Radiometric Correction with Topography Influence of Multispectral Imagery Obtained from Unmanned Aerial Vehicles

Round 1

Reviewer 1 Report

The manuscript deals with methods of radiometric correction of multispectral images. The aim of the manuscript is clearly defined in Section 1.2.

 

Questions and comments:

- add a section in the abstract devoted to the results obtained 

- Figs. 6,7,8 missing scale

- Row 294 data were collected in the month of June. What influenced the choice of this period? In June the area is covered with dense vegetation. Have you considered comparing the period with vegetation - June and the period without vegetation - December?

- Fig. 10,11 in the legend the item "background" is shown - what exactly does this item represent? Would it not be more appropriate to remove it?

- How were the points for the GPS measurements selected and were the 10 points sufficient? For clarity, it would be useful to add the distribution of points for the GPS measurements to Figures 10 and 11.

 

Author Response

Dear Reviewer,

We are very grateful for your insightful review and valuable comments and remarks on our paper. You have raised some important issues, and your comments have been very helpful for improving the manuscript. We agree with all your remarks, and we have revised our present research paper in the light of your useful suggestions. The detailed explanations and corrections in line with your recommendations are described below. We have also added a draft version of the article with the corrections and modifications in the text marked in red to make them easier to locate.

We will be happy to introduce any further changes that may be required.

List of changes

Rev #1: The paper was corrected according to the presented comments.

Ad. 1 add a section in the abstract devoted to the results obtained 

Thank you for your advice. We have added additional information to the abstract.

Ad. 2 Figs. 6,7,8 missing scale

Thank you for your advice. We have added missinga scale to Fig. 7 and 8. We think that the scale for the image of the whole country is unnecessary.

Ad. 3 Row 294 data were collected in the month of June. What influenced the choice of this period? In June the area is covered with dense vegetation. Have you considered comparing the period with vegetation - June and the period without vegetation - December?

Thank You for your comment. In section 3 we explained our motives to conduct data acquisition only in June. In our further research we will acquire data in different months. Please see lines 379-387:

Image data were acquired in June 2021. The choice of measurement period was selected based on the prevailing meteorological conditions and the degree of vegetation development. The weather conditions in June 2021 made it possible to obtain data at a low wind speed of 5 m/s, guaranteeing flight stability and air humidity below 30%. Thanks to this, it was possible to eliminate noise in the infrared channels. In addition, in 2021, the vegetation was in full bloom due to the growing season and the prevailing weather conditions, especially the level of precipitation and air temperature.

The areas where the research was carried out allowed to obtain data for areas without vegetation - thus simulating the situation in winter, when the vegetation dies.

Ad. 4 Fig. 10,11 in the legend the item "background" is shown - what exactly does this item represent? Would it not be more appropriate to remove it?

We have corrected images 10 i 11 (Now Fig. 11 and Fig. 12).

Ad. 5 How were the points for the GPS measurements selected and were the 10 points sufficient? For clarity, it would be useful to add the distribution of points for the GPS measurements to Figures 10 and 11.

We have added the distribution of Ground Control Points to the Figures.

 We improved the language, grammar, and readability. We verified some language expressions again and corrected typos. The paper was revised by a native speaker.

Author Response File: Author Response.pdf

Reviewer 2 Report

Overall this looks to be a very good paper which is likely to be of interest to many people undertaking UAV remote sensing. My only suggestion would be that the paper needs a thorough edit by a native English speaker and proof-reading as the paper is a little hard to read and understand in places because of the sentence structure in places throughout the manuscript.

Author Response

Dear Reviewer,

We are very grateful for your insightful review and valuable comments and remarks on our paper. You have raised some important issues, and your comments have been very helpful for improving the manuscript. We agree with all your remarks.

We improved the language, grammar, and readability. We verified some language expressions again and corrected typos. The paper was revised by a native speaker.

 

 

Reviewer 3 Report

Abstract logic is hard to follow. The abstract should provide more details on the advantages of the proposed method over others.

