Reconstruction of Hourly FY-4A AGRI Land Surface Temperature under Cloud-Covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors(1) The LST correlation model is based on the LST on the clear-sky conditions, can it be used in cloudy-sky conditions?
(2) How many valid samples are in the measured LST scale conversion?
(3) Section 3.1, With whom are measured LST before and after scale conversion compared?
(4) Figure 8, it seems that the reconstructed LST is smoother in temporal than the reference LST, which seems unreasonable, please explain.
(5) There is similar research on the reconstruction of all-weather LST from geostationary satellites, please cite it. https://doi.org/10.1109/TGRS.2022.3227074
Author Response
Dear Reviewer,
We would like to resubmit our revised manuscript for publication in Remote Sensing titles “Reconstruction of Hourly FY-4A AGRI Land Surface Temperature Under Cloud-covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information” (Manuscript ID: remotesensing-2977505) after addressing the editor and reviewers’ comments (and suggestions). We would like to express our gratitude to your valuable comments and constructive suggestions on our manuscript, which we used to revise our manuscript. Revised portions are marked in red in the manuscript. In the following document we will address your comments (and suggestions) point by point. We hope you can reconsider our manuscript.
Best regards.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsLand surface temperature (LST) is an important parameter in climate change applications. This paper describes the methodological steps in detail and provides a full analysis and discussion of the results. The S-G filtering method is employed in this paper, which takes into account the characterization of FY4A in terms of temporal resolution. The language and academic writing of the manuscript are correct.
However, several issues need to be clarified before it can be accepted.
1. In the validation section, the authors consider the low spatial resolution of FY4A but do not elaborate on the methodology in section 2.3.4.
2. The bias is also an important metric to validate the results of LST retrieval or reconstruction, please give the results of the bias.
Author Response
Dear Reviewer,
We would like to resubmit our revised manuscript for publication in Remote Sensing titles “Reconstruction of Hourly FY-4A AGRI Land Surface Temperature Under Cloud-covered Conditions Using a Hybrid Method Combining Spatial and Temporal Information” (Manuscript ID: remotesensing-2977505) after addressing the editor and reviewers’ comments (and suggestions). We would like to express our gratitude to your valuable comments and constructive suggestions on our manuscript, which we used to revise our manuscript. Revised portions are marked in red in the manuscript. In the following document we will address your comments (and suggestions) point by point. We hope you can reconsider our manuscript.
Best regards.
Author Response File: Author Response.docx
