A Preliminary Study on the Use of Remote Sensing Techniques to Determine the Nutritional Status and Productivity of Oats on Spatially Variable Sandy Soils
Round 1
Reviewer 1 Report
Comments and Suggestions for Authors1.In the abstract, there should be a description of the research results, supported by data.
2. In the results analysis, correlation analysis and ANOVA should be tested for significance.
3. In lines 282-287, is it necessary to include descriptive statistics instead of the results in Figure 5?
4. Does EVI reflect the diversity of oat plant structure in lines 289-291? This should be supported by relevant literature.
5. Does the considerable dispersity of MCARI in lines 291-294 indicate that chlorophyll content varies significantly between plants? Anova is needed here, and relevant literature is available to support the research results.
6. In 295-298 lines, there was a significant correlation between NDVI and fresh biomass of plants. Relevant significance indicators need to be provided here.
7. In 298-301 lines, nitrogen uptake by plants was significantly correlated with NDVI, SAVI, EVI and NDMI. Relevant significance indicators also need to be provided here.
8. All the research results in this paper should be supported by relevant literature.
9. During the discussion, the research results should be compared with previous studies to confirm the reliability of the conclusions of this study.
10. GNDVI index has not been used to guide the early fertilization of oats. Does it prove that GNDVI is an effective tool?
Author Response
02 February 2025
General comment:
We thank the Reviewers for their effort in reviewing our manuscript and valuable comments which helped to improve our study. We have considered all comments carefully and provided explanations below.
Responses to Reviewer 1
Comment:
1.In the abstract, there should be a description of the research results, supported by data.
Response:
Thank you for comment. The abstract was corrected as follows (lines 27-33):
“The results of the study confirmed that sandy soils, characterized by limited water and nutrient capacity, require a specialized approach to resource management. The selected remote sensing indices provided an effective method for monitoring oat canopy variability in real time. They are useful tools, providing valuable information necessary for precise management of oat crops. Fertilization supported with remote sensing monitoring increases fertilization efficiency and optimizes yields in diverse soil conditions.”
Comment:
- In the results analysis, correlation analysis and ANOVA should be tested for significance.
Response:
For figures that present the correlation between parameters, p-values were calculated and added to the results as a new figures 5-14.
Unfortunately, analyzed parameters don't have a normal distribution but a left-skewed distribution which makes ANNOVA analysis irrelevant in our case. However, for future studies, it is planned to make PCA analysis to distinguish the independence of different groups. To make the information clear to the reader that the analysis can’t be based on normal distribution statistical tests following sentence was added to the manuscript in section 2.3. (lines 260-263):
“Analysed parameters like N uptake, sand content(%) or GNDVI in different phenological stages represent left-skewed distribution samples although the correlation and p-values were calculated to estimate the most promising parameters to use for predicting biomass yield and N uptake by oats. “
Comment:
- In lines 282-287, is it necessary to include descriptive statistics instead of the results in Figure 5?
Response:
In lines 282-287, the data variation of field measurement results and remote sensing indices were presented. Figure 5 includes the relationships between the parameters. The improved version adds the significance of correlations.
Comment:
- Does EVI reflect the diversity of oat plant structure in lines 289-291? This should be supported by relevant literature.
Response:
In the structure of the manuscript, a discussion of results has been separated from result. A reference on the EVI index has been added in the discussion of results chapter.
Relevant literature added (lines 629-632
Xue, J.; Su, B. Significant remote sensing vegetation indices: A review of developments and applications. J. Sens. 2017, 2017, 1353691
Zou, X.; Mõttus, M. Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops. Remote Sens. 2017, 9, 994. https://doi.org/10.3390/rs9100994
Comment:
- Does the considerable dispersity of MCARI in lines 291-294 indicate that chlorophyll content varies significantly between plants? Anova is needed here, and relevant literature is available to support the research results.
Response:
Due to the nature of the collected data, only correlations and p-values were estimated to determine the relationship between the results.
Comment:
- In 295-298 lines, there was a significant correlation between NDVI and fresh biomass of plants. Relevant significance indicators need to be provided here.
