ID (top panels).

5. The significance test of the differences needs to be supplemented in the results of Figure 6；

 We have included significance in Table 1 and again in Figure 5. The goal of Figure 6 was to move away from the arbitrary p-value and instead visualize the coefficients with their 95% confidence intervals.

4. It is necessary to carefully check the entire text and correct some details, eg.line268-269, what is N?

We don't know what the reviewer is referring to. On line 268, we introduce the simple ratio index equation, and perhaps the N they are referring to is the N in NDVI and there was an issue with formatting when viewed?

Reviewer 2 Report (Previous Reviewer 4)

Comments and Suggestions for Authors

This paper solves my question and is recommended for publication

Comments on the Quality of English Language

Minor editing of English language required

Author Response

Thank you for reviewing our paper. 

Round 2

Reviewer 1 Report (Previous Reviewer 2)

Comments and Suggestions for Authors

1. I think your title should be revised again, not just adding a "more".

As stated in line13-14, to derive the empirical relationship between ANHP and APAR is a simple and effective way to estimate ANHP from satellites, which does not represent “satellite-based predictions” in the title. So your title is not accurate enough.

2. In the previous comments, it should be the Introduction that needs to be further condensed and logically organized, not the Abstract.

You can start by stating the importance, then the advances in methods and data, followed by current gaps and challenges, and finally the objectives of this study. You can reorganize the introduction in a more logical way according to this suggestion, for example, it is better not to include the previous research progress in the objectives paragraph.

3. I noticed that authors modified some details, but you still need to carefully check the whole text to make it more standardized. For example, line 317 is not appropriate as a separate title; line 365, “explained variance = 1 –- residual sum of squares /by 365 the total sum of squares, coefficient of determination”.

4. The following issues must be corrected.

"Lines 439-441. When data were broken down by ecosite, both RAP and iAPAR had better MAE%, MPE%, MAPE%, and r (correlation) values in the taller structured Salt Flats and Sandy Plains ecosites compared to the Loamy Plains ecosite (Table 2).

This is not justified by the data in Table 2. Loamy Plains have lower r, but also lower MAE and mixed results in MAE%. Either the above text or the data of Table 2 have to be corrected.

In Tables 2, S1 and S2 not all R2 values correspond to the squared values of r. In Table S2 there is even a negative R2 value!!!

In the legends of tables 2, S1 and S2 MPE and MAPE should be changed to MPE% and MAPE%.

 

In the legend of Table 2 (lines 456-457) "explained variance" should be changed to "Pearson correlation coefficient".

Comments on the Quality of English Language

Minor editing of English language required

Author Response

1. I think your title should be revised again, not just adding a "more".

 

As stated in line13-14, to derive the empirical relationship between ANHP and APAR is a simple and effective way to estimate ANHP from satellites, which does not represent “satellite-based predictions” in the title. So your title is not accurate enough.

Thank you for clarifying; we have changed the title to include APAR

Predictions of aboveground herbaceous production from satellite-derived APAR are more sensitive to ecosite than grazing management strategy in shortgrass steppe

 

2. In the previous comments, it should be the Introduction that needs to be further condensed and logically organized, not the Abstract.

 

You can start by stating the importance, then the advances in methods and data, followed by current gaps and challenges, and finally the objectives of this study. You can reorganize the introduction in a more logical way according to this suggestion, for example, it is better not to include the previous research progress in the objectives paragraph.

 

Thank you for your suggestion to reorganize the introduction. We believe the current structure follows a similar organization to the one suggested. I will point out certain paragraphs.

 

This paragraph is important to set the groundwork for why this work may be important.

Rangelands include vast expanses of grassland, savanna, shrubland, desert, and tundra on which the indigenous vegetation is predominantly grasses, forbs, or shrubs and which are often managed in terms of the abundance and distribution of native and domestic herbivores. Rangelands cover about 50% of the earth’s land surface [1], pro-vide 70% of the forage for ruminant livestock [2], and are often located in remote, rural areas. Aboveground net herbaceous production (ANHP) estimates are critical to many rangeland management and monitoring efforts. They are used to estimate potential carrying capacity for herbivores [3], understand the effects of management decisions and natural drivers on ecosystem structure and function [4,5], predict fire risk and be-havior [6,7] inform grazing management decisions [8], and calibrate and validate eco-logical models (e.g., carbon cycling, wildlife habitat; [9]). Given the vast extent and remote nature of most rangelands, spatially explicit maps of ANHP are desirable to provide information for locations where intensive ground data collection is cost-prohibitive or impractical.

