Long-term monitoring of snow cover is crucial for climatic
and hydrological studies. The utility of long-term snow-cover products lies in
their ability to record the real states of the earth's surface. Although a
long-term, consistent snow product derived from the ESA CCI
Snow cover is an important indicator to estimate climatic changes and a key input for climate, atmospheric, hydrological, and ecosystem models (Fletcher et al., 2009; Hüsler et al., 2012; Xiao et al., 2018). On one hand, snow cover exacerbates the effect of global warming through the positive feedback between snow and albedo (Serreze and Francis, 2006). Furthermore, it affects the hydrometeorological balance through snowmelt (Simpson et al., 1998). On the other hand, snow cover is severely affected by climate change due to its high sensitivity to changes in temperature and precipitation (Brown and Mote, 2009). Therefore, accurate monitoring of its long-term behavior is a vital issue in improving weather and climate prediction, supporting water management decisions, and investigating climate change impacts on environmental variables (Arsenault et al., 2014; Sun et al., 2020).
The Hindu Kush Himalayan (HKH) region, which is often called 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 region is of special interest due to its 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 the HKH region 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.
Optical satellite data provide important data sources for snow-cover retrieval through the contrasting spectral behavior of snow relative to other natural surfaces in the visible and middle-infrared regions (Tedesco, 2014; Zhou et al., 2013). The global spatial coverage of satellite data makes it an efficient data source to improve our knowledge of snow-cover dynamics (Siljamo and Hyvärinen, 2011; Solberg et al., 2010). Many satellites have been used to generate snow-cover products at various spatial and temporal resolutions, such as AMSR-E (Tedesco and Jeyaratnam, 2016), MODIS (Riggs et al., 2016a), AVHRR (Advanced Very High Resolution Radiometer; Shan et al., 2016), VIIRS (Riggs et al., 2016b), and Landsat (Rosenthal and Dozier, 1996). In particular, new generation satellite sensors (e.g., MODIS, VIIRS) generally show an advantage over old sensors such as AVHRR and TM/ETM (Thematic Mapper and Enhanced Thematic Mapper) which suffer from significant saturation over snow in the visible channels (WMO, 2012). Nevertheless, AVHRR offers the unique opportunity to generate a consistent snow product over a 30-year normal climate period (IPCC, 2013) and thus remains vitally important. In response to the systematic observation requirements of the Global Climate Observing System (GCOS), 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 (Essential Climate Variable) databases. For this demand, a global time series of daily fractional snow-cover products has been generated from AVHRR GAC (global area coverage) 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 timescales.
Nevertheless, there are many factors, such as data processing (e.g., calibration, geocoding) and the accuracy of cloud masking, atmospheric constituents, topographic effects, bidirectional reflectance distribution function (BRDF), and the limitations of snow-cover retrieval algorithms, influencing the accuracy of the AVHRR GAC snow-cover extent. Hence, the performance of the AVHRR GAC snow product needs to be extensively evaluated, especially over the HKH region which is highly sensitive to climate change. This paper presents the validation of the AVHRR GAC snow product over the HKH area during snow seasons. Of particular importance is validating the temporal performance of the product (i.e., different platform operated over the entire dataset period). To this end, the first validation was carried out using 118 in situ stations' measurements. The correlation between spatial products and “point” measurements depends strongly on the selected snow depth. Therefore, the influence of snow depth on the accuracy of the product was also investigated. Considering that the HKH region features distinct characteristics of snow cover with shallowness, patchiness, and frequent short duration ephemeral snow (Qin et al., 2006), in situ site measurements alone are not enough to characterize its accuracy. A multi-scale validation and comparison strategy is highly needed to assess its accuracy over greater spatial extent and elevation ranges. Within this validation framework, the influences of land-cover types, elevations, aspects, slopes, and topographies on the accuracy of AVHRR GAC snow were also explored. Finally, the MODIS snow maps were also introduced to conduct a comparison between the well-validated MODIS product and the new AVHRR GAC snow product. Section 2 describes the study area and data. The validation methodology is explained in Sect. 3. The performance of the AVHRR GAC snow dataset is presented and discussed in Sect. 4. A brief conclusion is presented at the end.