Response:
This comment probably concerns the part of the manuscript between lines 315-321 (in the revised version line 382). All correlation coefficients have been added.
Comment:
- In 298-301 lines, nitrogen uptake by plants was significantly correlated with NDVI, SAVI, EVI and NDMI. Relevant significance indicators also need to be provided here.
Response:
Correlation coefficients have been added (lines 387-388).
Comment:
- All the research results in this paper should be supported by relevant literature.
Response:
Additional papers added in manuscript (line 157, 632).
Rao, M. S., Reddy, V. R. (2016). Nutrient management in cereal crops: The early growth phases and their impact on yield. Agronomy Journal, 108(6), 2376-2389.
Xue, J.; Su, B. Significant remote sensing vegetation indices: A review of developments and applications. J. Sens. 2017, 2017, 1353691
Zou, X.; Mõttus, M. Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops. Remote Sens. 2017, 9, 994. https://doi.org/10.3390/rs9100994
Comment:
- During the discussion, the research results should be compared with previous studies to confirm the reliability of the conclusions of this study.
Response:
Corrected (lines 568-571, 607-612, 629-632)
The size and surface area of soil particles are very important because they are the place where chemical and biochemical processes occur that affect soil properties such as fertiliser retention, hydraulic conductivity, soil structural stability and microbial abundance [Hu et al. 2015].
In earlier studies, GNDVI has not been used to monitor the nutritional status of plants in early growing stages. The illustrated correlations of field basic parameters, such as bio-mass yield, nitrogen uptake or soil texture with GNDVI indicate its usefulness for monitoring plant condition. By early identification of the dependence of GNDVI with soil moisture, it can be a valuable tool for optimizing fertilization and making decisions affecting yield.
Variability in the EVI index suggests diversity in the density and structure of oat plants across the field. Index EVI was developed to be less sensitive to measurement due to changes within the crop canopy and is dependent on plant structure and has a reduced background effect on its value [Xue 2017, Zou 2017].
Comment:
- GNDVI index has not been used to guide the early fertilization of oats. Does it prove that GNDVI is an effective tool?
Response:
Additional information added (revised version L. 607-612)
“In earlier studies, GNDVI has not been used to monitor the nutritional status of plants in early growing stages. The illustrated correlations of field basic parameters, such as biomass yield, nitrogen uptake or soil texture with GNDVI indicate its usefulness for monitoring plant condition. By early identification of the dependence of GNDVI with soil moisture, it can be a valuable tool for optimizing fertilization and making decisions affecting yield.”
Author Response File: 
Author Response.docx
Reviewer 2 Report
Comments and Suggestions for AuthorsLine number, comment
72 Rephrase this sentence. "with-out" should be "without". "form" should probably "from". "significant changing last 50 years" should be "significant changes in the last 50 years."
79 Discuss these indices, why were they chosen and examined? What are the differences among them?
123 Why were these dates chosen, as opposed to others?
124 Define, discuss, and give references for BBCH. Many readers will not know what BBCH is and will need a reference.
135 give reference for the Kjeldahl method used for N content and oat biomass.
137 Discuss in detail the processing of the Sentinel-2 Copernicus data. What level of data were used? How were atmospheric corrections made? How was cloud filtering done? What QC methods were used? How were indices calculated? Which bands were processed and how were they processed?
142 Give dates of satellite acquisitions so that they can be peer-reviewed.
140 Is it appropriate to use data from AGRICOLUS for scientific use? What protocols are in place for determining the data quality from a commercial partner such as AGRICOLUS? What assurances do you have that their data workflow is appropriate for your research? Please address the technical aspects of their product relative to direct use of the Sentinel-2 data from Copernicus. How is their product different/better/worse than the source data? How has it been modified or packaged for the end user?
147 Discuss how each index is calculated mathematically with respect to the band architecture of the satellite reflectance bands. Give mathematical representations of the calculations of the indices.
240 "using a digital device" What was the device? What was the brand name? What was its accuracy and error? Did you use a cell phone? What kind of antenna was used? How accurate were the location estimates?
241 again, please discuss the decisions that determined when images were obtained, and the criteria for image acceptance/rejection. Also, discuss the methods for calculating the indices either here or elsewhere in the paper.