Please describe in detail the data acquisition process and data volume of the FieldSpec 4 Wide-Res spectroradiometer. 

The empirical formula mentioned in the paper may be inaccurate, can the method maintain good accuracy if it is applied to other scenarios. 

Lines 407-439: The authors Introduce three radiometric correction methods, but do not explain the principles and specific steps of the second and third methods. For the newly developed third method, the authors only presents results without specifying the method steps. 

Lines 585-599 In terms of supervised classification, only use maximum likelihood method for classification without comparing multiple methods. Additionally, only methods 1 and 3 are classified, and method 2 is not classified. 

It would be helpful to provide more background information on the importance of radiometric correction for multispectral images, especially for those obtained from low altitudes using UAVs. This could include a brief overview of the challenges associated with such data and how radiometric correction can help to address them. 

While the paper briefly mentions existing radiometric correction techniques, it would be helpful to provide more detail on the specific methods that have been used and their relative strengths and weaknesses. This could help to provide context for the new method proposed in the article. 

The paper could benefit from including more examples of the types of images that are commonly affected by radiometric distortion, as well as the practical applications of radiometric correction in various fields, such as agriculture, forestry, and environmental monitoring. 

The paper should provide a more detailed discussion of the limitations of existing radiometric correction techniques, and how the newly proposed approach addresses these limitations.

Author Response

Dear Reviewer,

We are very grateful for your insightful review and valuable comments and remarks on our paper. You have raised some important issues, and your comments have been very helpful for improving the manuscript. We agree with all your remarks, and we have revised our present research paper in the light of your useful suggestions. The detailed explanations and corrections in line with your recommendations are described below. We have also added a draft version of the article with the corrections and modifications in the text marked in red to make them easier to locate.

We will be happy to introduce any further changes that may be required.

List of changes

Rev #3: The paper was corrected according to the presented comments.

Ad. 1 Abstract logic is hard to follow. The abstract should provide more details on the advantages of the proposed method over others.

Thank You for comment. We have provided more detsails in abstract.

Ad. 2 Please describe in detail the data acquisition process and data volume of the FieldSpec 4 Wide-Res spectroradiometer.

Thank You for comment. We have described in detail the data acquisition process and data volume of the FieldSpec 4 Wide-Res spectroradiometer. Please see lines 339-343:

Spectroradiometer data were obtained using a 1° probe or a plant probe. Spectral information was obtained with a frequency of ten measurements per measurement. And the results were averaged from 20 measurements. Before each measurement, a calibration was performed based on a reflectance standard of 95%.

Ad. 3 The empirical formula mentioned in the paper may be inaccurate, can the method maintain good accuracy if it is applied to other scenarios.

Thank You for comment. As we mention in conclusions, we willcontinue research in future in different time and weather conditions.

Ad. 4 Lines 407-439: The authors Introduce three radiometric correction methods, but do not explain the principles and specific steps of the second and third methods. For the newly developed third method, the authors only presents results without specifying the method steps.

Thank You for comment. Our methodology is presented in Fig.15 and follow by the procedure.

Ad. 5 Lines 585-599 In terms of supervised classification, only use maximum likelihood method for classification without comparing multiple methods. Additionally, only methods 1 and 3 are classified, and method 2 is not classified.

Thank You for comment. Due to our overside we have missed method to results. We’ve added results to Figure 26 and we explained the choice of classification method. Please see lines 723-730:

The next step in evaluating the results was to perform supervised classification. During the described work, a supervised classification was carried out using several commonly used classifiers, such as minimum distance, Mahalanobis distance, KNN, etc. The accuracy of individual classification methods was analysed for the data for which the correction was made using the first approach. Among the applied, the best results in the accuracy assessment were obtained by the Maximum Likelihood method. The choice of such simple and commonly used methods was dictated by the assumption that these methods are most often used by researchers and users, e.g. in precision farming applications. using the Maximum Likelihood method. As in the case of SAM spectral angle analysis, the best results were obtained for the correct image after applying the proposed original correction method (Fig.26.) .