This paragraph outlines the different metrics we are using in this paper and how they have previously been used, which is important for readers who are not familiar with these particular metrics.

Satellite imagery has proven valuable for estimating production parameters such as gross primary production (GPP), net primary production (NPP), aboveground net primary production (ANPP) and ANHP (which is the herbaceous-only fraction of ANPP). Satellite-based estimates of plant production tend to rely on radiation use effi-ciency logic [3,10]. In short, this logic assumes that plant production is a function of the amount of incoming photosynthetically active radiation (PAR) absorbed by the plant canopy, and the proportion of that radiation that is converted into plant bio-mass. One of the simplest approaches to estimating plant-absorbed photosynthetically active radiation (APAR) from satellites is to use the normalized difference vegetation index (NDVI), which can be calculated from the red and near-infrared reflectance bands obtained by many land surface monitoring satellites. NDVI has been shown to be related to leaf area index (LAI) [11] and the fraction of photosynthetically active radiation (fPAR) absorbed by plants [11]. By measuring or estimating incoming PAR, fPAR can be translated into APAR, which, in turn, can be used to estimate plant pro-duction by applying processed-based or empirical models [3,12].

 

This paragraph outlines the previous research in this field. We include this to make sure readers know we are building on previous research and not reinventing a new process.

Spatially coarse estimates of GPP from satellites have been available for decades [13]. The most widely used comes from the Moderate Resolution Imaging Spectrora-diometer (MODIS) and uses the processed-based MOD17 algorithm [14,15], originally designed for global coverage at 0.5 – 1 km spatial resolution. Empirical models have also been tested using MODIS-derived APAR (250 m) with strong relationships at the paddock scale between cumulative APAR and ANHP estimated from ground-based clipping [3]. ANHP estimates are particularly important to rangeland managers since herbaceous production is directly related to available forage for livestock, and pro-vides habitat for a number of ground-nesting grassland bird species. In central North American grasslands, estimates of ANHP exclude primary production by species such as cacti (Opuntia polyacantha) and low-statured woody plants (e.g. Guterizzia sarothrae), which comprise only a small fraction of ANPP and are not grazed by livestock. ANHP estimates at fine spatial scales are needed to monitor and manage heterogenous rangeland landscapes. Recent improvements in computational capacity and satellite coverage, combined with clever downscaling and data fusion techniques, have al-lowed for advancements in fine-scale estimates of ANHP using both processed-based and empirical approaches.

This paragraph outlines the advances in our specific research area with new models.

For example, the Rangeland Analysis Platform (RAP) builds on the MOD17 algo-rithm. RAP estimates APAR from Landsat NDVI (30 m) for rangelands across the United States every 16 days, then uses a process-based model to convert APAR to GPP, partition GPP by vegetation type, and ultimately estimate the portion of GPP allocated to aboveground herbaceous biomass [16]. This process is used to provide, among other things, an estimate of ANHP every 16 days at 30 m resolution [17]. In another ap-proach, Gaffney et al. (2018) used Landsat-MODIS fusion data (30 m) to estimate daily APAR from NDVI, and then fit an empirical model to predict ground-clipped ANHP using integrated (cumulative) growing season APAR (iAPAR) in the shortgrass prai-ries of Colorado, USA. Theoretically this approach could estimate daily production at 30 m resolution, although it was only validated at peak production.

This outlines our previous research which the foundation for our current research. Previous reviewers were confused on this particular aspect so we have included it.

When sufficient ground data are available, empirical approaches such as that used by Gaffney et al. (2018) are appealing for developing more locally specific esti-mates of ANHP, for example at the ranch scale. By contrast, process-based models such as RAP require a number of assumptions about biophysical parameters (e.g., light use efficiency, respiration rates) and meteorological forcing (e.g., responses to climatic stressors). These parameters can vary by plant community or even individual species, making it is challenging to produce them at locally relevant scales, both because the parameters are not always known and because information on dominant plant types is not always available in a spatially explicit form. Empirical models can be a simple way to directly use the observed relationship between iAPAR and ANHP, allowing for sep-arate models or coefficients for different dominant plant types [3,12].

 

This paragraph outlines the gaps in the current research.

