Contribution of Biophysical Factors to Regional Variations of Evapotranspiration and Seasonal Cooling Effects in Paddy Rice in South Korea

Round 1

Reviewer 1 Report

In this paper, the RS-PM model with MODIS datasets was applied to estimate the 110 ET of paddy rice fields in entire South Korea from 2011 to 2014. Fused data was used to evaluate the three hypotheses. This topic is very interesting. But I didn’t understand what this research want to convey to readers after reading the introduction. To test the function of paddy rice in climate change mitigation? The hypothesis shouldn’t be proposed in the middle of the introduction. If it appears at the end of the introduction, it will be reasonable. At least there are some concerns for this MS 1. In the abstract, the description of the method is lack but necessary. 2. Maybe this sentence “We found marginal contributions of air temperature to daily ET, at least not as 52 important as the incident solar radiation” in the Introduction is improper. It should be your conclusion. 2. The method that you used to classify the paddy rice is not convincing. You mentioned that you “detected by the flooding single classification approach”. Where are the detailed description and the process of how to achieve that？ Then the accuracy of your paddy rice map should be reported. 3. In Figure 2, I don’t this path diagram is meaningful. Because the PET and actual ET must be related. And the ET is calculated with VPD, T, and LAI. If you want to analyze the relationship between two variables, they should be derived from different sources. In another world, the Correlation coefficient between LGP and ET is reasonable. But the correlation coefficient between ET and T is useless. If you want to analyze the relationship between ET and T, the sensitive analysis of T in RS-PM is acceptable. Specific comments; 1. lack of reference in L122. 2. Table 2 can be displayed as a scatter figure. 3. in section 3.1: Spatial variations of ET in paddy fields, South Korea. A map is necessary to display the spatial distribution of ET in paddy fields 4. what do you want to say with Spatial variations of paddy in table 3? Maps could depict this information better.

Author Response

Responds to the reviewer’s comments:

Reviewer 1#

In this paper, the RS-PM model with MODIS datasets was applied to estimate the ET of paddy rice fields in entire South Korea from 2011 to 2014. Fused data was used to evaluate the three hypotheses. This topic is very interesting. But I didn’t understand what this research want to convey to readers after reading the introduction. To test the function of paddy rice in climate change mitigation? The hypothesis shouldn’t be proposed in the middle of the introduction. If it appears at the end of the introduction, it will be reasonable.

Reply: We agree with your comments. Major revisions have been made in Introduction part as follows:

The first paragraph states why it is necessary to carry out this research, namely disentangling biophysical factors responsible for spatial and temporal variations of paddy field ET and seasonal fluctuations of monthly air temperature in paddy fields. The second paragraph summaries the current progress in association with temporal variations of paddy field ET and consequently propose our first hypothesis “Field-to-field changes in the FFTD could become an important biophysical factor influencing spatial and temporal variations of ET among paddy fields”. The third paragraph summaries the current disputes on climate change mitigation of paddy rice fields, consequently proposed our second hypothesis “Considering the temperate forest being adjecant to paddy rice planting areas, the monthly T_s of paddy fields may not always be higher than that of the temperate forest”. The fourth paragraph briefly shows the methods used for ET estimation in paddy rice fields in South Korea from 2011 to 2014, and datasets used to quantify spatial variations of paddy rice ET and their climate change mitigation effects.

Importantly, we moved the two hypotheses to the end of the Introduction. The revised Introduction contains 916 words.

Please kindly check new revisions. And let me know if you have more questions without any hesitations.

 

At least there are some concerns for this MS

1. In the abstract, the description of the method is lack but necessary.

Reply: A brief description of the method used for ET estimation was added. Please kindly check the revisions. Please let me know whether or not the revisions meet your requirements.

 

2. Maybe this sentence “We found marginal contributions of air temperature to daily ET, at least not as important as the incident solar radiation” in the Introduction is improper. It should be your conclusion.

Reply: I am sorry for the typing error in that sentence. It should be “They found marginal contributions of air temperature to daily ET, at least not as important as the incident solar radiation”. A revision has been made in Line 74 in the clean version.

 

3. The method that you used to classify the paddy rice is not convincing. You mentioned that you “detected by the flooding single classification approach”. Where are the detailed description and the process of how to achieve that？Then the accuracy of your paddy rice map should be reported.