248 Why did the density differ? Were the crops planted at different densities, or does this variability relate to mortality, or some other reason?
438 You could also discuss the direct relationship between particle size and nutrient holding capacity in terms of the exchange complex (clays and silts having higher surface area than sands and consequently more nutrient holding capacity). There is a large amount of literature on this and it should be touched on. Any intro soil fertility textbook will give you good information.
463 Table 4 shows the maximum plant density upwards of 500 plants per m2. Please explain what the target planting density was and why the actual measured plant density could be higher than the planned planting density. Does this indicate a failure of planting the right number of crops, or how did it happen? Presumably the planter is calibrated to provide the target number of plants per acre. How and when would it be exceeded?
517 "it is strongly recommended to use more" Such as? Please explain and be explicit about what more is needed.
What taxonomic information is available about the geologic and pedological development and classification of the soils? What are their taxonomic classifications and characterizations? What gradients in soil development and/or taxonomy does the site have? Please address the natural soils and their variability from a taxonomic standpoint. At the very least, describe their classification in the methods section.
It was fine, except where indicated by line number.
Author Response
02 February 2025
General comment:
We thank the Reviewers for their effort in reviewing our manuscript and valuable comments which helped to improve our study. We have considered all comments carefully and provided explanations below.
Responses to Reviewer 2
Comment:
72 Rephrase this sentence. "with-out" should be "without". "form" should probably "from". "significant changing last 50 years" should be "significant changes in the last 50 years."
Response:
Corrected
Comment:
79 Discuss these indices, why were they chosen and examined? What are the differences among them?
Response:
Indices characteristics were given in methods chapter (original version - lines 157-171, revised version Lines 228-247).
Comment:
123 Why were these dates chosen, as opposed to others?
Response:
Specific growing phases were selected for measurements due to their basic importance for assessing the crop yield indices as well as the possibility of making management decisions related to the correction of agricultural technology.
Rao, M. S., Reddy, V. R. (2016). Nutrient management in cereal crops: The early growth phases and their impact on yield. Agronomy Journal, 108(6), 2376-2389.
Comment:
124 Define, discuss, and give references for BBCH. Many readers will not know what BBCH is and will need a reference.
Response:
Added (L. 153-1570
Meier, U. (2001) Growth Stages of Mono and Dicotyledonous Plants. BBCH Monograph, Federal Biological ResearchC entre for Agriculture and Forestry, Bonn
Comment:
135 give reference for the Kjeldahl method used for N content and oat biomass.
Response:
The Kjeldahl method was chosen due to its high precision and reliability in determining the total nitrogen content in oat biomass.
Added (L. 174)
Amin M., Flowers T.H. Evaluation of Kjeldahl digestion method. J. Res. Science. 2004;15:159–179
Comment:
137 Discuss in detail the processing of the Sentinel-2 Copernicus data. What level of data were used? How were atmospheric corrections made? How was cloud filtering done? What QC methods were used? How were indices calculated? Which bands were processed and how were they processed?
Response:
The following explanation was inserted in lines 187-197
“Level 2A data that has already been atmospherically corrected was used. This correction resulted in data that reflects real conditions on the surface, not just the atmospheric conditions at a measurement moment. Atmospheric correction at level L2A was performed using the Sen2Cor algorithm.
Using Sentinel-2 data at level L2A, the C2RCC (Sentinel-2 Cloud and Cloud Shadow Mask) algorithm, used by the Copernicus system, automatically detects clouds by creating appropriate cloud masks. Thanks to this process, which takes place at the data processing stage, pixels containing clouds and cloud shadows were removed from the analyzed images.
In the data analysis, specific point locations were selected, and the Point Sampling Tool plug-in was used to precisely determine them.”
