VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data
, Mariana de Jesús Marcial-Pablo 1, Waldo Ojeda-Bustamante 2, Ernesto Sifuentes-Ibarra 3, Marco Antonio Inzunza-Ibarra 1 and Ignacio Sánchez-Cohen 1,*
Round 1
Reviewer 1 Report
General:
Congratulations for the actual topic, instruments, and results!
The manuscript brings important contributions in use of remote sensing for agricultural purposes, especially for researchers with poor access to satellite data/institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets and high-performance data processing.
However, there are some issues that must be addressed to improve the overall communication of author`s work.
The methodology and conclusions can be improved, some parts could be synchronized.
Introduction
Row 60-64, The opinions are disputable according with others UAV actual possibilities
Row 65-68, please, references!
Please synchronize the goal from introduction (rows 81-96), with conclusions, title and methodology!
Methodology
Row 109-110, Why only Sentinel-2 has spectral bands similar to Landsat 8 (please, justify the focus only on L8)?
Please, take care that figure 1 mainly shows the relationship between Atmospheric Transmission, [%], and Wavelength, [nm]. It is necessary to correct the figure 1 title according with mainly objective of the figure (rows 108-109 and 137-140).
The use of different cloud cover filters algorithms for Landsat and Sentinel -2 images ( CFMask and s2cloudless), did not affect the results of comparison? Can it explain the atypical imagines mentioned in results and discussions? (Figure 9, row 361-365)
Also, keep in mind that the readers should find here (only and exclusively) detailed description of all your procedures and method (author, process conditions) used. Anybody must be able to repeat your methods and obtain the same results.
Results
Row 337, reference is presented in different form, please, correct it.
Must be presented concordant with research methodology.
Please, be more intensely in compare your findings with existing literature and other results.
Conclusions
Please, corelate with title, goal, methodology and results!
Rows 375-383 seems to be dedicated to GEE platform, not VICAL, I think must be reformulated in accordance.
Author Response
The authors would like to thank the Reviewer for their comments that enrich and make the manuscript more detailed.
To improve the manuscript, your observations and suggestions were taken into account.
Please, check the attached manuscript
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data
This paper presents an innovative approach to extracting vegetation indices (VI) using Google Earth Engine (GEE). Today, low, medium and high resolution remotely sensed data such as Landsat and Sentinel-2 are available at high temporal resolution. However, processing and abstracting meaningful information takes hours and days. This innovative approach attempts to eliminate the woes of searching, downloading, processing, radiometric correcting, and analyzing voluminous remotely sensed data to extract vegetation indices (VI) and assess the ground productivity. It:
a. uses Java programming codes within the platform of publicly available Google Earth Engine
b. utilizes algorithms that help in the quantitative and qualitative evaluations of the dynamics of biophysical crops along with the possibilities of detecting phenological and physiological behaviors of plants
c. alleviates the problems of validating and enhancing vegetation signals by using remotely sensed images on which atmospheric noises such as brightness, shadows, and color anomalies are already corrected /suppressed
d. develops vegetation indices calculator (VICAL) using the reflectance that is applicable to calculate any agricultural area and estimate production per unit area in any part of the world.
e. combines remotely sensed images taken for the same location that have high, medium, and low reflectance values to find out the average of all these reflectance values by minimizing the cloud effects over any area to derive the vegetation indices.
f. uses GEE calculator and visualizes VIs associated with spatial and temporal variations of several biophysical crop variables in any location in the world with minimal data requirements.
This innovative approach is very beneficial for researchers who are constrained to access, download, store, and process, voluminous satellite data due to a variety of reasons, the most important one being the institutional inabilities to have the computational facilities. It also helps to develop and enhance the freely available VICAL graphical user interface (https://inifapcenidraspa.users.earth-engine.app/view/vical) to enable the rapid computation of multiseasonal and multispectral images to extract VIs.
The paper is well written, however, to publish it, the following areas need the authors’ attention.
Page 2 line 56: adquired??
Page 2, lines 79-85: Reorganize for clarity.
Page 4: lines 149-152: run-on sentences.
Page 8: line 171—habilities???
Page 9, line 188 by [86] wasà by Laipelt et al.???
Page 11, line 253, it might be worthwhile providing VICAL procedures briefly in a stepwise manner.
Please improve the abstract and concluding remarks with the addition of research findings.
Author Response
The authors would like to thank the Reviewer for their comments that enrich and make the manuscript more detailed.
To improve the manuscript, your observations and suggestions were taken into account.
Please, check the attached manuscript
Author Response File: Author Response.pdf