The HKH region covers a mountainous region of more than 4 million km
The validation based on in situ stations covers mainly the eastern part of the HKH region (Fig. 1a). To demonstrate the accuracy of the AVHRR snow product over the whole area, Landsat data covering the entire region were introduced to conduct a multi-scale validation (Fig. 1b). Furthermore, in order to explore its performance in high detail for a wide range of conditions (e.g., elevation, topography, and land cover), validation against Landsat TM data was also performed in detail using two tiles of Landsat data (path 140, rows 40 and 41, denoted as “P140-R40/41”) (Fig. 1c), covering a diverse region on the Nepal/Tibet border centered around Mount Everest. This region was chosen because it contains the greatest elevation range in the Himalayas. The northernmost part of this region are areas on the Tibetan plateau exceeding 6000 m a.s.l. where vegetation change is occurring rapidly (Qiu, 2016). Furthermore, it covers a broad range of climatic conditions (Bookhagen and Burbank, 2006). Therefore, this region is a microcosm of the range of conditions experienced across the wide HKH region and thus provides a good point for investigating snow extent accuracy under different conditions (Anderson et al., 2020).
The AVHRR GAC snow-cover extent time series version 1 derived in the frame
of the ESA CCI
To reduce the effect of cloud coverage, a temporal filter of
AVHRR GAC raw
In situ data were provided by the China Meteorological Administration
(
Landsat data were introduced for two purposes: (i) to check the spatial
consistency between AVHRR GAC snow and Landsat-based snow based on 197
scenes covering the whole HKH region and (ii) to explore the factors (e.g.,
elevation, topography, and land cover) influencing the accuracy of AVHRR GAC snow
based on P140-R40/41. To mitigate the effect of clouds, the validation
over P140-R40/41 was restricted to clear-sky (cloud no more than 10 %)
scenes of Landsat 5 TM during snow seasons (
The Terra MODIS Level 3, Collection 6, 500 m daily snow-cover products
(MOD10A1) (Hall and Riggs, 2016) over the HKH region from 2000 to 2013 were obtained
through Google Earth Engine (GEE). The MODIS snow detection algorithm also uses
NDSI and other test criteria (Riggs et al., 2016a). Instead of directly
providing binary snow-covered area (SCA) and FSC, version V006 provides
NDSI_Snow_Cover and NDSI. The former is
reported in the range of 0–100 with other features identified by mask
values, while the latter represents the real NDSI values multiplied by
10 000, which is calculated for all pixels (Riggs et al., 2016a). This
treatment provides more information and great flexibility to enhance the
accuracy of the product because the NDSI range is not necessarily restricted to
0.4 to 1.0 for snow detection. Actually, NDSI_Snow_Cover functions very similarly to FSC in version V005
since it can be linked using the equation of FSC
The digital elevation model (DEM) information was obtained from the SRTM (Shuttle Radar Topography Mission) dataset, which provides a nearly global coverage with a spatial resolution of 90 m. In this study, the elevation, slope, aspect, and topographical variability were derived using this dataset in order to investigate their influences on the accuracy of the AVHRR GAC snow extent product. The topographical variability within a certain AVHRR GAC pixel was determined by calculating the standard deviation of elevations of all sub-pixels within its spatial extent, while the elevation, slope, and aspect were resampled to match the resolution of the AVHRR GAC snow dataset.
The MODIS Terra/Aqua Combined Annual Level 3 Global 500 m Collection 6 land-cover dataset (MCD12Q1) was generated using a supervised classification
methodology (Friedl et al., 2010). In this study, the International
Geosphere–Biosphere Programme (IGBP) of the MCD12Q1 mosaic was used to
investigate the difference in accuracy over different land-cover types. It
includes 11 types of natural vegetation, 3 types of developed and mosaic
lands, and 3 types of non-vegetated lands, which have been reclassified into
nine major classes: forest, grassland, savannas, croplands, built-up lands,
barren, permanent snow and ice, water body, and wetlands. In order to match
with the pixel size of AVHRR GAC snow, the MCD12Q1 was resampled to
0.05
AVHRR GAC snow extent was evaluated from several aspects. The validation based on in situ sites aims to prove the long-term consistency since in situ stations provide valuable long time series measurements, while the comparison with Landsat and MODIS snow is focused on their spatial consistency and the in-depth analysis of influential factors (elevation, topography, and land cover). The validation strategy is briefly summarized in Table 1.
