Reply on RC1

First, the AVHRR GAC snow dataset have been updated with the final released version. Because the final AVHRR GAC snow data published and accessible for everyone is different from what we have previously employed in the paper. Our team have improved the retrieval algorithm, because there was a need to retrieve also snow on ground with an identical procedure as for viewable snow. The final AVHRR GAC data dataset (openly accessible here https://catalogue.ceda.ac.uk/uuid/5484dc1392bc43c1ace73ba38a22ac56) in the whole time series was based on the algorithm SCAMOD (Metsämäki et al. 2015). Consequently, many results and conclusions have been reworked.

Second, we expand the description of the generation of AVHRR GAC snow cover product in Section 2.2 and include the snow cover maps for illustration.
Third, we have made more in-depth analysis of the performance of AVHRR GAC snow. In particular, the study area has been divided into eight groups according to their elevations (0-200, 200-500, 500-1500, 1500-2500, 2500-3500, 3500-4500, 4500-5500, >5500) in order to take the topography into consideration. Furthermore, the effect of landcover type, slope, aspect, and topographical variability were analyzed for different elevation regions in Section 4.2.3 (Pixel-based comparison and potential influential factors on accuracy).
Fourth, the structure of the manuscript has been improved. The accuracy of MODIS based on in situ sites was discussed along with AVHRR in Section 4.1. Furthermore, the comparison between AVHRR GAC and MODIS snow regarding the accuracy and temporal stability is also presented in this section. The comparison between AVHRR GAC snow and MODS snow regarding their absolute values as well as the comparison between AVHRR GAC snow and Landsat snow were presented in Section 4.2 (Comparison based on medium to high resolution data).
Last but not the least, based on the new results and analyses, more comprehensive conclusions were presented in Section 5.
For the specific comments for each reviewer, we have made detailed reply as following: Specific Comments: Line 44: The GCOS does recommend area covered by snow cover as an essential climate variable daily at 1 km or higher resolution. The GAC resolution is much lower than recommended. The lower resolution needs to be discussed relevant to GCOS observation requirements.
Re: On the one hand, GCOS definitely is in need of long, meaning climate-relevant time scales of >30 years, thus this is only covered by AVHRR. On the other hand, although the GAC resolution is lower than recommended, this snow dataset is unique over such a long time scale at daily resolution available. In order to clarify this point, we have added the sentences as "……the ESA Climate Change Initiative (CCI) has emphasized the necessity of generating consistent, high quality long-term datasets over the last 30 years as a timely contribution to the ECV databases. In this demand, a global time series of daily fractional snow cover product has been generated from AVHRR GAC data (Naegeli et al., 2021). This snow dataset is unique as it spans 4 decades and thus provides information about an ECV at climate-relevant time scales."in the Introduction.
Discussion is also lacking on explaining the relevance of coarse GAC resolution product in the HKH region, especially when higher resolution snow cover data sources are available.
Re: We would like to point out the AVHRR GAC snow is a global product for all land areas, excluding Antarctica and Greenland ice sheets. It provides daily products for the period 1982-2019. This dataset is unique as it spans 4 decades and thus provides information about an ECV at climate-relevant time scales. In fact, this is the best spatial resolution over such a long-time scale at daily resolution available. It is important to note that the evaluation of AVHRR GAC snow over HKH is just a typical representation of its performance over mountainous area.
The HKH was selected as the study area partly because of its particular sensitiveness to climate change and thus reliable daily snow cover data across this area are in great demand, and partly because this area is featured by rich diversity of climates, hydrology, ecology, biology, and topography. Then it provides a favorable condition to explore the influential factors (e.g., elevations, landcover type, slope, aspect, and topographical variability) on the accuracy of snow mapping.
In order to clarify this point, we have emphasized the relevance of HKH and AVHRR GAC snow in Introduction as "The Hindu Kush Himalaya (HKH), which is often called as the freshwater tower of Asia, comprises the highest concentration of snow outside the polar regions. The snow cover of this area plays a crucial role in the water supply of several major Asian rivers (Immerzeel et al, 2009). On the other hand, the HKH area is of special interest due to large area, rich diversity of climates, hydrology, ecology, and biology (Wester et al., 2019). Variations in snow cover affect the precipitation, near-ground air temperature, and summer monsoon in Eurasia and across the Northern Hemisphere (Hao et al., 2018). Given the fact that HKH is particularly sensitive to climate change and thus shows strong interannual variability, reliable daily snow cover data over a long time series across this area are in great demand." Section 2.2: I do not understand how you created a AVHRR GAC snow extent. I read the FCDR product user guide. I downloaded and looked at the FCDR data products. There is no snow cover extent dataset in any of the products. There is no AVHRR channel reflectance data in those products that could be used to calculate NDSI.
Re: In the revised manuscript, we have extended the description of AVHRR GAC snow as "The AVHRR GAC snow cover extent time series version 1 derived in the frame of the ESA CCI+ Snow project is the most recent long-term global snow cover product available (Naegeli et al., 2021 (Stengel et al., 2020). The data were pre-processed with an improved geocoding and an inter-channel and inter-sensor calibration using PyGAC (Devasthale et al., 2017). Snow cover extent retrieval method was developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. Alongside the daily reflectance and brightness temperature information, an excellent cloud mask including pixel-based uncertainty information is provided (Stengel et al., 2017(Stengel et al., , 2020. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The water bodies, permanent ice bodies and missing values are flagged. SCAmod retrieves both the snow cover on top of the canopy as well as on ground below the canopy by taking the canopy density into account. Here, we focus on the latter variable as this is most suitable for the comparison with in situ stations. (2012). The AVHRR GAC FCDR snow cover product comprises only one longer data gap of 92 days between November 1994 and January 1995 resulting in a 99 % data coverage over the entire study period of 38 years. In this study, we will focus on the evaluation of raw daily retrieval of AVHRR GAC snow extent (denoted by "AVHRR_Raw") since additional uncertainty will be introduced with the gap-filling process." in Section 2.2. (2006) is based on a using Landsat TM data at 30 m to estimate the fractional amount of snow in a MODIS 500 m pixels. That relationship is not directly applicable to GAC data at 1x5 km 2 resolution. Please explain why you applied this regression to GAC data to estimate FSC.

