Analysis Ready Data of the Chinese GaoFen Satellite Data

Round 1

Reviewer 1 Report

The paper is well-written and has potential to be published in Remote sensing. 

Some minor corrections must be applied before publication. 

Please find the attached file of annotated PDF.

Comments for author File: Comments.pdf

Author Response

Thank you very much for the time you spent reviewing our manuscript and for approving our job. 

The new version has been updated according to your suggestions.

Reviewer 2 Report

1) This paper mainly talks about the formation of database which alone is not sufficient for a scientific manuscript.

2) No specific applications on remote sensing is available in this paper which is actually the focal point of the journal.

3) The block diagram shown in the figure includes very conventional steps. However, the authors must note that few blocks may need shuffling based on the nature of application irrespective of the data

4) The authors are encouraged to pay more concentration on the results section of the manuscript.

5) It is also very difficult to understand the scientific contribution in the paper.

Author Response

Thank you very much for reviewing our manuscript and for approving our job. Thank you very much for your constructive suggestions. Here is our reply.

This paper not only talks about the formation of database. More importantly, it proposed a data processing method framework specifically for Chinese domestic satellites. In recent years, China has launched many satellites, and collected huge data. However, there is still no complete processing framework so far, so the application of satellite data is limited. When every researcher wants to use these data, they should start with data preprocessing, which leads to repetitive work and a lot of time wasted; furthermore, the processed data are not well qualified for further applications at most of the cases. Take Landsat TM/ETM+ and Sentinel-2/MSI for example, researchers can download data of different levels and regions according to their needs instead of preprocessing the data themselves. This is the real meaning of the Analysis Ready Data (ARD).

In this framework (Figure 2), conventional steps are not used conventional methods, geometric normalization, radiometric normalization, and atmospheric correction are performed using new methods or procedures, which have been published in academic journals (see references [13-16, 21, 26]) with innovative and practical ideas and are indispensable parts of the framework. These methods and the ideas behind are the major scientific contributions and they are all summarized in this article.

In fact, more than 10 biophysical and geophysical products have been produced using these ARD data proposed in this paper based on the fund National Key R&D Program of China, like vegetation index (NDVI) product, leaf area index (LAI) product, vegetation coverage (FVC) product, net primary productivity (NPP) product, land cover (LC) classification product, albedo product, and so on. These products have been applied and will be released in public soon. Therefore, this paper will be the required reference for many upcoming papers for biophysical and geophysical parameters produced using the Chinese GF ARD from this study.

In this paper, an application example of land cover classification has been added used the ARD (see section 3.6). With the support of the monthly time series surface reflectance data from this study, higher accurate landcover map can be easily and quickly produced by using the simplest pixel based supervised classification method compared to the major landcover maps nowadays.

Reviewer 3 Report

The work is interesting, but the author should clarify their findings on the real application. At the same time, the authors need to emphasize the innovation of their results.

Author Response

Thank you very much for reviewing our manuscript with very supporting opinion and the constructive suggestion. The ARD could be used in many quantitative remote sensing products, like vegetation index (NDVI), leaf area index (LAI), vegetation coverage (FVC), net primary productivity (NPP), land cover (LC), albedo, and so on. In fact, more than 10 biophysical and geophysical products have been produced using these ARD data proposed in this paper based on the fund National Key R&D Program of China. These products have been applied and will be released in public soon. In this paper, an application example of land cover classification has been added used the ARD (see section 3.6). With the monthly time series surface reflectance data, high precision land cover product can be easily obtained even using the simplest pixel based supervised classification method. This application shows the advantages of the proposed ARD very well.

This paper proposed a data processing framework specifically for Chinese domestic satellites. In recent years, China has launched many satellites, and collected huge data. However, there is still no complete processing framework so far, so the application of satellite data is limited. When every researcher wants to apply data, they should start with data preprocessing, which leads to repetitive work and a lot of time wasted. Take Landsat TM/ETM+ and Sentinel-2/MSI for example, researchers can download data of different levels and regions according to their needs instead of preprocessing the data themselves. This is the real meaning of the Analysis Ready Data (ARD).

Round 2

Reviewer 2 Report

I accept the paper now