Validation strategy: data and purpose.
Although the validation based on in situ sites leaves issues of scale unresolved
and therefore likely accompanied by uncertainties, in situ observations provide the
only source to validate the time series AVHRR GAC snow extent over this long
period. Since there is no reliable way to convert SD to FSC, both FSC and SD
information were converted to binary information by applying appropriate
thresholds, respectively. Different thresholds have been suggested for in situ SD
measurements to determine whether the associated pixel is covered by snow,
ranging from 0 to 5 cm (Parajka et al., 2012; Hori et al., 2017; Hao et
al., 2019; Huang et al., 2018; Liu et al., 2018; Zhang et al., 2019; Gascoin
et al., 2019). Therefore, the sensitivity of thresholds was tested by
computing accuracy metrics with SD increasing from 1 to 5 cm. The FSC
maps were transferred from fractional to binary snow information by applying
a threshold of FSC
Concerning the comparison of spatial satellite data with in situ measurements, a
point-wise comparison was implemented. To relate in situ “point” measurements
with AVHRR GAC “area” snow information, both the center pixel containing
the in situ point measurement and the
A short summary of all the combinations of thresholds.
The
Contingency table used to determine probability of detection and bias.
Based on these measures, indicators such as accuracy (ACC), Heidke skill
score (HSS), and bias (Bias) were determined (Eqs. 3–5) (Hüsler
et al., 2012). ACC denotes the percentage of correctly classified pixels
divided by the total number of pixels. ACC values closer to 1 denotes a
perfect agreement between the snow product and the reference data, while a
value of 0 corresponds to complete disagreement. However, it is strongly
influenced by the most frequent category (i.e., in summer) (Hüsler et
al., 2012) and thus ideally requires an equal distribution of categories.
Hence, we confine our accuracy assessment to the snow season (from October
to March) only, a limitation that was implemented in other studies as well
(Yang et al., 2015; Gafurov et al., 2012; Hüsler et al., 2012; Huang et
al., 2011). The HSS and Bias provide refined measures in cases when the
frequency distribution within the validation subsets is not equal. The
former describes the proportion of pixels correctly classified over the
number that was correct by chance in the total absence of skill. Negative values
indicate that the chance performance is better, 0 represents no skill, and a
perfect performance obtains an HSS of 1 (Hüsler et al., 2012). It is
generally true that a value above 0.3 denotes a relatively good score for a
reasonably sized sample for the binary forecast (Singh, 2015). The Bias,
described by the ratio of the number of snow-covered pixels to the number of
reference data pixels, is a relative measure to detect overestimation (value
is higher than 1) or underestimation of snow (value is less than 1).
Unbiased results should have a value of 1.
Finally, in order to check the relative performance of AVHRR GAC snow to the
well-used MODIS product, MOD10A1 V006 was also evaluated with in situ station data
following the same method. It is expected that the major difference in their
performance is either due to the quality of the applied processing and snow-cover retrieval algorithms or the general satellite data characteristics. As
for the comparison of their absolute values, the root mean square error
(RMSE), mean bias (mBias), and the coefficient of correlation (
In order to evaluate AVHRR GAC snow at a broader spatial scale, Landsat
TM/ETM aggregated FSC was used as the reference. Snow-free values are
treated as 0 % snow, and a fully snow-covered pixel is assigned 100 % snow.
The validation was conducted from two aspects: (i) one is based on 197 scenes
covering the whole HKH region in order to increase the spatial coverage of
validation, and (ii) the other is based on
To test the sensitivity of the in situ SD threshold for the snow-cover detection, the overall accuracy metrics were computed by combining data of all in situ sites throughout the study period (from 1982 to 2013 for the AVHRR-GAC-derived snow and from 2000 to 2013 for MOD10A1). The variations in Bias, ACC, and HSS with all the threshold combinations (Table 2) are shown in Fig. 3.
