Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

Muhuri, Arnab; Gascoin, Simon; Menzel, Lucas; Kostadinov, T. S.; Harpold, A. A.; Sanmiguel‐Vallelado, Alba; López‐Moreno, Juan I.

doi:10.1109/jstars.2021.3089655

Cited by 49 publications

(28 citation statements)

References 74 publications

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“…Recent studies show that the MODIS snow cover product overestimates the snow cover areas for the Mount Everest region, with the absolute error ranging from 20.1 % to 55.7 %, whereas the improved algorithm estimates the snow cover for the HKH region with an absolute accuracy greater than 90 % . The cloud cover and topographic shading in the mountainous regions are known to be the major factors affecting the accuracy of snow cover products (Muhuri et al, 2021). Parajka et al (2010) developed and validated a regional snow line method (SNOWL) for estimating snow cover from the MODIS daily product, especially during cloudy conditions (up to 90 %) over Austria.…”

Section: Reported Analysis Of Modis Snow Cover Datamentioning

confidence: 99%

Snow cover variability and trend over the Hindu Kush Himalayan region using MODIS and SRTM data

et al. 2022

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Abstract. Snow cover changes have a direct bearing on the regional and global energy and water cycles and the change in the Earth's climate conditions. We studied the relatively long-term (2000–2017) altitudinal spatiotemporal changes in the coverage of snow and glaciers in one of the world's largest mountainous regions, the Hindu Kush Himalayan (HKH) region, including Tibet, using remote sensing data (5 km grid resolution) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite. This dataset provided a unique opportunity to study zonal and hypsographic changes in the intra-annual (accumulating season and melting season) and interannual variations in snow and glacial cover over the HKH region. The zonal and altitudinal (hypsographic) analyses were carried out for the melting season and accumulating season. The altitude-wise linear trend analysis (Pearson's) of snow cover, shown as a hypsographic curve, clearly indicates a major decline in snow cover (average of 5 % or more at 100 m interval aggregates) between 4000–4500 and 5500–6000 m altitudes, which is consistent with the median trend (Theil–Sen – TS) and the monotonic trend (Mann–Kendall – MK; statistics) analysis. This analysis also revealed the regions and altitudes where major and statistically significant increases (10 % to 30 %) or decreases (−10 % to −30 %) in snow cover are identified. The extrapolation of the altitude-wise linear trend shows that it may take between ∼ 74 and 7900 years, for 3001–6000 and 6000–7000 m altitude zones respectively, for mean snow cover to decline approximately 25 % in the HKH. More detailed analysis based on longer observational records and model simulations is warranted to better understand the underlying factors, processes, and feedbacks that affect the dynamic of snow cover in HKH. These preliminary results suggest a need for continued monitoring of this highly sensitive region to climate variability and change that depends on snow as a major source of freshwater for all human activities.

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Section: Reported Analysis Of Modis Snow Cover Datamentioning

confidence: 99%

Snow cover variability and trend over the Hindu Kush Himalayan region using MODIS and SRTM data

et al. 2022

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“…We have used unsupervised classification technique to classify the surface features because of the large study area [87]. A confusion or error matrix and Kappa coefficient (K) has been used to perform the accuracy assessment [88]. A set of 261 random points were created for accuracy assessment, which were then overlaid on the Google Earth image for cross checking the unsupervised classification result with the actual ground feature.…”

Section: Land Cover and Usementioning

confidence: 99%

Modelling Permafrost Distribution in Western Himalaya Using Remote Sensing and Field Observations

