Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021

Tang, Zilong; Deng, Gang; Hu, Guojie; Pan, Hongyu; Sang, Guoqing

doi:10.1016/j.jhydrol.2022.128438

Cited by 64 publications

(55 citation statements)

References 84 publications

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“…There are ten soil types in this study area, including Arenosols, Anthrosols, Cambisols, Fluvisols, Gleysols, Gypsisols, Leptosols, Phaeozems, Regosols, and Solonchaks. SDD data were extracted from daily cloud-free MODIS NDSI and snow phenology dataset over High Mountain Asia (2000-2021) [48,49]. Details about the datasets can be seen in Table 1.…”

Section: Reanalysis Datasetsmentioning

confidence: 99%

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

Zhao

Chen

et al. 2022

Remote Sensing

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The land surface temperature obtained from remote sensing was widely used in the simulation of permafrost mapping instead of air temperature with the rapid development of remote sensing technology. The land surface freezing and thawing index (LFI and LTI), which is commonly regarded as the ground surface freezing and thawing index (GFI and GTI), can produce certain errors in the simulation of permafrost distribution on the Qinghai–Tibet Plateau. This paper improved the accuracy of the thermal condition of the surface soil in the Qinghai–Tibet Engineering Corridor (QTEC) by calculating the LFI (or LTI) and N-factors. The environmental factors affecting the spatial distribution of the GFI and GTI were detected by the GeoDetector model. Finally, the multiple linear relationships between the GFI (or GTI) and the environmental factors were established. The results from 25 monitoring sites in the QTEC show that the Nf (ratio of GFI to LFI) is 1.088, and the Nt (ratio of GTI to LTI) is 0.554. The explanatory power of the interaction between elevation and latitude for the GFI and GTI is 79.3% and 85.6%, respectively. The multiple linear regression model with six explanatory variables established by GFI (or GTI) has good accuracy. This study can provide relatively accurate upper boundary conditions for the simulation of permafrost distribution in the QTEC region.

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Section: Reanalysis Datasetsmentioning

confidence: 99%

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

Zhao

Chen

et al. 2022

Remote Sensing

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“…A Landsat study in the South American Andes [45] found significant declines in austral summer snow cover extent over the study period of 1986-2018. There have been numerous MODIS satellite studies in the High Mountain Area of Asia, these showing strong spatial and temporal heterogeneity in changes in snow cover extent [70][71][72][73].…”

Section: Discussionmentioning

confidence: 99%

Decline of Late Spring and Summer Snow Cover in the Scottish Highlands from 1984 to 2022: A Landsat Time Series

Spracklen

2023

Remote Sensing

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Late spring and summer snow cover, the remnants of winter and early spring snowfall, not only possess an intrinsic importance for montane flora and fauna, but also act as a sensitive indicator for climate change. The variability and potential trends in late spring and summer (snowmelt season) snow cover in mountain regions are often poorly documented. May to mid-September Landsat imagery from 1984 to 2022 was used to quantify changes in the snow-covered area of upland regions in the Scottish Highlands. There was substantial annual variability in the area of May to mid-September snow cover combined with a significant decline over the 39-year study period (p = 0.02). Long-term climate data used to show variability in May to mid-September snow cover was positively related to winter snowfall and negatively related to winter and April temperatures. The results from a long-running field survey counting the number of snow patches that survive until the following winter were used to check the veracity of the study. Further, accuracy was estimated through comparison with higher resolution Sentinel-2 imagery, giving a user and producer accuracy rate of 99.8% and 87%, respectively. Projected future warming will further diminish this scarce, valuable habitat, along with its associated plant communities, thus threatening the biodiversity and scenic value of the Scottish Highlands.

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“…The growing season differences can be attributed to the increased water demand for vegetation growth in the peak growing season (Chen et al, 2019), thus showing more sensitivity to SWE. In contrast, the significance of SCED to changes in GPP was relatively small and its spatial distribution was much more fragmented, largely due to the great spatial heterogeneity and instability of snow cover (Tang et al, 2022), coupled with vegetation growth influenced by various non-snow factors on the TP (Piao et al, 2012).…”

Section: Multiple Impacts Of Snow Cover On Gppmentioning

confidence: 99%

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

Liu,

Xiao,

Zhang

et al. 2023

Ecosphere

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Snow cover is an important control element in the Tibetan Plateau (TP) ecosystem; however, the impact of snow cover changes on gross primary productivity (GPP) is largely unknown, particularly under complex geographical conditions. In this study, we investigated the impacts of snow cover changes on different seasonal GPP and their mechanisms in different geographical zones using multisource remote sensing data from 1982 to 2018. Snow cover significantly affected GPP by nearly 15% of the TP region, and snow cover days (SCDs) were the dominant snow cover indicators affecting GPP variations compared with snow water equivalent (SWE) and snow cover end date (SCED). In general, an increase in snow cover leads to a significant increase in GPP in regions with low precipitation and temperature, but limits the accumulation of GPP in humid and warm regions. Furthermore, from the humid to the arid zone, the “moisture effect” of snow cover (by altering soil moisture) plays an increasingly important role in regulating GPP variations with increasing drought levels. This study elucidates the importance of snow cover in regulating different seasonal GPP variations and significantly improves our insight into the response of vegetation carbon uptake to snow cover changes in the TP.

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Satellite observed spatiotemporal variability of snow cover and snow phenology over high mountain Asia from 2002 to 2021

Cited by 64 publications

References 84 publications

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

Ground Surface Freezing and Thawing Index Distribution in the Qinghai-Tibet Engineering Corridor and Factors Analysis Based on GeoDetector Technique

Decline of Late Spring and Summer Snow Cover in the Scottish Highlands from 1984 to 2022: A Landsat Time Series

Increased snow cover enhances gross primary productivity in cold and dry regions of the Tibetan Plateau

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