Reply: The flooding single classification approach has been described in Lines 168-170 in the clean version, as follows: “Xiao et al. [29] proposed a flooding single classification method (LSWI + T ≥ EVI, T is a threshold parameter) with a constant T of 0.05 to identify paddy rice areas and evaluate the FFTD_sat [33]. They designated the first date of the EVI 8-day period meeting the flooding single classification approach at a paddy pixel as the FFTD_sat of the paddy pixel.”

The accuracy of the flooding single classification approach was described in Lines 531-533 in clean version, as follows: “Paddy rice areas per county quantified by the flooding single classification method had a strong correlation with national census data with R² of up to 0.85 (Figure S11 in Supporting Information SP8).”

To obtain the pure paddy rice pixels throughout South Korea, we used a 5-m national census map of land use and land cover to filter potential paddy rice map derived by the flooding single classification approach, as follows: “Paddy pixels, obtained by the flooding single classification method, were denoted as potential paddy pixels, as they may still contain numerous small landholdings of subpixels. We assumed that the land cover and land use type (agriculture, forest, and grassland) throughout South Korea were relatively stable from 2011 to 2014 (Figure S2). A spatial information system embedded in the ArcGIS 10.2 software was used to stack the 5-m land cover map and the map of potential paddy pixels, aiming to count the number of paddy rice fields within each potential paddy pixel. The 5-m spatial resolution land cover map of the raster form was developed by the Ministry of the Environment Republic of Korea through the comprehensive county field census and remote sensing retrieval by visual interpretation (For detailed information, please refer to Jeong et al., 2012 [18] and Figure S1). The Figure S3 in Supporting Information SP1 shows an example of the selection of homogenous paddy pixels in the Charyeong rice plain. Potential paddy pixels with homogeneity of ≥ 90% throughout South Korea were regarded as pure paddy pixels.” Please kindly check the Lines 171-182 in the clean version.

 

4. In Figure 2, I don’t this path diagram is meaningful. Because the PET and actual ET must be related. And the ET is calculated with VPD, T, and LAI. If you want to analyze the relationship between two variables, they should be derived from different sources. In another world, the Correlation coefficient between LGP and ET is reasonable. But the correlation coefficient between ET and T is useless. If you want to analyze the relationship between ET and T, the sensitive analysis of T in RS-PM is acceptable.

Reply: I understand your comments well. Please let me make explanations in detail as follows: More than four meteorological variables and crop growth variables are used as input parameters in the pixel-based RS-PM model. Those input parameters and phenological traits used for correlation analysis were derived independently. Which parameter plays important roles in ET determination of paddy field throughout South Korea strongly depends on geographical variations of the input parameter. For example, for temporal variations of daily ET in paddy field, the most important factor in daily ET determination is dRn (daily net radiation), followed by dVPD and dTair as shown in Figure 2, which is similar to previous reports. Although dRn, dVPD and dTair are input parameters of the pixel-based RS-PM model, the relative contributions to temporal variations of paddy rice ET found in Figure 2 are also supported by correlation analysis according to the eddy covariance observations. The important role of incident solar radiation in determination of daily ET was not found for regional variations of the RGS-ET (the sum of daily ET from the SoS to the EGS), because geographical variations of the incident solar radiation are not greater than other meteorological factors, such as VPD and Tair, throughout the whole South Korea.

Additionally, it is impossible to install hundreds of eddy covariance system throughout the whole South Korea to independently collect ET observations in paddy rice systems. In order to disentangle the relative importance of biophysical factors to regional variations of ET, building the correlation between the biophysical factors and the estimated ET seems indispensible, as commonly seen in regional research of ET patterns (Liu et al., 2007; Zhang et al. 2010; Yang et al., 2011; Yao et al., 2014; Feng et al., 2017).

Liu CS, Zhang WC, Zhao DZ, Gao YN. Remotely-sensed evapotranspiration of typical oasis in the southern edge of tarim basin and its relationship to land cover changes. IEEE International Geoscience and Remote Sensing Symposium, 2007, 1-12: 3237–3240.

Zhang SW, Lei YP, Li HJ, Wang Z. Temporal-spatial variation in crop evapotranspiration in Hebei Plain, China. Journal of Food Agriculture & Environment, 2010, 8, 672–677.

Yang ZF, Liu Q, Cui BS. Spatial distribution and temporal variation of reference evapotranspiration during 1961-2006 in the Yellow River Basin, China. Hydrological Sciences Journal-Journal Des Sciences Hydrologiques, 2011, 56, 1015–1026.

