Review Reports - Combining RadCalNet Sites for Radiometric Cross Calibration of Landsat 9 and Landsat 8 Operational Land Imagers (OLIs)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This paper combins the RadCalNet Sites to evaluate the Radiometric Cross Calibration 2 of Landsat 9 and Landsat 8 OLI, which is a SI-traceable inter calibration methodology to quantify the offset 22 and uncertainty. A good agreement has been found in the Landsat 8 and 9 OLI instruments. Some questions are shown as follows:

1. Table 1 shows the uncertainty, plesase give its conditions becasuse in every channel the uncertainty is not same.

2. Can you give a conclusion, which site has the best calibration results, and the comparison results of various sites.

3. Figure 6-7, some results are very worse, please analyze the reason.

Comments on the Quality of English Language

The quality of English Language is very good.

Author Response

The authors thank the reviewer for the time spent on the review and the insightful comments that led to a strengthening of the manuscript. The manuscript has been modified to address the issues raised by the comments. Please find point-by-point response to the reviewer's comments attached below.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors have combined the RadCalNet Sites for Radiometric Cross Calibration of Landsat 9 and Landsat 8 OLI. They assert that RadCalNet sites offer a potent vicarious calibration method, enabling the calibration of multiple sensors to a common SI traceable reference. I have some questions that should be addressed in this paper.

1- Generally, there are two broad categories of radiometric correction approaches [x, xx]: absolute radiometric normalization (ARN) and relative radiometric normalization (RRN). ARN approaches typically involve converting digital numbers (DNs) of pixels to surface reflectance values through modeling the atmosphere or using definite transformation equations. On the other hand, RRN techniques are often preferred due to the use of cost-effective image processing operations to rectify radiometric distortions in temporal images without requiring sensor parameters and in situ atmospheric data at the time of imagery acquisition.
Please refer to this matter by citing the following papers and state why the authors chose to use the ARN method based on the RadCalNet Sites for L8 and L9.

[x] A. Moghimi, A. Sarmadian, A. Mohammadzadeh, T. Celik, M. Amani and H. Kusetogullari, "Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-20, 2022, Art no. 5400820, doi: 10.1109/TGRS.2021.3063151.

[xx] C. Lo and X. Yang, "Some practical considerations of relative radiometric normalization of multidate Landsat MSS data for land use change detection", Proc. ASPRS/RTI Annu. Conv., pp. 1184-1193, 1998.

2- Please include the following papers to support the idea:
https://doi.org/10.3390/rs11202401
https://doi.org/10.1016/j.rse.2023.113755
https://doi.org/10.3390/rs11192253
The contribution of the work should be listed in the last paragraph of the introduction.

3- If the data is acquired from the same area, please visually present them for better readability. Readers want to discern the differences between data acquired by Landsat 8 and 9.

4- Please visualize the results after calibration based on the RadCalNet Sites. I expect the images to be very similar to each other after the calibration. The authors should use the RMSE between the two images to assess the differences before and after calibration based on the RadCalNet Sites between L8 and L9 images.

5- I couldn't find a dataset description. Please add this and apply the proposed approach to two images from L8 and L9 acquired from the same site at the same time, as atmospheric conditions may vary at different times.