As shown in Fig. 3a, an SD threshold of 2 cm (case2) maximizes the overall
accuracy of the AVHRR GAC snow-cover dataset. With the further increase in
SD threshold, the AVHRR GAC snow detected will be seriously
overestimated. This indicates the presence of snow can be best detected by
the AVHRR GAC dataset for in situ snow depth measurement of 2 cm. Furthermore, the
increasing rate of ACC and decreasing rate of HSS are the highest between
the 1 and 2 cm SD thresholds, and it flattens for greater SD thresholds. When it
comes to the influences of geometric mismatch or spatial heterogeneity
(center pixel versus
Sensitivity analysis of product accuracy related to snow depth thresholds of the in situ station data. The overall error is a spatiotemporally integrated statistical measure.
As seen from Fig. 3a, AVHRR snow datasets show distinct advantages over MODIS snow regarding the Bias value. The former shows biases of 0.94 and 1.03 for the AVHRR raw snow and gap-filled snow, respectively, while the latter is seriously overestimated with the bias of 1.74. Nevertheless, the three datasets show comparable ACC, with the values of 0.94, 0.92, and 0.94 for AVHRR raw snow, AVHRR gap-filled snow, and MODIS snow datasets, respectively. The HSSs of the three datasets are reasonable, with the values larger than 0.3. MODIS snow shows the largest HSS of 0.35, followed by AVHRR raw snow with an HSS of 0.34. The AVHRR gap-filled snow-cover dataset ranks last, with the smallest HSS of 0.31. From the above results, it can be found that the AVHRR raw dataset performs slightly better than the AVHRR gap-filled dataset with respect to the agreement with in situ sites and the algorithm performance (skill). This is reasonable since additional uncertainty was introduced in the gap-filling process. For this reason, we will only focus on AVHRR raw snow for further analysis. Generally, AVHRR raw snow is comparable with MODIS snow when ACC and HSS are focused.
The time series (denoted by the dashed lines) of ACC,
HSS, and Bias for AVHRR raw and MODIS snow data during the investigated
period. A simple moving average with a box dimension of
From Fig. 4, it can be seen that the interannual variability in these accuracy metrics is evident, especially for ACC and HSS. In the time series of AVHRR GAC snow, ACC is basically distributed between about 88 % and 92 % (Fig. 4a). An obvious increase in ACC can be observed from 1982 to 1985, followed by a decrease in ACC from 1985 to 1992. Then an increasing trend of ACC occurs from 1992 to 2000. From 2000 to 2010, ACC is relatively stable with time. But after 2010, an increasing trend reappears. Differently from the previous assessments, the ACC of the AVHRR snow datasets at the beginning of the time series (1982–2000) is slightly worse than the end of the time series (2000–2013) regarding the magnitude of ACC and its temporal consistency. The HSS shows a different behavior compared to ACC (Fig. 4b), which increases slightly and monotonously from 0.45 at the beginning to about 0.48 at the end of the time series. This further indicates that the performance of AVHRR snow continues to improve with time. Nevertheless, the improvements of the performance of AVHRR GAC snow do not occur in the Bias (Fig. 4c). The Bias shows the best performance from 1990 to 2000, with relatively stable values around 1. But during other time periods, relatively large fluctuations appear, and it generally overestimates snow during these periods.
As shown in Fig. 4, it can be seen that MODIS snow is inferior to AVHRR GAC snow regarding the magnitude of ACC and its temporal consistency. Furthermore, its HSS is consistently smaller than that of AVHRR GAC snow. Nevertheless, its temporal stability is slightly better than AVHRR GAC snow since the HSS of MODIS almost stays constant over time. When it comes to Bias, MODIS snow shows a more serious overestimation than AVHRR GAC snow but comparable temporal stabilities to the latter.