And additionally L. 215-222 “The following formulas for individual indices were used for calculations:
- NDVI = (NIR – RED)/(NIR + RED)
- GNDVI = (NIR–G) / (NIR+G)
- SAVI = (NIR – RED) × (1 + L) / (NIR + RED + L)
- EVI = G × (NIR – RED) / (NIR + C1 × RED – C2 × BLUE + L)
- NDMI = (NIR – SWIR) / (NIR + SWIR)
- MCARI = [(RED_EDGE1 – RED) – 0.2 × (RED_EDGE1 – GREEN)] × (RED_EDGE1 / RED)”
Comment:
142 Give dates of satellite acquisitions so that they can be peer-reviewed.
Response:
The dates of satellite acquisitions were provided in Table 1 (L. 224). Moreover the following explanation was provided in lines 162-166
“The selected image dates were the closest to the field analysis dates. The satellite captures images every five days, so those that best matched the study periods and had minimal cloud cover were chosen to avoid distorting the results. Other available images were rejected due to high cloud cover, which could have negatively impacted the quality of the analysis.”
Comment:
140 Is it appropriate to use data from AGRICOLUS for scientific use? What protocols are in place for determining the data quality from a commercial partner such as AGRICOLUS? What assurances do you have that their data workflow is appropriate for your research? Please address the technical aspects of their product relative to direct use of the Sentinel-2 data from Copernicus. How is their product different/better/worse than the source data? How has it been modified or packaged for the end user?
Response:
AGRICOLUS is a platform which supports modern agricultural practices and was developed basically for farmers. It enables data collection and provides some decision-making tools. It can also be valuable for scientific research as it gives access to all information related to cultivation practices at the field scale. The Wroclaw University of Environmental and Life Sciences contributed to the development of this platform in the framework of Horizon projects such as Wateragri and Farmwise.
This platform was used to analyze the plant condition in actual time and quickly provided visual information on the changes of indicators in the field.
In the research, data from AGRICOLUS were not used directly, because the platform calculates indicators at the level of the entire field, not for a single point. To obtain more precise results, data from QGIS was used, which confirmed the consistency of the results with AGRICOLUS.
Comment:
147 Discuss how each index is calculated mathematically with respect to the band architecture of the satellite reflectance bands. Give mathematical representations of the calculations of the indices.
Response:
The following text was included in lines 215-222:
“The following formulas for individual indices were used for calculations:
- NDVI = (NIR – RED)/(NIR + RED)
- GNDVI = (NIR–G) / (NIR+G)
- SAVI = (NIR – RED) × (1 + L) / (NIR + RED + L)
- EVI = G × (NIR – RED) / (NIR + C1 × RED – C2 × BLUE + L)
- NDMI = (NIR – SWIR) / (NIR + SWIR)
- MCARI = [(RED_EDGE1 – RED) – 0.2 × (RED_EDGE1 – GREEN)] × (RED_EDGE1 / RED)”
Comment:
240 "using a digital device" What was the device? What was the brand name? What was its accuracy and error? Did you use a cell phone? What kind of antenna was used? How accurate were the location estimates?
Response:
Text added in lines 301-305:
“The map of sampling points was prepared in Google Earth, and then the points in the field were navigated using Google Maps. To accurately locate the points, a built-in GPS receiver in a mobile phone was used, which allowed for determining geographic coordinates. The accuracy of the location estimates depended on the quality of the GPS signal. In most cases the accuracy was 2-3 m.”
Comment:
241 again, please discuss the decisions that determined when images were obtained, and the criteria for image acceptance/rejection. Also, discuss the methods for calculating the indices either here or elsewhere in the paper.
Response:
“The selected image dates were the closest to the field analysis dates. The satellite captures images every five days, so those that best matched the study periods and had minimal cloud cover were chosen to avoid distorting the results. Other available images were rejected due to high cloud cover, which could have negatively impacted the quality of the analysis.” (L. 313-317)
Comment:
248 Why did the density differ? Were the crops planted at different densities, or does this variability relate to mortality, or some other reason?
Response:
The differences in plant density were mainly due to soil, moisture and topographic conditions.
Comment:
438 You could also discuss the direct relationship between particle size and nutrient holding capacity in terms of the exchange complex (clays and silts having higher surface area than sands and consequently more nutrient holding capacity). There is a large amount of literature on this and it should be touched on. Any intro soil fertility textbook will give you good information.
Response:
Added (568-571)
“The size and surface area of soil particles are very important because they are the place where chemical and biochemical processes occur that affect soil properties such as fertilizer, water retention, hydraulic conductivity, soil structural stability and microbial abundance”.
