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Article
Peer-Review Record

Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales

Agronomy 2022, 12(8), 1972; https://doi.org/10.3390/agronomy12081972
by Jae-Hyun Ryu 1,2, Dohyeok Oh 1,3, Jonghan Ko 1, Han-Yong Kim 1, Jong-Min Yeom 4 and Jaeil Cho 1,5,*
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Agronomy 2022, 12(8), 1972; https://doi.org/10.3390/agronomy12081972
Submission received: 4 July 2022 / Revised: 12 August 2022 / Accepted: 17 August 2022 / Published: 20 August 2022
(This article belongs to the Special Issue Advances in Field Spectroscopy in Agriculture)

Round 1

Reviewer 1 Report

It is good job. Just several minor questions.
1. Only one rice cultivar was mentioned in this manuscript, why "These japonica rice cultivars " in line 127?

2. How about the rice cultivar Saenuri,such as the extension and application of this cultivar in your country?
3. Is the regular pattern revealed in this study applicable to other rice varieties?

4. Line 57:"……according to the comprehensive results obtained by [12]“,by what???
5. Line170:"……in the
study of [43]  ",This kind ofwriting is rarely seen.

Author Response

Please see our attached file.

Author Response File: Author Response.pdf

Reviewer 2 Report

In this study, vegetation indices derived from remote sensing were used to assess the effects of heat stress on paddy rice under elevated air temperatures in a temperature gradient field chamber. It is a very detailed paper with some interesting objectives that could make substantial contributions to this field. I recommend that it be accepted after a few minor modifications.

1- There is no mention of the distance between the multispectral camera and the samples. A clear explanation should be provided

2- I believe that the introduction should be revised to include relevant studies. It is recommended to include the following papers, for example

·         Kumagai, E., Burroughs, C. H., Pederson, T. L., Montes, C. M., Peng, B., Kimm, H., ... & Bernacchi, C. J. (2022). Predicting biochemical acclimation of leaf photosynthesis in soybean under in‐field canopy warming using hyperspectral reflectance. Plant, Cell & Environment, 45(1), 80-94.

·         Melandri, G., Thorp, K. R., Broeckling, C., Thompson, A. L., Hinze, L., & Pauli, D. (2021). Assessing Drought and Heat Stress-Induced Changes in the Cotton Leaf Metabolome and Their Relationship With Hyperspectral Reflectance. Frontiers in plant science, 12.

·         Park, E., Kim, Y. S., Omari, M. K., Suh, H. K., Faqeerzada, M. A., Kim, M. S., ... & Cho, B. K. (2021). High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image. Sensors, 21(16), 5634.

 

·         Xie, X. J., Zhang, Y. H., Li, R. Y., Shen, S. H., & Bao, Y. X. (2019). Prediction model of rice crude protein content, amylose content and actual yield under high temperature stress based on hyper-spectral remote sensing. Quality Assurance and Safety of Crops & Foods, 11(6), 517-527.

Author Response

Please see our attached file.

Author Response File: Author Response.pdf

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