et al. 2021

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The presence and extent of permafrost in the Himalaya, which is a vital component of the cryosphere, remains severely under-researched with its future climatic-driven trajectory only partly understood and the future consequences on high-altitude ecosystem tentatively sketched out. Previous studies and available permafrost maps for the Himalaya relied primarily upon the modelled meteorological inputs to further model the likelihood of permafrost. Here, as a maiden attempt, we have quantified the distribution of permafrost at 30 m grid-resolution in the Western Himalaya using observations from multisource satellite datasets for estimating input parameters, namely temperature, potential incoming solar radiation (PISR), slope, aspect and land use, and cover. The results have been compared to previous studies and have been validated through field investigations and geomorphological proxies associated with permafrost presence. A large part of the study area is barren land (~69%) due to its extremely resistive climate condition with ~62% of the total area having a mean annual air temperature of (MAAT) <1 °C. There is a high inter-annual variability indicated by varying standard deviation (1–3 °C) associated with MAAT with low standard deviation in southern part of the study area indicating low variations in areas with high temperatures and vice-versa. The majority of the study area is northerly (~36%) and southerly (~38%) oriented, receiving PISR between 1 and 2.5 MW/m2. The analysis of permafrost distribution using biennial mean air temperature (BMAT) for 2002-04 to 2018-20 suggests that the ~25% of the total study area has continuous permafrost, ~35% has discontinuous permafrost, ~1.5% has sporadic permafrost, and ~39% has no permafrost presence. The temporal analysis of permafrost distribution indicates a significant decrease in the permafrost cover in general and discontinuous permafrost in particular, from 2002-04 to 2018-20, with a loss of around 3% for the total area (~8340.48 km2). The present study will serve as an analogue for future permafrost studies to help understand the permafrost dynamics associated with the effects of the recent abrupt rise in temperature and change in precipitation pattern in the region.

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“…As shown in Figure 3, the Landsat-7 and Sentinel-2 derived velocities have a measurement error of −3.4 ± 11.6 m yr −1 and 0.4 ± 4.6 m yr −1 , respectively. Since all the image pairs are centred around December with little interannual fluctuations, the uncertainty depends largely on radiometric quality and image resolution, indicating that Sentinel-2 imagery has a better radiometric quality than Landsat-7 imagery [21], and higher resolution improves pixel matching [20], hence yielding more consistent results. 2.…”

Section: Methodsmentioning

confidence: 99%

“…We first derived glacier velocities for two periods, early winter in 1999-2000 and 2017-2018, using Landsat-7 and Sentinel-2 image pairs, respectively. High-resolution satellites provide a powerful tool for monitoring ice and snow [19,20]. We chose Sentinel-2 over Landsat-7 for mapping the present glacier motion because it has been tested to have a better geometric and radiometric quality [21].…”

Section: Introductionmentioning

confidence: 99%

Glacier Velocity Changes in the Himalayas in Relation to Ice Mass Balance

2021

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Glacier evolution with time provides important information about climate variability. Here, we investigated glacier velocity changes in the Himalayas and analysed the patterns of glacier flow. We collected 220 scenes of Landsat-7 panchromatic images between 1999 and 2000, and Sentinel-2 panchromatic images between 2017 and 2018, to calculate surface velocities of 36,722 glaciers during these two periods. We then derived velocity changes between 1999 and 2018 for the early winter period, based on which we performed a detailed analysis of motion of each individual glacier, and noted that the changes are spatially heterogeneous. Of all the glaciers, 32% have sped up, 24.5% have slowed down, and the rest 43.5% have remained stable. The amplitude of glacier slowdown, as a result of glacier mass loss, is significantly larger than that of speedup. At regional scales, we found that glacier surface velocity in winter has uniformly decreased in the western part of the Himalayas between 1999 and 2018, while increased in the eastern part; this contrasting difference may be associated with decadal changes in accumulation and/or melting under different climatic regimes. We also found that the overall trend of surface velocity exhibits seasonal variability: summer velocity changes are positively correlated with mass loss, i.e., velocity increases with increasing mass loss, whereas winter velocity changes show a negative correlation. Our study suggests that glacier velocity changes in the Himalayas are spatially and temporally heterogeneous, in agreement with studies that previously highlighted this trend, emphasising complex interactions between glacier dynamics and environmental forcing.

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Performance Assessment of Optical Satellite-Based Operational Snow Cover Monitoring Algorithms in Forested Landscapes

Cited by 49 publications

References 74 publications

Snow cover variability and trend over the Hindu Kush Himalayan region using MODIS and SRTM data

Snow cover variability and trend over the Hindu Kush Himalayan region using MODIS and SRTM data

Modelling Permafrost Distribution in Western Himalaya Using Remote Sensing and Field Observations

Glacier Velocity Changes in the Himalayas in Relation to Ice Mass Balance

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