Subpixel Snow Mapping Using Daily AVHRR/2 Data over Qinghai–Tibet Plateau
, Xinyun Wang 5, Zhaojun Zheng 6, Chenzou Liu 7, Huicai Yang 1,2 and Baoni Xie 1,2
Round 1
Reviewer 1 Report
Dear Authors,
I am reviewer 3 of previous submission, the article was amended in agreement with my requests and I had already accepted it in this form on 24/05/2022.
Best regards
Author Response
We thank your positive comments to our work, and your valuable and constructive comments.
Reviewer 2 Report
Qinghai-Tibet Plateau is covered with snow widely, and has significant impacts on regional hydrologic cycle and atmospheric change. Basing on subpixel snow mapping of daily AVHRR/2 data, the high accurate historical data of fractional snow cover can be obtained, and the time duration of high accurate snow cover data can also be largely extended, and the fractional snow cover data are very important to meet requirements of modelling hydrology and atmosphere systems. There are two advantages in the article.
1) If the subpixel snow cover mapping of AVHRR/2 data was implemented, the time series of the subpixel snow area data from moderate resolution remotely sensed data will extended to 40 years ago.
2）A lookup table was introduced into the snow mapping, and the processing speed of the mapping is 3 times faster than that without using the lookup table.
However, several issues still need to be resolved before a publication to the Remote Sensing.
Major Comments:
1. Some relevant literature needs to be added appropriately in section 1 (Introduction)
2. The objective of this study is to develop a fast, and yet accurate algorithm for subpixel snow mapping in rugged mountainous area, based on AVHRR/2 data. The approach of cloud removal is not the important issue in this article, and I can basically understand why there is no accuracy verification for the cloud removal method in Section 2.2.4. However, the author should mention in the discussion section, especially some successful cloud removal methods for the fractional (subpixel) snow cover products. For example, the cubic spline interpolation algorithm of Tang et al.
[1] Tang Z, Wang X, Wang J, et al. Spatiotemporal variation of snow cover in Tianshan Mountains, Central Asia, based on cloud-free MODIS fractional snow cover product, 2001–2015 [J]. Remote Sensing, 2017, 9(10): 1045.
3. in Figure 2, the four conditions are unrelated and can be combined with an "or" operation.
4. What is the blue in Figure 3? What are the types of snow cover? Should there be legend instructions?
Author Response
We thank reviewer’s valuable and constructive comments.
Comment 1: Some relevant literature needs to be added appropriately in section 1.
Response: According to the suggestion, the article has been revised.
Comment 2: The objective of this study is to develop a fast, and yet accurate algorithm for subpixel snow mapping in rugged mountainous area, based on AVHRR/2 data. The approach of cloud removal is not the important issue in this article, and I can basically understand why there is no accuracy verification for the cloud removal method in Section 2.2.4. However, the author should mention in the discussion section, especially some successful cloud removal methods for the fractional (subpixel) snow cover products. For example, the cubic spline interpolation algorithm of Tang et al.
Response: According to the suggestion, the article has been revised in section 2.2.4.
Comment 3: In Figure 2, the four conditions are unrelated and can be combined with an "or" operation.
Response: According to the suggestion, the article has been revised.
Comment 4: What is the blue in Figure 3? What are the types of snow cover? Should there be legend instructions?
Response: Because the article was focused on obtaining the fractional snow cover over Qinghai-Tibet Plateau, I’m sorry, the types of snow cover were not researched in the article, and the blue also was identified in Figure 3. In fact, the blue in Figure 3 is lakes.
