A New Spatio-Temporal Selection Method for Estimating Upwelling Medium-Wave Radiation

Round 1

Reviewer 1 Report

Accurate estimation of unperturbed upwelling radiation is crucial for detecting anomalous values and identifying radiative anomalies in the landscape. This paper proposes a Spatio-Temporal Selection (STS) process that decouples the prediction pixels from the target location, achieving a 10-40% reduction in estimation error compared to traditional contextual estimation. The STS method enhances estimation availability and enables high-fidelity image reconstructions for easily identifying thermal anomalies and geographic features.

The manuscript can be accepted before minor revisions.

for specific comments:

1. the last paragraph should be the main objectives of the current study, and the main work should be shown list by list.

2. Improve the quality of all figures and tables.

3. Update all reference with lastest ones.

Thank you for inviting me to review the manuscript.

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

In the manuscript titled proposes a new method of deriving background radiation levels, decoupling the set of prediction pixels used for estimation from the target location. However, there are still some problems with the manuscript. Therefore, some revision has to be done before this manuscript could be accepted for publication in the Remote Sens.

 1. The STS method presented in this paper can be further described, for instance what fitting method was used.

2. Whether the criteria for filtering data sets using only RMSE are rigorous？

3. Is there any theoretical basis for using the 2% anomaly rate as the standard in line 195?

Further optimization of language logic can make the manuscript more concise.

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

The Spatio-Temporal Selection method is applied in this paper to estimate the unperturbed state of upwelling radiation from the earth’s surface.

The authors propose an interesting methodology and observations trying to select training pixels for predictive purposes from a target-centred search area based upon similarities in brightness temperature. However, the experimental part of the manuscript is not comprehensive enough to highlight the superiority of the proposed method. It is hoped that the authors will make further improvements to the manuscript. Here are my specific comments：

 

1. Avoid the first-person position, such as 'we', or 'they' in technical writing, such as in ln 101, 161...etc. Third-person singular or past tense is preferred. Please revise accordingly.

 

2. All figures/tables should be described in context before the figures were placed to avoid confusion for the readers. The inappropriate figure placement was such as Figure 1, 2, 3, 5, Table 2, 3.  Please revise.

 

3. The numbering of figure captions is in “Figure 1.” format. The reference of figures in the context should be consistent, i.e. using “Figure 2c.” instead of “fig.2c.”. Please revise.

 

4. Insufficient detailed description of essential methodologies of this research, such as training methods, and model verification/validation procedures. Please try to replenish details otherwise, it is obscure for readers to follow up/duplicate the procedure.

Author Response

Please see attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

All comments have been explained in detail. 

Reviewer 3 Report

Thank you for the improvement and response.