Hu FN., Xu C., Li H., Li S., Yu Z., Li Y., He. X, 2015 Particles interaction forces and their effects on soil aggregates breakdown Soil Till. Res. 147 1–9
Comment:
463 Table 4 shows the maximum plant density upwards of 500 plants per m2. Please explain what the target planting density was and why the actual measured plant density could be higher than the planned planting density. Does this indicate a failure of planting the right number of crops, or how did it happen? Presumably the planter is calibrated to provide the target number of plants per acre. How and when would it be exceeded?
Response:
Text added in lines 327-330
“In field conditions it is very difficult to maintain the assumed crop density parameters. Terrain was not flat (differences in elevation up to 12 m), different operating speeds, and the quality of seed material had an effect on the obtained oat plant density.”
Comment:
517 "it is strongly recommended to use more" Such as? Please explain and be explicit about what more is needed.
Response:
Text added in lines 656-660:
“In agricultural practice, it is worth using a combination of remote sensing data with field measurements (e.g. soil sampling and plants analysis), computer modeling or monitoring of weather conditions. Only such approach allows for a more complete and effective assessment of the plants condition and planning of agrotechnical activities, including fertilization, which leads to better use of resources and sustainable soil management.”
Comment:
What taxonomic information is available about the geologic and pedological development and classification of the soils? What are their taxonomic classifications and characterizations? What gradients in soil development and/or taxonomy does the site have? Please address the natural soils and their variability from a taxonomic standpoint. At the very least, describe their classification in the methods section.
Response:
New information was added (Lines 111-117)
“The study site is located at the transition between post-glacial denuded moraine (Pleistocene), presently recognisable as undulated lowland, and the higher terraces (Pleistocene) of Odra river. However, both landform units were dissected with numerous local streams, presently drained or channeled, which left the (Holocene) alluvial sediments, covering irregularly the older ones. As a result, the lithology of sediments noticeably differs across the experimental field, which results in contrasting soil cover.”
Author Response File: Author Response.docx
Round 2
Reviewer 1 Report
Comments and Suggestions for Authors1、 In the abstract, there was no data support, and the description of the conclusion was not targeted.
2、 The description of the findings is too broad. Research conclusions must be closely linked to research objectives. Common sense knowledge is not the result of research.
3、 Research conclusions must be derived from results, with clear indicators.
4、 Research objectives and conclusions must have certainty, otherwise there is no application value.
5、 How does the vegetation index reflect the early stages of crop development? Do they reflect one overall development or several indicators?
6、 How to apply vegetation index to fertilizer management, whether vegetation index threshold values indicate different crop development condition?
7、 From fig. 5 to fig. 14, only one correlation graph with significance index needs to be retained for each graph.
8、 The summary and conclusion must be improved.
Author Response
06 February 2025
General comment:
We thank the Reviewers for valuable review of our manuscript and important comments which helped to improve our study and allowing us to change the presentation of our research data. Manuscript was deeply corrected. We have considered all comments carefully and provided explanations below.
Responses to Reviewer 1
Comment:
1、 In the abstract, there was no data support, and the description of the conclusion was not targeted.
Response:
Abstract was reorganise. New information was added (below)
At BBCH 12 growing stages, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI and EVI, which indicates their usefulness for monitoring crop emergency and early development. At early growing phase (BBCH 31), the GNDVI index is particularly valuable for predicting final nitrogen uptake and oats biomass yield. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for fertilization management at this critical stage.
Comment:
2、 The description of the findings is too broad. Research conclusions must be closely linked to research objectives. Common sense knowledge is not the result of research.
Response:
The results has been rewritten. A significant part of the text from the previous version has been removed and new ones have been added (lines 479-602 and 625-761)
Comment:
3、 Research conclusions must be derived from results, with clear indicators.
Response:
Research conclusion has been rewritten. Some part of the text from the previous version has been delate and new ones have been added (lines 1007-1016, 1024-1026, 1029-1030)
Comment:
4、 Research objectives and conclusions must have certainty, otherwise there is no application value.
New information added in conclusion chapter.
