Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

Abstract

Abstract. 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+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

“…This is consistent with the reason for the opposite trend of R 2 for different surface types. From Figure 7a,b, it can be seen that OA and R 2 show a trend of decreasing and then increasing with increasing altitude, which is consistent with existing AVHRR fractional-snow-cover studies [36]. This phenomenon occurs due to the unique geographical environment of High Mountain Asia and the date for selecting the validation images.…”

“…High Mountain Asia is a large tectonic geomorphic unit with the highest elevation in the world, and its snow and ice reserves are the largest outside the North and South Poles [36]. With a geographical range of approximately 24-40 • N and 65-105 • E [37], High Mountain Asia encompasses a cold and dry environment.…”

“…The accuracy of fractional-snow-cover retrieval algorithms is related not only to the surface type but also to the elevation, which has a significant effect on the accuracy. The snow recognition accuracy decreases to a certain extent as the elevation increases [36]. In this study, the 313 scenes used to verify Landsat-5 images were divided into four categories according to altitude: 39 scenes from less than 3000 m, 25 scenes from 3000-4000 m, 108 scenes from 4000-5000 m, and 141 scenes from more than 5000 m. As mentioned previ-ously, when selecting verification images, the set rule is that the proportion of snow-cover area in the whole image should be more than 30%, and verification data are concentrated over three winters.…”

Development and validation of a new MODIS snow-cover-extent product over China

“…Meanwhile, to more directly show whether SCE is overestimated or underestimated and to what extent, a metric termed as "bias" is introduced. The bias, defined as the ratio of pixels classified as snow to real snow pixels (Hüsler et al, 2012;Wu et al, 2021), is a relative measure to detect overestimation (values > 1) or underestimation of snow (values < 1).…”

Towards forecasting future Snow Cover Dynamics in the European Alps – The Potential of Long Optical Remote-Sensing Time Series

“…For example, the Global Snow Pack (GSP) developed by Dietz et al [18] is a global MODIS-based daily snow cover product enabling snow cover analyses at a 500 m resolution starting in the year 2000. Even longer time series of snow cover dynamics have been generated on the basis of data from the Advanced Very High Resolution Radiometer (AVHRR) [19][20][21][22]. Both sensors offer very high temporal resolutions, which makes them particularly suitable for the analysis of snow-related climatology and phenology.…”