An Improved Approach to Estimate Stocking Rate and Carrying Capacity Based on Remotely Sensed Phenology Timings

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Stocking Rate and Carrying Capacity estimation are difficult due to seasonal rotational grazing regimes. The authors combined the remotely sensed phenology timings of grassland, considered relations between biomass growth and consumption dynamics for accurate LCC and ASR estimation, results were validated in both coarse and fine scale in QTP. The authors are familiar with correlational research, and the paper was well organized and written. The methodology was well introduced and the results were deep analyzed. Overall it is a good paper and only few minor comments are given below:

1.      Line 68, and extra space was added.

2.      For all formula, the formula should be centered and do not beyond words.

Author Response

Dear reviewer, 

Thanks for your supportive comments and careful inspection.

All the issues have been carefully addressed and revised.

(1) The extra space in Line 68 was removed, and (2) all the formulas have been centered.

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript built a concept model to assess the livestock carrying capacity (LCC) and actual stocking rate (ASR) in grasslands. From the view of the theory model, it is a nice paper. The model concept is clear. The writing is fluent. However, I would say it is not a remote sensing paper. The required key model parameters were from the meta-analysis of published papers. The assessment is based on the entire steppe rather than the satellite grid. Although the authors said “… base on remotely sensed phenology timings” in the title, they used the modeled peak phenological timing (lines 180-185). Anyway, this is not a big issue. Satellite-based land surface phenology is spatially heterogeneous. Using a single value to represent an entire steppe is no better than a modeled value. The major issue is that I have no clue how this concept model could be applied to satellite-based grid datasets, or if it is out of my knowledge.

Author Response

Dear reviewer, 

We fully acknowledge the challenges associated with applying our conceptual model to grid datasets. The inherent nature of our proposed model, which is based on plant growth and consumption models, makes it difficult to transfer these models directly into raster data format. As such, we decide to represent these models as vector data rather than raster data. While remotely sensed phenology timings can be extracted as raster datasets, integrating vector data (plant growth and consumption models) with raster data (remotely sensed phenology timings) in our model requires the latter to be used as an averaging value for an area or converted to vector data format. In our study, we assumed that each plant community corresponds to a specific growth model, such as the model used for the Haibei alpine grassland in this study. Therefore, one set of remotely sensed phenology timings is applied to each area representing a specific plant community. We acknowledge the availability of numerous studies that have accurately extracted remotely sensed phenology timings in our study area, and we collected data from a meta-analysis of published papers to incorporate this information into our model. Furthermore, we have addressed the possibility of employing patch-based analysis in our model if multiple plant growth models are developed for different plant communities. This approach would require extensive ground data to facilitate model simulations, as highlighted in lines 455-460 of the manuscript.
Lastly, we believe that remotely sensed phenology timings are crucial parameters in our model, as they play a key role in characterizing remote sensing phenology characteristics. Despite being used as vector data (averaged values for areas) rather than raster data, they effectively capture the essence of remote sensing phenology. Therefore, we advocate for retaining the term "remotely sensed phenology timings" in the title.
Thank you once again for your valuable feedback and insights.