Next Article in Journal
Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling
Previous Article in Journal
Extraction of Floating Raft Aquaculture Areas from Sentinel-1 SAR Images by a Dense Residual U-Net Model with Pre-Trained Resnet34 as the Encoder
 
 
Article
Peer-Review Record

UAV-LiDAR Measurement of Vegetation Canopy Structure Parameters and Their Impact on Land–Air Exchange Simulation Based on Noah-MP Model

Remote Sens. 2022, 14(13), 2998; https://doi.org/10.3390/rs14132998
by Guotong Wu 1, Yingchang You 1, Yibin Yang 2, Jiachen Cao 1, Yujie Bai 1, Shengjie Zhu 3, Liping Wu 1, Weiwen Wang 1, Ming Chang 1,* and Xuemei Wang 1
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Remote Sens. 2022, 14(13), 2998; https://doi.org/10.3390/rs14132998
Submission received: 25 April 2022 / Revised: 14 June 2022 / Accepted: 20 June 2022 / Published: 23 June 2022

Round 1

Reviewer 1 Report

This study aims to update more specific canopy structure information in the Noah-MP model. To do it, the 3D canopy-structure information collected by the UAV-LiDAR system was used to fit the distribution of canopy parameters and the parameters of the Noah-MP model were modified in a typical forest area. This research is relevant and interesting. The scientific contribution and specific novelty of this paper are edited well. The methods and results are presented adequately. Some major revisions may be needed before publication.

DETAILED COMMENTS:

(1) More contributions of Noah-MP should be described in the abstract.

(2) The objectives of this research are not clear, please summarize them in detail.

(3) Figure 1: the colors of different vegetation types are similar, which is very difficult to understand. Change the color style.

(4) More parameters about the acquisition of LiDAR and image should be added, such as scan angle, overlap, point density, etc.

(5) More contents about the Noah-MP model should be added, especially the correlation of vegetation structural parameters with the model's parameters.

(6) The Results section should write the findings of this study more than citing the conclusions of others. It is suggested to move the content that is not the results to Discussion.

(7)Figure 5: add the accuracy (e.g., R2 or RMSE) to Figure 5.

(8) What's the role of UAV dense image in this study? A discussion about the ability of point clouds derived from UAV dense image should be added.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Dear authors,

thanks for very interesting paper. It looks fine with me with one comment. It is not clear for me relation of scheme on Figure 2 and text in next chapters 2.3 - 2.6. I don't see here clear relation. Please explain better  or link it better with next text

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

This was a well-written paper.  I only have a few comments:

 

Lines 157-158:  I do not see blue and green crosses.

Lines 183-187: What is the “single wood” segmentation and location? Is this referring to individual tree metrics?  I’m not familiar with this jargon.  How exactly were the structure parameters obtained?  Manually?

Line 239: How exactly were the observed values obtained?

Line 265:  Please check R2 of 0.01.

Figures: Check to make sure all symbols and legend entries are visible: e.g., default triangle in Fig. 8, R^2 in Fig. 7.

 

Line 424: Recommend more realistic “centimeter” instead of “millimeter” accuracy.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

The authors have incorporated most of the modifications proposed, to improve their manuscript, which is highly appreciated. Now, this is a good paper may suitable for publication.

Back to TopTop