The fractional snow method of Salomonson and Appel
Re: As we explained before, the final AVHRR GAC snow adopted by the revised manuscript is different from what we have previously employed. Our team have improved the retrieval algorithm, because there was a need to retrieve also snow on ground with an identical procedure as for viewable snow. The final algorithm was developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. The detailed description of this method can be seen in Section 2.2 (AVHRR GAC snow extent retrieval) in the revised manuscript.
NDSI cannot be calculated from AVHHR data until 1998 when with NOAA-15 Channel 3A at 1.6 um was added to AVHRR. Before then there was no shortwave infrared channel covering 1.6 um. It is not possible to calculate NDSI from AVHRR data prior to 1998. But your dataset record is 1982-2013. How can it possibly be consistent across major design changes in the AVHRR? Re: The old retrieval algorithm of snow cover extent considers the high reflectance of snow in the visible spectra and the low reflectance in the short-wave infrared (). In order to construct the long-term AVHRR snow extent dataset, the reflectance values stem either from channel 3a centred around 1.61 μm or from the reflective part of channel 3b centred around 3.75 μm (Roger et al., 1993). We derived the reflective part of channel 3b (ref3b) using the method proposed by Baum (1999): (2) Where is the measured radiance (mW m -2 sr -1 cm) for channel 3b, is the 11 μm brightness temperature, is the integrated solar spectral irradiance (mW m -2 cm) weighted by the spectral response function for channel 3b, and is the cosine of the solar zenith angle. To account for changing solar zenith (sunz) angles the Top of Atmosphere (TOA) reflectance was corrected by a division of the cosine(sunz).
The calculation of NDSI from AVHRR GAC is not explicitly given. Without explanation of how NDSI was calculated the entirety of the validation discuss and results is doubtful.
Re: The Normalised Difference Snow Index (NDSI) were determined by the high reflectance in the visible spectra and the low reflectance in the short-wave infrared: (1) In the case of here used AVHRR GAC data, is represented by channel 1 centred around 0.63 μm. The reflectance values stem either from channel 3a centred around 1.61 μm or from the reflective part of channel 3b centred around 3.75 μm (Roger et al., 1993). We derived the reflective part of channel 3b (ref3b) using the method proposed by Baum (1999): ( 2) where is the measured radiance (mW m -2 sr -1 cm) for channel 3b, is the 11 μm brightness temperature, is the integrated solar spectral irradiance (mW m -2 cm) weighted by the spectral response function for channel 3b, and is the cosine of the solar zenith angle. To account for changing solar zenith (sunz) angles the Top of Atmosphere (TOA) reflectance was corrected by a division of the cosine(sunz). Section 2.3.2: The estimate of FSC for TM/ETM data is flawed. Salomonson and Appel (2006) did not derive the FSC regression to estimate TM/ETM FSC, they derived a FSC estimate for MODIS data based on the higher spatial resolution TM/ETM data. That regression is not appropriate to estimate FSC in TM/ETM data.
Re: Although the method by Salomonson and Appel (2006) is originally designed for MODIS FSC products with a mean absolute error of less than 10% (Salomonson and Appel, 2004), we assumed that such an accuracy can be achieved with higher resolution data in this paper. This treatment follows the recommendations of (Metsamaki et al., 2015), which applied fractional snow method by Salomonson and Appel (2006) to Landsat data for the evaluation of coarse-pixel snow extent products.
In order to clarify this point, we have added the sentences as "Following the recommendation of Metsamaki et al. (2015), the fractional snow method by Salomonson and Appel (2006) was employed to generate reference FSC from Landsat TM/ETM imagery. This method is originally designed for MODIS FSC products, with a mean absolute error of less than 10% (Salomonson and Appel, 2004). In this paper, we assumed that such an accuracy can be achieved with higher resolution data." in Section 2.3.2 in the revised manuscript.

Reference:
Metsamaki, S., Pulliainen, J., Luojus, &K., et al.: Introduction to globsnow snow extent products with considerations for accuracy assessment, Remote Sens. Environ., 156, pp. 96-108, 2015. No AVHRR GAC snow map is shown! No snow maps are shown! The research describes building an AVHRR GAC snow cover map, but none is shown. Visual evidence of the snow maps must be presented.
Re: We have included the snow cover maps for illustration in Section 2.2 in the revised manuscript.