Integrated Application of Remote Sensing and GIS in Crop Information System—A Case Study on Aman Rice Production Forecasting Using MODIS-NDVI in Bangladesh
Round 1
Reviewer 1 Report
The paper is interesting and inherent in the journal's objectives.
However, it would be better to improve the introduction by inserting more bibliographic references on Remote Sensing and GIS.
Conclusions need to be more closely related to results.
A revision of the English language is required.
Author Response
Response to Reviewer 1 Comments
Point 1: The paper is interesting and inherent in the journal's objectives.
Response 1: Yes, thanks for your comments.
Point 2: However, it would be better to improve the introduction by inserting more bibliographic references on Remote Sensing and GIS.
Response 2: According to your suggestion, we have added following lines at Introduction section with relevant references “However, the MODIS constellations have been used in retrieving agricultural crop information and it is mostly used due to its larger regional scale, smaller dataset and faster revisiting time [16, 17]. Moreover, the dynamics of MODIS derived NDVI products is representative of crop growth and biomass changes which is closely related to crop yield and has direct relationship with Leaf Area Index (LAI), biomass and vegetation cover [18-20]. The suitability of using MODIS derived NDVI data for crop yield estimation prediction, crop production and monitoring has been recommended by several studies [21-24]. Therefore, the MODIS derived NDVI product is used in the present study.”
Point 3: Conclusions need to be more closely related to results.
Response 3: According to your suggestion, the conclusion section has been modified and following lines have been added “This methodological framework directly calculates the district-wise rice production statistics based on the pixel-by-pixel NDVI summation. A strong correlation is found between the district wise pixel-based summation of MODIS-NDVI and ground-based BBS estimated Aman production.”.
Point 4: A revision of the English language is required.
Response 4: The English language has been revised at some cases as per your suggestion.
Author Response File: 
Author Response.docx
Reviewer 2 Report
The paper presents a predictive model for rice cultivation in Bangladesh. It does not contain original scientific knowledge, but it is written comprehensively and has all the elements typical of a case study. I appreciate the simulation method based on observation of vegetation indices obtained from satellite images of the modeled area.
I have these questions about this article:
- In what SW was the simulation model created? Was it your own application (in which language) or as a module in a commercial SW (eg ArcGIS)?
- Is the model generally applicable and under what conditions?
- What further development of the model is expected?
Author Response
Response to Reviewer 2 Comments
Point 1: The paper presents a predictive model for rice cultivation in Bangladesh. It does not contain original scientific knowledge, but it is written comprehensively and has all the elements typical of a case study. I appreciate the simulation method based on observation of vegetation indices obtained from satellite images of the modeled area.
Response 1: Thanks for your appreciation and comments on the manuscript.
Point 2: In what SW was the simulation model created? Was it your own application (in which language) or as a module in a commercial SW (eg ArcGIS)?
Response 2: We used Erdas Image software and ArcGIS software to retrieve the crop information.
Point 3: Is the model generally applicable and under what conditions?
Response 3: Yes, this model is applicable in general condition but it does not consider the pest invasion factor in the crop field.
Point 4: What further development of the model is expected?
Response 4: The model need more ground truth validation and need to consider some climatic factors that have impacts on the crop management in this region. We are writing for a project (fund) to conduct some field validation. We have also mentioned about this in our manuscript.
Author Response File: Author Response.docx
Reviewer 3 Report
The manuscript describes the use of remote sensing data for forecasting rice crop in Bangladesh.
Generally, the paper is written well. However, some writing mistakes (e.g. line 60) should be corrected.
The originality of the paper is not mentioned and the method used is too simple.
Line 14, R-square value of
The use of GIS is very minor in this research, and therefore, does not support the paper title. This is more about comparing MODIS-NDVI data with available ground data.
Since MODIS provides low resolution dataset, the mixed-pixel issue which leads to biased results was not considered. This should be taken into account for crop estimation.
Section 2.4 needs more detailed descriptions to support Figure 2.