In either case, little is known about the effects of grazing by large herbivores on the relationship between satellite-derived APAR and ANHP. Herbivory can reduce, enhance, or fail to affect ANPP and ANHP, depending on plant community character-istics and evolutionary history of herbivory [18–20]. For process-based models, it is likely that herbivory changes some of the underlying biophysical assumptions used, due to its effects on plant regrowth. However, it is also possible that grazing may change the assumed relationships between NDVI and LAI or NDVI and fPAR, which would affect both process-based and empirical models. For example, if grazing re-moves the upper canopy layers of vegetation and exposes lower layers with different reflectance properties, it may alter satellite-observed NDVI regardless of actual changes in production on the ground. Furthermore, while GPP estimates can be vali-dated on the ground using flux towers which can capture the effects of grazing, it is more challenging to measure ANPP and ANHP in a grazed setting [21]. In grazed grasslands, ANHP is typically estimated from vegetation clipped in cages that exclude grazing during the current growing season. Therefore, it is possible that production at the scale seen by satellites (i.e., grazed vegetation at the 30m pixel scale) is different from the production estimates used to calibrate and validate models (i.e., ungrazed biomass in a < 1 m2 cage), either as a result of under compensation (reduced produc-tion) or over-compensation (increased production) in response to grazing [20].

Here we outline our study system and the importance of understanding ecological sites and plant communities when estimating ANHP using satellites, and the potential importance of removal of vegetation through grazing.

Considering the potential disparities when calibrating and developing models, we aim to expand on previous research conducted by Gaffney et al. (2018) at the Central Plains Experimental Range (CPER) on the shortgrass step of northeast Colorado, USA. Here, our main objective was to determine whether grazing management affects the relationship between ANHP and satellite-derived iAPAR, while accounting for differ-ences in plant communities across ecological sites. For rangelands of the western United States, ANHP is also strongly driven by soil properties and processes in combi-nation with climate, which have been used to delineate “ecological sites”, or groups of mapped soil types that support similar plant communities and respond similarly to management and disturbance [22]. At both CPER and in the broader surrounding re-gion of the  shortgrass steppe, the two most widespread ecological sites are Loamy Plains [23], which is typically dominated by C4 shortgrasses (Bouteloua gracilis, B. dac-tyloides) with sub-dominant C3 mid-height perennial graminoids (Pascopyrum smithii, Hesperostipa comata, Carex duriscula), and Sandy Plains [24], which is typically co-dominated by C4 shortgrasses and C3 mid-height graminoids. Less widespread soil types characterized by increased alkalinity support the Salt Flats ecological site, which is dominated by C4 mid-height grasses (Sporobolus airoides, Distichlis spicata) in combi-nation with mid-height C3 graminoids [25]. Both Sandy Plains and Salt Flats support significantly greater ANHP and more vertically oriented vegetation than the Loamy Plains ecological site [8]. As a result, our prior work developing a model relating tem-poral variation in iAPAR to ANHP [12] found a significantly greater slope for mid-height structured communities (Sandy Plains and Salt Flats) compared to the more prostrate plant community on Loamy Plains.  However, our previous models were calibrated only using sites grazed season-long (May – Oct) at a moderate stocking rate. Where rangeland managers employ adaptive, multipaddock (AMP) rotational grazing over the growing season, the sward is expected to experience a much different temporal pattern of defoliation, which is expected to change the shape and the season-al NDVI and APAR curves. 

We believe including a statement about our previous research in the objectives paragraphs allows for full transparency of what we are building on and allows others who are curious to further explore our previous work in this area. We have seen other research articles where previous research is very sparsely referenced in the article, and we believe that knowledge is continuously built on and is especially important to reference in the rapidly changing field of GIS.

We evaluated two main hypotheses associated with our main objective. First, we expected that the slope of iAPAR as a predictor of ANHP would vary by ecological site. Second, we expected that grazing management would not affect the relationship be-tween iAPAR and ANHP in this study area. Grazing seems unlikely to change the un-derlying relationship between NDVI and fPAR for the relatively short-structured her-baceous vegetation dominant in this region. Furthermore, previous ground-based re-search in semi-arid shortgrass systems has shown that, within a single growing season, the potential for substantial over- or under-compensation is limited by the semi-arid climate [26].

Our study also pursued a secondary objective, in which we compared satel-lite-predicted biomass production from the process-based RAP model to our empirical iAPAR-based model, across different grazing management regimes and ecosites at CPER. We expected that our empirical model would better account for variation across ecosites since it is calibrated to the location and explicitly accounts for the effects of ecosite on the ANHP-iAPAR relationship. We also evaluated how the RAP model per-formed across ecosites and grazing management regimes, since the parameters of RAP’s underlying process-based GPP model were calibrated at ungrazed grassland sites with taller structured vegetation.