In order to highlight the performance of AVHRR GAC snow in different months, the temporal variations in ACC, HSS, and Bias over different months are presented (Fig. 5). From Fig. 5a, it can be seen that ACC of AVHRR GAC snow is over 0.85 for all months and even above 0.90 for October. Nevertheless, both the temporal variation trend and the magnitude of ACC show differences from month to month. It is clear that the AVHRR GAC snow shows the highest ACC in October and lowest ACC in January, but the temporal stability of ACC is best in November and worst in January and December. It is interesting that the results tend to polarize into two groups: ACC for January through March and ACC for October through December. Generally, ACC in the former group is smaller than those in the latter group. It is noteworthy that ACC after 2000 is generally larger and more stable than those in earlier years on the monthly scale (Fig. 5a), indicating the better accuracy and consistency of the younger satellite platforms after 2000. Compared to AVHRR GAC snow, the ACC of MODIS snow consistently shows large temporal variations for all months, and there is no month that shows advantages over others regarding the magnitude and temporal stability of ACC (Fig. 5b).
The HSS for different months are larger than 0.4 throughout the time series, but large differences of the magnitude and temporal stability exist between different months (Fig. 5c). Similar to ACC, the AVHRR GAC snow generally shows the largest HSS in October for most of the time. Furthermore, the HSS in October shows a similar temporal variation trend with the overall temporal trend of HSS in Fig. 4b. Among all the months, the HSS in December shows the largest temporal variations, featured by the highest HSS from 1990 to 2000 and the lowest HSS from 2005 to the end. The HSS in January through March shows relatively smaller temporal variations than those in October through December. Regarding the magnitude of HSS, the different rank of these months during different periods may be associated with the shift of snow-cover phenology due to interannual variability intensified by global warming. Unlike AVHRR GAC snow, MODIS snow shows larger HSSs in January and February (Fig. 5d). Furthermore, the temporal variations in HSS are more significant than AVHRR GAC during the same period.
The temporal behavior of ACC for AVHRR raw and MODIS snow data in different months (indicated by the different colored lines) during the snow season. The solid-colored lines represent the fitted trend of these accuracy indicators for different months.
Although the AVHRR GAC snow shows the best performance in October regarding the magnitude of ACC and HSS, it shows serious overestimation in this month (Fig. 5e). In particular, AVHRR GAC snow generally overestimates snow in February, March, October, and November. By contrast, it either slightly overestimates or underestimates snow in December and January, with the bias distributed around 1. This result is understandable because during December and January, snow coverage tends to be dense and spatially continuous, which results in unbiased estimation. By contrast, during February, March, October, and November, snow cover tends to be patchy, and AVHRR GAC data are more able to detect snow than in situ point observations due to the large pixel coverage. MODIS snow consistently overestimates snow in different months and shows larger temporal variations than AVHRR GAC snow (Fig. 5f).
From the results above, it can be concluded that the AVHRR GAC snow dataset performs variably throughout the course of the year, which may be related to the different amounts of snow in the HKH region. Generally, the magnitudes of ACC and HSS are largest in October and smallest in January. But the temporal stability of ACC is best in November and worst in January and December, while that of HSS is worst in December. The results of Bias provide different perspectives for the performance of AVHRR GAC snow. It generally overestimates snow in February, March, October, and November. By contrast, unbiased estimation is likely to occur in December and January. Compared to AVHRR snow datasets, the interannual variability in ACC, HSS, and Bias of the MODIS snow product in different months is generally stronger (Fig. 5).
Figure 6 show the boxplots of the validation metric derived from each in situ station, with the aim of revealing their spatial variability. It can be observed that the spatial variability in these validation metrics widely exists given their dispersed distribution. The maximum of ACC even reaches 0.99 for the AVHRR snow datasets, while the minimum values are close to 0.76 (Fig. 6a). Similarly, HSS also shows a dispersed distribution for the AVHRR snow datasets. The AVHRR raw dataset ranges from 0.2 to 0.39 with min–max values of 0.01 to about 0.68 (Fig. 6b). Likewise, the bias is located around 0.51–1.6 with min–max values of 0.05 and 2.89 for the AVHRR dataset (Fig. 6c). These results are understandable because the performance of satellite snow datasets is affected by many factors. Despite the awareness of spatial variability in these validation metrics, the degree of variability depends on satellite datasets and metrics. The HSS and Bias of the MODIS snow dataset are more divergent than the AVHRR raw snow dataset (Fig. 6).