Yao YJ, Zhao SH, Zhang YH, Jia K, Liu M. Spatial and decadal variations in potential evapotranspiration of China based on reanalysis datasets during 1982-2010. Atmosphere, 2014, 5, 737–754.

Feng Y, Cui NB, Zhao L, Gong DZ, Zhang KD. Spatiotemporal variation of reference evapotranspiration during 1954-2013 in Southwest China. Quaternary International, 2017, 441, 129–139.

There is another way to independently quantify ET, i.e. the water balance model. Knowing the inputs from rainfalls and inlet water amount per pre-defined time period and the outlet water amount, water retention in soils and percolation during the same time period could generate ET. However, the calculated ET is commonly made at monthly scale not daily scale. It may not be useful for our correlation analysis.

Specific comments:

1. lack of reference in L122.

Reply: A citation has been inserted in Line 105 in the clean version. Please kindly check it. 

 

2. Table 2 can be displayed as a scatter figure.

Reply: Yes. We have convert Table 2 into figure 2. It look better for understanding. Please kindly check it in the clean version.

 

3. in section 3.1: Spatial variations of ET in paddy fields, South Korea. A map is necessary to display the spatial distribution of ET in paddy fields

Reply: I agree. A map to display the spatial distribution of ET (i.e. the PF-ET and RGS-ET) in paddy fields was added in Figure 4. Please kindly check them.

 

4. what do you want to say with Spatial variations of paddy in table 3? Maps could depict this information better.

Reply: I agree. A map to display the spatial distribution of ET (i.e. the PF-ET and RGS-ET) in paddy fields was added in Figure 4. Please kindly check them. And please let me know whether or not the quality of spatial map is high enough for better readability, without ant hesitations. Thanks again for your sincere comments.

 

Author Response File: Author Response.docx

Reviewer 2 Report

General comments

This manuscript calculates the biophysical factor’s contribution to ET variations and their cooling impacts in paddy rice in S-Korea -- by Wei Xue et al. The paper is generally well-written. The introduction is well-structured with a good story-line explaining the three hypotheses of the research. It is also informative, simple, and well sustained. Good bibliography was cited. The research methods are adequate and thoroughly explained, and the results analysis is sound. Conclusions are good and concise also. However, the use of graphics is pretty poor and I have some suggestions to improve them. Sometimes the writing style is a little odd, with occasional unnecessarily long complex statements. I recommend converting those to simple declarative sentences.  Additionally, I have some minor comments, see below. I hope that these are helpful for the authors. I look forward to reading the revised version.

 

Minor comments

Abstract: VPD and FFTDsat need to be fully described. And, there are too much jargons. I do recommend try to avoid them: such as RGS-ET, EGS, SoS, LGP, gsVPD, FFTDsat, gsLAImax, etc., and make the abstract as simple and understandable as possible. Also, methods which is a modeling approach, the datasets used (satellite, in-situ, etc.,) and the time-period, etc., are not mentioned in the abstract at all -- these need to briefly described.

Keywords: it would be good to include a term like ‘’numerical simulation/modelling approach’’, as this paper is also a numerical-simulation approach.

Line 134: what is TIMESAT? For the first time in the MS, try to make it clear.

Line 188: here you may need to explain this TIMESAT program with a bit more details to make it clear for non-experts.

Fig.1: the term ’meteo’ is commonly used. What are the variable’s units? It is better to write them in the caption.

Line 160: can you add the link here to access to DEM data?

Line 231: same as above, try to explain the RS-PM model -or FAO-56 PM model with details. What type of model is that: e.g. distributed physically-based model? Lumped 1D model? What are the inputs? etc., Also, both RS-PM model and FAO-56 PM model are used, what are the differences?

Fig. 3. Can you improve the figure? For example: 145 FFTD is totally invisible, you may use a different color to draw it?  the subpanels b and d can also have y-axis values. X-axis numbers can be smaller, -or shown in a better date-format (e.g. day.month)

Fig. 4. Panels 2011-2014 maps are very busy and small also, so details (flooding dates) are not clear. I suggest to enlarge them as far as possible, also remove elevation colors (if it is needed, you may add a new elevation-map in Supporting Information). Colors used for rivers + some flooding dates are merged making it difficult to distinguish the differences.

