Remote Sensing of Global Sea Surface pH Based on Massive Underway Data and Machine Learning
, Yan Bai 1,2,4,*, Xianqiang He 1,2,5, Shujie Yu 2,5, Siqi Zhang 2 and Fang Gong 2,6Round 1
Reviewer 1 Report
I think the manuscript has been improved a lot an could be considered for publication in this journal.
Author Response
Response to Reviewer 1 Comments
Point 1:
I think the manuscript has been improved a lot an could be considered for publication in this journal.
Response 1:
Thank you very much for your comment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
On lines 28-29 the terms R2 and RMSE are used without first defining them. This should be avoided.
Over all, the graphs, tables, and formulas are very well presented and appreciated.
I feel that this is a great step in learning and understanding the pH issues caused by CO2, and is a potentially great method for tracking it. Clearly, the paper has been through some revision and other editors have put forth their opinions: the authors have responded to these with a great deal of additional explanation, work, and science. The ample references further enforce this.
Author Response
Response to Reviewer 2 Comments
Point 1:
On lines 28-29 the terms R2 and RMSE are used without first defining them. This should be avoided.
Over all, the graphs, tables, and formulas are very well presented and appreciated.
I feel that this is a great step in learning and understanding the pH issues caused by CO2, and is a potentially great method for tracking it. Clearly, the paper has been through some revision and other editors have put forth their opinions: the authors have responded to these with a great deal of additional explanation, work, and science. The ample references further enforce this.
Response 1:
Thank you for your comments. We have added the definitions at line 28 of the manuscript.
The full expression of this sentence is:
After several tests of machine learning methods and groups of input parameters, we chose the random forest model with longitude, latitude, sea surface temperature (SST), chlorophyll a (Chla) and Mixed layer depth (MLD) as model inputs with the best performance of correlation coefficient (R² = 0.96) and root mean squared error (RMSE = 0.008) in the training set and R² = 0.83 (RMSE = 0.017) in the testing set.
Author Response File: Author Response.docx