During the early growth phase (BBCH 31), the GNDVI index is particularly valuable for predicting final nitrogen uptake and biomass yield. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for targeted fer-tilization strategies at this critical stage.
The research confirmed that remote sensing, and particularly the GNDVI index, is a valuable tool for supporting fertilization management on light soils with high spatial variability. However, it is essential to use more than just remote sensing indices to assess the nutritional status and productivity of cultivated crops.
Comment:
5、 How does the vegetation index reflect the early stages of crop development? Do they reflect one overall development or several indicators?
New information added (Lines 660-666)
Among the evaluated indices, GNDVI at BBCH 31 demonstrated a significant correlation with final nitrogen uptake by the crop (r=0.44, p<0.01) and biomass yield (r=0.39, p=0.01). This distinguishes it from other indices, whose correlations with these parameters were weaker or statistically insignificant, as observed in the cases of MCARI and NDMI. Given its stronger and statistically significant relationships with key agronomic parameters, GNDVI appears to be the most effective predictive index for assessing nitrogen uptake by the total crop and biomass ac-cumulation during BBCH 31 stage.
Comment:
6、 How to apply vegetation index to fertilizer management, whether vegetation index threshold values indicate different crop development condition?
Response:
Nitrogen uptake by oats was dependent on the NDVI index in the BBCH 31 phase which is crucial for agronomic decision, particularly for nitrogen fertilization. Figure 9 added. Regression equation can be the basis for developing a model of second nitrogen fertilization doses.
Comment:
7、 From fig. 5 to fig. 14, only one correlation graph with significance index needs to be retained for each graph.
Response:
Figures 5-14 were deleted and new ones added (Figure 4-9) to clearly show significant correlations.
Comment:
8、 The summary and conclusion must be improved.
Response:
Both chapters were deeply corrected and improved.
Author Response File: 
Author Response.pdf
Round 3
Reviewer 1 Report
Comments and Suggestions for Authors- In the abstract, "in the growth period of bbch12, NDVI, SAVI, GNDVI and EVI had the highest correlation with plant density", such a conclusive description needs to be supported by data, such as "in the growth period of bbch12, NDVI, SAVI, GNDVI and EVI had the highest correlation with plant density. The correlation coefficients were....... ".
- In the abstract, "At the early growth stage (BBCH 34 31), GNDVI was significantly correlated with the final nitrogen uptake and biomass of oat, and the correlation coefficient reached... (P<0.01), indicating that GNDVI index is particularly valuable for predicting final nitrogen uptake and biomass yield of oat."
- The unclear text in Figures 6 and 9 needs to be rewritten.
Author Response
20 February 2025
General comment:
Thank to the Reviewer for comments of our manuscript. We have considered all comments carefully and provided explanations below.
Responses to Reviewer 1
Comments
In the abstract, "in the growth period of bbch12, NDVI, SAVI, GNDVI and EVI had the highest correlation with plant density", such a conclusive description needs to be supported by data, such as "in the growth period of bbch12, NDVI, SAVI, GNDVI and EVI had the highest correlation with plant density. The correlation coefficients were....... ".
Response
Added new information in abstract:
“At BBCH 12 growing stage, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI and EVI. The correlation coefficients ranged from 0.38 to 0.56, with a significance level of ≤ 0.01 which indicates their usefulness for monitoring crop emergency and early development.
Comments
In the abstract, "At the early growth stage (BBCH 34 31), GNDVI was significantly correlated with the final nitrogen uptake and biomass of oat, and the correlation coefficient reached... (P<0.01), indicating that GNDVI index is particularly valuable for predicting final nitrogen uptake and biomass yield of oat."
Response
Added new information in abstract:
At early growing stage (BBCH 31-34), GNDVI was significantly correlated with the final nitrogen uptake (r=0.44, p<0.01) and biomass yield of oat (r=0.39, p=0,01). This suggests that the GNDVI index is particularly useful for predicting the final nitrogen uptake and biomass yield of oat.
Comments
The unclear text in Figures 6 and 9 needs to be rewritten.
Response
Improved readability of Figs. 6 and 8. Our opinion text in Fig. 9 is clear.
Author Response File: Author Response.pdf