Author Response

Reviewer 2#

General comments

This manuscript calculates the biophysical factor’s contribution to ET variations and their cooling impacts in paddy rice in S-Korea -- by Wei Xue et al. The paper is generally well-written. The introduction is well-structured with a good story-line explaining the three hypotheses of the research. It is also informative, simple, and well sustained. Good bibliography was cited. The research methods are adequate and thoroughly explained, and the results analysis is sound. Conclusions are good and concise also. However, the use of graphics is pretty poor and I have some suggestions to improve them. Sometimes the writing style is a little odd, with occasional unnecessarily long complex statements. I recommend converting those to simple declarative sentences. Additionally, I have some minor comments, see below. I hope that these are helpful for the authors. I look forward to reading the revised version.

Reply: Thanks a lot for your sincere comments. We have made revisions in the Introduction for better readability. The long sentences that are hard to understand have been changed into simple declarative sentences. For example, lines 85-87, 94-95, 103-105, 106-119, 188-191, 230-232 in the clean version.

 

Minor comments

Abstract: VPD and FFTDsat need to be fully described. And, there are too much jargons. I do recommend try to avoid them: such as RGS-ET, EGS, SoS, LGP, gsVPD, FFTDsat, gsLAImax, etc., and make the abstract as simple and understandable as possible. Also, methods which is a modeling approach, the datasets used (satellite, in-situ, etc.,) and the time-period, etc., are not mentioned in the abstract at all -- these need to briefly described.

Reply: We made changes in Abstract to be more concise and understandable as possible through minimizing the amounts of abbreviations and jargons. A brief statements of the modeling method used in our study was added in Abstract. The datasets used in terms of MODIS 8-day surface reflectance products and gridded daily climate data of ground surface have been involved by the brief statements.

 

Keywords: it would be good to include a term like ‘’numerical simulation/modelling approach’’, as this paper is also a numerical-simulation approach.

Reply: The term “numerical simulation” has been added in Keywords. And all terms have been arranged in alphabetical order.

 

Line 134: what is TIMESAT? For the first time in the MS, try to make it clear.

Reply: Yes, we inserted an explanation of TIMESAT where it firstly appeared. Please kindly check the Lines 134-135 in the clean version.

 

Line 188: here you may need to explain this TIMESAT program with a bit more details to make it clear for non-experts.

Reply: Yes, we inserted an explanation of TIMESAT where it firstly appeared, and more detailed information about TIMESAT was added in Lines 188-191 in the clean version.

 

Fig.1: the term ’meteo’ is commonly used. What are the variable’s units? It is better to write them in the caption.

Reply: The right abbreviation “meteo” was used in Figure 1. Units of variables were added in the caption.

 

Line 160: can you add the link here to access to DEM data?

Reply: A link to access the DEM data has been cited in Line 165 in the clean version.

 

Line 231: same as above, try to explain the RS-PM model -or FAO-56 PM model with details. What type of model is that: e.g. distributed physically-based model? Lumped 1D model? What are the inputs? etc., Also, both RS-PM model and FAO-56 PM model are used, what are the differences?

Reply: A cleat sentence “In this study, we adopted a data fusion method that integrated MODIS 8-day surface reflectance products, gridded daily climate data of ground surface, and a remote sensing pixel-based Penman-Monteith ET model (i.e. the RS–PM model ) to quantify ET patterns of paddy rice in South Korea from 2011 to 2014” has been added in Abstract. Additionally, the detailed descriptions about the pixel-based RS-PM model were stated in Lines 105-108 and Lines 230-238 in the clean version.

 

Fig. 3. Can you improve the figure? For example: 145 FFTD is totally invisible, you may use a different color to draw it?  the subpanels b and d can also have y-axis values. X-axis numbers can be smaller, -or shown in a better date-format (e.g. day.month)

Reply: Yes, the quality has been improved through using red colors for the 145 FFTD paddy fields. Other minor changes in terms of font size of x and y axis have been made. Please kindly check the Figure 5 in the clean version.

 

Fig. 4. Panels 2011-2014 maps are very busy and small also, so details (flooding dates) are not clear. I suggest to enlarge them as far as possible, also remove elevation colors (if it is needed, you may add a new elevation-map in Supporting Information). Colors used for rivers + some flooding dates are merged making it difficult to distinguish the differences.

Reply: Yes, the background information in terms of DEM and rivers distribution has been removed. Please let me know whether or not the quality of spatial map is high enough for better readability, without ant hesitations. Thanks again for your sincere comments.

 

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors addressed most of the comments. I suggest accepting in the present format.