The boxplots of ACC
Following the early study (Klein and Barnett, 2003), the effect of SD on the
accuracy of satellite snow datasets was evaluated (Fig. 7a). Observed SD
was divided into six categories: SD
The effect of FSC on the accuracy of satellite snow datasets was checked in
Fig. 7b. FSC was grouped into five categories using the ranges of
0 %–20 %, 20 %–40 %, 40 %–60 %, 60 %–80 %, and 80 %–100 %. Likewise, the ACC of the AVHRR and MODIS snow datasets shows a
similar response to FSC. The highest ACC was found when FSC
The variation in ACC with snow depth
As seen in Fig. 7a, we can find that the accuracy of the AVHRR snow datasets
is larger than the MODIS snow product when SD
Figure 8a and b present the distribution of ACC for two satellite snow datasets against in situ site observations over different elevation regions (five classes) and land-cover types (four types), respectively. It is generally thought that coarse-pixel satellite snow products perform better at higher elevations due to the continuous and thick snow cover (Yang et al., 2011). Nevertheless, the ACC over the HKH region shows different phenomena. The two satellite snow products consistently show larger ACC over slightly lower elevations than those over higher elevations. Nevertheless, an exception can be found in the elevation region of 3500–4500 m, where the ACC of the two datasets is the lowest over the whole HKH region. Furthermore, the ACC over these elevation regions is the most divergent, demonstrating that the accuracy of snow product within this range is more likely to be affected by other factors. It is noteworthy that the MODIS snow product slightly outperforms the AVHRR snow dataset over different elevation regions. This is reasonable since the spatial-scale mismatch between in situ and satellite-based observations is greater for the AVHRR snow datasets than for the MODIS snow dataset.
Boxplots of ACC from the direct comparison with in situ site
observations over different elevation regions
Despite the effect of elevation on ACC, it was not treated when we explored the effect of land-cover type on ACC (Fig. 8b) because the number of in situ sites over different land-cover types and different elevation regions are very limited. For AVHRR GAC snow, the highest agreement with in situ measurements is found in the barren class, followed by grasslands and savannas. Although nearly half of the in situ sites over forest show ACC larger than 0.91, substantial numbers of in situ stations show relatively low ACC over forest. This indicates that the well-known issues of identifying snow in forested areas using optical satellite data are not fully resolved in AVHRR GAC snow. It is interesting to find that the MODIS snow product maintains its superiority over different land-cover types, and its advantage becomes more pronounced over forest and savannas. The different performance between AVHRR snow and MODIS snow is partly caused by their individual accuracy and partly caused by the different effects of spatial-scale mismatch between in situ and satellite-based observations.
In order to investigate the absolute difference between AVHRR GAC and MODIS
snow, we compared them on the pixel basis following the cross-validation
framework. The indicators of RMSE, mean Bias (mBias), and correlation
coefficient (
The distribution of mBias
In order to avoid the spatial limitations of the in situ stations, the comparison
between the AVHRR raw snow datasets and Landsat data was also carried out over
the whole extent of the HKH region. The RMSE, mBias, and
Summary of accuracies of AVHRR raw snow data with Landsat5 TM snow data over the whole HKH region.
In order to explore the performance of AVHRR GAC snow in high detail for a
wide range of conditions, the spatial accuracy was assessed on the pixel
basis based on Landsat5 TM data time series over the areas covered by
P140-R40/41. The AVHRR snow datasets systematically underestimate snow-covered
areas with regards to the Landsat5 TM data (Table 5). This can be explained
by the fact that direct coarse-resolution FSC is more likely to be lower
than the FSC aggregated from high-resolution FSC because high-resolution
data are able to pick up snow in one pixel, which is too little to create
enough snow signals in coarse-resolution pixels but will show up in the
aggregated FSC (Singh et al., 2014; Jain et al., 2008). The accuracy of
AVHRR GAC snow is different over the two areas, with better performance over
P140-R40 than P140-R41 (Table 5). AVHRR raw snow shows a higher
accuracy with a smaller RMSE of 11.39 % (vs. 15.08 %) and mBias of
Summary of accuracies of AVHRR raw and AVHRR gap-filled snow data with Landsat5 TM snow over P140-R40 and P140-R41.