Ad. 6 It would be helpful to provide more background information on the importance of radiometric correction for multispectral images, especially for those obtained from low altitudes using UAVs. This could include a brief overview of the challenges associated with such data and how radiometric correction can help to address them.

Thank You for comment. We have provided more background information on the importance of radiometric correction for multispectral images, especially for those obtained from low altitudes using UAVs. Please lines 66-85:

…considered [11–17][11–17]. The application of radiometric correction is essential in remote sensing applications. The process of radiometric calibration refers to the ability to convert the digital numbers recorded by imaging systems into physical units like radiance (W/m2/sr/µm) or apparent top-of-atmosphere reflectance. That type of correction is crucial for reliable quantitative measurements of the imagery [18]. When studying climate changes, it is important to know the correct reflectance values of forests to establish accurately their health [19–23]. Moreover, in precision agriculture, accurate reflectance data are used to determine whether a crop field is being watered properly or to look for pest infestation [24–27]. In addition, accurate spectral response is crucial in different multitemporal analyses [28–30].

The importance of radiometric correction in the case of satellite and aerial imagery is obvious. Due to the high altitude of the platforms, the influence of the atmosphere must be removed. However, in the case of UAV data, it is not so obvious. As mentioned by Shin et al. [22], UAV images are acquired at a relatively low altitude, compared to aerial or satellite imagery, therefore they may not possess significant radiometric distortions. However, UAV images have small field of view, and therefore it is important to proceed with mosaicking. Because the time to acquire images by UAV for a large area is much longer than in the case of aerial or satellite images, each UAV image may experience different turbulence, a different incidence angle, different illumination, or different signal processing chains. Therefore, radiometric correctio of UAV images is crucial.

Ad. 7 While the paper briefly mentions existing radiometric correction techniques, it would be helpful to provide more detail on the specific methods that have been used and their relative strengths and weaknesses. This could help to provide context for the new method proposed in the article.

Thank You for comment. We have provided more details on the specific methods that have been used. Please see lines 279-285:

Commonly used radiometric methods for UAV data are quite simple and easy to use, however are based only on flight parameters and DEM and reference panels used for camera radiometric calibration at the beginning of the flight. Therefore, results are not very accurate for different land cover types like vegetation and artificial surfaces, especially when flight is conducted in different light conditions. Our new method is based not only on such parameters, but we also consider different land cover types. However, for now it can be used only for full vegetation season.

Ad. 8 The paper could benefit from including more examples of the types of images that are commonly affected by radiometric distortion, as well as the practical applications of radiometric correction in various fields, such as agriculture, forestry, and environmental monitoring.

Thank You for comment. Radiometric distorsions incluence the spectral reflectance characteristics application of radiometric correction allow to correct radiometric errors. So we have found adding adddiotnal comments unnecessary.

Ad. 9 The paper should provide a more detailed discussion of the limitations of existing radiometric correction techniques, and how the newly proposed approach addresses these limitations.

Thank You for comment. In cocnlusions we have added the limitations of existing radiometric correction techniques. Please see lines 899-910:

Commonly used radiometric methods for UAV data are quite simple and easy to use, however are based only on flight parameters and DEM and reference panels used for camera radiometric calibration at the beginning of the flight. Therefore, results are not very accurate for different land cover types like vegetation and artificial surfaces, especially when flight is conducted in different light conditions. Our new method is based not only on such parameters, but we also consider different land cover types. However, for now it can be used only for full vegetation season.  Further research is needed to evaluate the performance of of our new radiometric correction under different weather and geographic condition the empirical line correction under different conditions and to compare it with other radiometric correction methods.

We improved the language, grammar, and readability. We verified some language expressions again and corrected typos. The paper was revised by a native speaker.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Thank you for writing the responses to the review and completing the manuscript.

Reviewer 3 Report

English has improved but the whole article is still poorly readable.