In addition, more graphical supports such as figures showing the RS images in different dates are necessary.
In conclusion, “Nevertheless the developed models do not consider the pest invasion and natural disasters.” Since the model ignored two important factors in food security, how does it play a key role in national food security?
Author Response
Response to Reviewer 3 Comments
Point 1: The manuscript describes the use of remote sensing data for forecasting rice crop in Bangladesh.
Response 1: yes, thanks for your comments.
Point 2: Generally, the paper is written well. However, some writing mistakes (e.g. line 60) should be corrected.
Response 2: It has been revised.
Point 3: Line 14, R-square value of
Response 3: The Line 14 (The regression study between district wise pixel-based summation of MODIS-NDVI and ground-based BBS estimated Aman production shows a strong correlation (R2=0.54–0.78)) has been revised as per your suggestion.
Point 4: The use of GIS is very minor in this research, and therefore, does not support the paper title. This is more about comparing MODIS-NDVI data with available ground data.
Response 4: The vegetation layer has been generated and updated with the help of GIS data which is one of the major components of this study. We extract the district wise statistics with the GIS boundary layer. Besides we prepared maps using vector and raster data in ArcGIS.
We compared the MODIS-NDVI data with available ground based data but using this methodology we can now extract the country scale crop production statistics quickly based on the remote sensing techniques quickly which is very much important.
Point 5: Since MODIS provides low resolution dataset, the mixed-pixel issue which leads to biased results was not considered. This should be taken into account for crop estimation.
Response 5: yes, we considered the mixed pixel in crop estimation. In our methodology section we mentioned that country scale vegetation mask layer has been used to extract the rice pixel only.
Now, according to your comments we have added following lines in the methodology section “The country scale vegetation mask layer consists of (i) forest features, (ii) homestead vegetation together with (iii) seasonal crops, and (iv) mostly non-vegetated soil areas. This vegetation mask layers have been generated from high-resolution satellite data (RapidEye/Landsat) and has been utilized to find the district-wise rice pixel only in present study. The regular updating of these vegetation layers in every 3-4 years can give satisfactory updated surface feature at country scale. The detail procedure of preparing the vegetation mask layer has been stated in [24]. The Pixel-wise spatial summation of NDVI values for all the individual pixels covering each individual district area in the MVC NDVI image provided a single value for each district. The detail procedure of updating vegetation mask layer properties has been stated in [24] which is one of my published articles. Because of this in our present manuscript we provide the reference in methodology section.
Point 6: Section 2.4 needs more detailed descriptions to support Figure 2.
Response 6: We revised the section 2.4 according to your suggestion and added some description to support the Figure 2 (Now it has become Figure 3).
Point 7: In addition, more graphical supports such as figures showing the RS images in different dates are necessary.
Response 7: According to your suggestion, we have added figures showing the RS images in different dates (RS images of 2012) in Figure 2 as a sample case. Besides Table A1 summarize the acquisition date of remote sensing images from 2011-2017.
Point 8: In conclusion, “Nevertheless the developed models do not consider the pest invasion and natural disasters.” Since the model ignored two important factors in food security, how does it play a key role in national food security?
Response 8: The national statistical organization needs reliable and timely crop information but at present they have to rely on the field based information which is not available at the right time. Besides they do not have remote sensing based statistics to make any decision related to food security. The forecasting or prediction of the amount of rice production prior to the end of growing season is crucial in order to ensure food security issues of a country because the rice production statistics can help governments, planners, and decision-makers to formulate appropriate policies in rice importing in the event of shortfall or exporting in the event of surplus as well as purchasing rice earlier at comparatively cheaper rates if other rice-producing countries do not have information about forthcoming need. Thus the earlier forecasting of the Aman crop based on this research can have significant roles on national food security issues. Thus this method play key role in the national food security issues in the context of our country.
Author Response File: Author Response.docx
Round 2
Reviewer 3 Report
The authors revised the paper but the given comments in the review report were not responded.
Round 3
Reviewer 3 Report
Now, the paper is fine.