Both the land-cover types and topographies are highly heterogeneous over the
HKH region. Here, the sub-region P140-R40/41 was chosen to investigate the
factors (i.e., elevations, land-cover type, slope, aspect, and
topographical variability) influencing the accuracy of the AVHRR GAC snow dataset (Fig. 10). From Fig. 10a, it can be seen that RMSE shows a strong positive
response to elevations. But an exception can be found within the region of
3500–4500 m, where RMSE shows a clear decrease but also the greatest
spread. This occurs because the accuracy of AVHRR GAC snow is not merely
influenced by elevations. Over the flat areas (0–200 m) and hills (200–500 m), the highest density of RMSEs is distributed between 0 % and 5 %.
Over lower and medium height mountains (500–2500 m), the highest
density of RMSEs is distributed between 0 % and 10 %. With the further
increase in elevation (i.e., 2500–3500 m), more than half of the pixels
show RMSEs larger than 10 %. Nevertheless, over the elevation region of
3500–4500 m, more than half of the pixels show small RMSEs of less than
5 %. But the maximum RMSE can reach 45 % over this region. Over the
elevation region of 3500–4500 m, the highest density of RMSEs is lower
than 15 %, and the RMSE increases significantly over the extreme high area
(
Given the considerable effect of elevation on the accuracy of AVHRR GAC
snow, the regions P140-R40/41 are divided into eight groups according to
their elevations (Fig. 10b). From Fig. 10b, it can be seen that the RMSE
is rising with elevation in each individual land-cover type. Nevertheless,
an exception can be found in grasslands, which show the largest RMSE over
the region 2500–3500 m, and the RMSEs decrease significantly over the
region 3500–4500 m. Over the flat areas (0–200 m), AVHRR snow
mapping accuracy is the best in croplands and the worst in the barren class, and the
accuracy is slightly better in forest than in savannas. Moreover, the
accuracy is most spatially stable in grasslands given the centralized
distribution of RMSE. When it comes to hills (200–500 m), croplands
still show the best accuracy, followed by the forest and grasslands, and
savannas rank last. As the elevation increases to 500–1500 m, croplands
still show the best accuracy. By contrast, grasslands show the worst
accuracy. Savannas show a smaller RMSE than forests. With the further increase
in elevations (2500–3500 m), only grassland, savannas, and forests
appear. The best performance occurs in forests, followed by savannas, and
grasslands rank last. Over the high mountain area (3500–4500 m),
savannas present the largest RMSE, followed by forest, and the grasslands
show the largest spatial variations within this range. With the further
increase in elevation (
The variations in RMSE dependent on
The effect of slope on the accuracy of the AVHRR GAC snow datasets is clearly
shown in Fig. 10c. Better results tend to appear over the areas with
smaller slopes. The RMSE over different elevation regions generally shows an
increasing trend with slope. Nevertheless, there are two outliers over the
regions of 1500–2500 m and extremely high areas (
Regarding the effect of aspect (Fig. 10d), there is not a clear trend of
RMSEs with aspect over the regions lower than 5000 m. Nevertheless, over the
areas higher than 5500 m, the RMSEs first show a clear decreasing trend and
then a clear increasing trend when aspect changes from the north-facing slope to the
south-facing slope, and vice versa. Moreover, the maximum RMSE can even
reach 70 % over the south-facing slope, which is larger than that of the north-facing slope (
From Fig. 10e, it can be found that there is only small topographical
variability over the regions with low elevations (
From the results above, we can conclude that the accuracy of AVHRR GAC snow is closely related to elevations, slopes, and topographical variability, and the negative influence of these factors on snow mapping accuracy is more significant over regions with high elevations. The effect of aspect can be ignored over the regions lower than 5500 m, but for the areas higher than 5500 m, the accuracy first increases and then decreases gradually from the north-facing slope to the south-facing slope, and vice versa. The effect of land-cover type on snow mapping accuracy is related to elevations.