3. I noticed that authors modified some details, but you still need to carefully check the whole text to make it more standardized. For example, line 317 is not appropriate as a separate title; line 365, “explained variance = 1 –- residual sum of squares /by 365 the total sum of squares, coefficient of determination”.

We believe that 2.3.3 Estimating ANHP from iAPAR should be considered a subsection of 2.3 Remotely sensed measurements.

We have corrected the additional “-“

 

4. The following issues must be corrected.

 

"Lines 439-441. When data were broken down by ecosite, both RAP and iAPAR had better MAE%, MPE%, MAPE%, and r (correlation) values in the taller structured Salt Flats and Sandy Plains ecosites compared to the Loamy Plains ecosite (Table 2).

 

This is not justified by the data in Table 2. Loamy Plains have lower r, but also lower MAE and mixed results in MAE%. Either the above text or the data of Table 2 have to be corrected.

The text agreed with Table 2 in that the values were ‘better’ as stated (lower MAE% and MAPE%, MPE% closer to 0 and higher r), however we agree that this is confusing, and thus changed the wording to be more specific. This line now reads: When data were broken down by ecosite, both RAP and iAPAR had lower MAE%, and MAPE%, less biased MPE% (i.e., closer to 0) and higher r (correlation) values in the taller structured Salt Flats and Sandy Plains ecosites compared to the Loamy Plains ecosite (Table 2).

 

In Tables 2, S1 and S2 not all R2 values correspond to the squared values of r. In Table S2 there is even a negative R2 value!!!

This is because they are derived from different formulas. We did not use the square of the Pearson’s correlation coefficient (r) to derive R². We specify how R² was calculated on lines 365 and 366. The R² formula we use accounts for more than just the linear relationship between predicted and observed values and it does allow for negative R2 values (e.g., when the mean of the data provides a better fit to the outcomes than the fitted function values.

Here is a Wikipedia article that describes the differences between r and R2 and provides some information on the negative R2 values: https://en.wikipedia.org/wiki/Coefficient_of_determination.

In the legends of tables 2, S1 and S2 MPE and MAPE should be changed to MPE% and MAPE%.

Thank you, this has been changed.

In the legend of Table 2 (lines 456-457) "explained variance" should be changed to "Pearson correlation coefficient".

Thank you, this has been changed.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Very interesting paper and well written

Reviewer 2 Report

Comments and Suggestions for Authors

The logic and writing conventions of the entire paper have significant issues, making it difficult for me to understand what the author is trying to convey. I suggest that it be revised carefully. For instance, is line 293 a formula? Why does the article have two sections numbered as "4", "4. Figures and Tables" and "4. Discussion"? There are also many expressions that are very unconventional. Could you please confirm if this is indeed the final version?

Comments on the Quality of English Language

I suggest thoroughly revising the logic of the paper's language.

Author Response

please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1.      In the “Abstract”, there is only a textual description, lacking specific data to support the relevant results.

2.      In the “Materials and Methods”, a map of the study area is missing, as well as a map of the distribution of each pasture. Similarly, the distribution of sampling points needs to be illustrated in a figure.

3.      In the “Results”, there is very little relevant content and the textual descriptions are separated from the figures and tables, which needs to be strengthened for an in-depth analysis of the study in order to fulfill the requirements of a scientific paper.

4.      What is the difference between ANHP and ANPP? Why use ANHP to represent aboveground productivity?

5. “Figures and Tables” is not suitable as a separate chapter. It should be combined with the results so that it is easy for the reader to read.

Author Response

please see the attachment

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

I did not find any innovative points in this paper, and I hope the author will carefully summarize them

I think the quantitative description of the author's abstract position is insufficient.

3. There is no research area map, which is inconvenient for readers to read

4. The flowchart description is not detailed enough

5. No conclusion found

6. Suggest adding more quantitative descriptions to the discussion location.

7. Image resolution and article format can be further optimized

Comments on the Quality of English Language

I did not find any innovative points in this paper, and I hope the author will carefully summarize them

I think the quantitative description of the author's abstract position is insufficient.

3. There is no research area map, which is inconvenient for readers to read

4. The flowchart description is not detailed enough

5. No conclusion found

6. Suggest adding more quantitative descriptions to the discussion location.

7. Image resolution and article format can be further optimized

Author Response

please see the attachment

Author Response File: Author Response.pdf