In this study, the ESA CCI
Validated against in situ station observations, the overall ACC of the AVHRR raw snow
dataset was about 94 %, which is the same as for MOD10A1. The use of a
temporal filter caused a slight reduction in ACC of the AVHRR gap-filled snow
dataset, with overall values around 92 %. AVHRR GAC raw snow is slightly
underestimated, with the bias of 0.94. Based on the observations of all in situ
sites, we obtain HSS
Regarding the temporal consistency of the AVHRR GAC snow datasets, the sensor-to-sensor consistency was found to differ slightly and unsystematically in ACC and Bias throughout the time series. While the consistent slight increasing trend of HSS is noteworthy, it is important to point out that the different performance of the AVHRR GAC snow datasets in different months is mainly caused by the variable amount of snow. Particularly, the performance of AVHRR GAC snow is worst in January and best in October regarding the magnitude of ACC and HSS, but when the temporal stability of accuracy was considered, it performs best in November and worst in January and December regarding the ACC, while that of HSS is worst in December. The results of Bias provide different perspectives for the performance of AVHRR GAC snow. It generally overestimates snow in February, March, October, and November, which is strongly linked to the patchiness of the snow cover that is not captured by the in situ data. By contrast, unbiased estimation is likely to occur in December and January when the snow cover is most continuous over greater areas.
The validation results with two independent reference datasets (i.e., in situ and
Landsat) both show considerable spatial variabilities, indicating the effect
of other factors (e.g., SD, FSC, land-cover type, elevation, slope, aspect,
and topographical variability). Generally, in snow-covered situations, the
accuracy of satellite snow datasets increases with increasing SD and FSC. By
contrast, in snow-free conditions, accuracy decreases with increasing SD and
FSC. Furthermore, the accuracy of AVHRR GAC snow is closely related to
elevations, slopes, and topographical variability, and the negative
influence of these factors on snow mapping accuracy is more significant over
regions with high elevations. The RMSE over different elevation regions
generally shows an increasing trend with slope. The effect of aspect can be
ignored over the regions lower than 5500 m, but over the areas higher than
5500 m, the accuracy first increases and then decreases gradually from the north-facing slope to the south-facing slope, and vice versa. The effect of land-cover
type on snow mapping accuracy is related to elevations. Its accuracy is
generally good in croplands since it is distributed only within the region
of
When it comes to the performance relative to the MODIS snow products, AVHRR raw
snow is comparable with MODIS snow when ACC and HSS are focused.
Nevertheless, it shows distinct advantages over the MODIS snow product focusing
on Bias. Regarding the temporal and spatial behaviors, different results
appear in the two dimensions. In the temporal dimension, the AVHRR snow datasets
display a more stable behavior regarding the ACC but less stable regarding
the HSS than the MODIS snow products, but in the spatial dimension, the AVHRR snow
datasets show a comparable spatial variability in accuracy but a smaller
spatial variability in HSS and Bias than the MODIS snow products. The absolute
differences between the AVHRR GAC and MODIS snow datasets were still reasonable,
with the overall RMSE of 12.8 % and 17.0 %, mBias of 0.06 % and
0.94 %, and
This study represents the first validation of the unique daily AVHRR GAC snow extent spanning 4 decades over the HKH region.
Although the reference datasets (i.e., in situ sites, high-resolution satellite data) have their own limitations and flaws, our results still encourage the compilation of a consistent, complete, long time series snow extent dataset from historical AVHRR GAC data. This study characterizes the product performance with distinct accuracy parameters from different perspectives and thus contributes to the ongoing efforts to improve the performance of existing snow products by enhancing our knowledge of the thematic and absolute accuracy of current products.
The in situ snow depth data are provided by the China Meteorology Administration
(CMA) (
XW was responsible for the main research ideas and writing the manuscript. KN, VP, CM, DM, and JW contributed to the data collection. SW contributed to the manuscript organization. All the authors thoroughly reviewed and edited this paper.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The authors are grateful to the ESA CCI (Climate Change Initiative) cloud
project team for making the datasets available for this study. The in situ data were
provided by the China Meteorology Administration (CMA) (
This research has been supported by the National Natural Science Foundation of China (grant no. 42071296). This work was jointly supported by the National Natural Science Foundation of China (grant no. 41801226) and the European Space Agency (ESA) Snow Climate Initiative (CCI+) project (grant no. 4000124098/18/I-NB).
This paper was edited by Alexandre Langlois and reviewed by three anonymous referees.