Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Mityók, Zoltán K.; Bolton, Douglas K.; Coops, Nicholas C.; Berman, Ethan E.; Senger, Sue

doi:10.1080/07038992.2018.1538775

Cited by 18 publications

(6 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Detecting snow with optical satellites is long established using band ratioing (Lopez et al, 2008;Rastner et al, 2014) and the Normalised Difference Snow Index (NDSI) (Gascoin et al, 2019;Salomonson and Appel, 2004). With these data in cloud computing platforms, global-scale snow cover maps are now being produced regularly at a medium spatial resolution (Dietz et al, 2015;Gascoin et al, 2019;Mityók et al, 2018). Snow cover on glaciers can be indicative of equilibrium line altitude (Rabatel et al, 2012).…”

Section: Snowmentioning

confidence: 99%

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

Taylor

Quincey

Smith

et al. 2021

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

Remote sensing technologies are integral to monitoring the mountain cryosphere in a warming world. Satellite missions and field-based platforms have transformed understanding of the processes driving changes in mountain glacier dynamics, snow cover, lake evolution, and the associated emergence of hazards (e.g. avalanches, floods, landslides). Sensors and platforms are becoming more bespoke, with innovation being driven by the commercial sector, and image repositories are more frequently open access, leading to the democratisation of data analysis and interpretation. Cloud computing, artificial intelligence, and machine learning are rapidly transforming our ability to handle this exponential increase in data. This review therefore provides a timely opportunity to synthesise current capabilities in remote sensing of the mountain cryosphere. Scientific and commercial applications were critically examined, recognising the technologies that have most advanced the discipline. Low-cost sensors can also be deployed in the field, using microprocessors and telecommunications equipment to connect mountain glaciers to stakeholders for real-time monitoring. The potential for novel automated pipelines that can process vast volumes of data is also discussed, from reimagining historical aerial imagery to produce elevation models, to automatically delineating glacier boundaries. Finally, the applications of these emerging techniques that will benefit scientific research avenues and real-world societal programmes are discussed.

show abstract

Section: Snowmentioning

confidence: 99%

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

Taylor

Quincey

Smith

et al. 2021

Progress in Physical Geography: Earth and Environment

View full text Add to dashboard Cite

show abstract

“…Optical remote sensing data such as Landsat [5], Sentinel-2 [6], and MODIS [7] boast a high degree of accuracy in snow identification and have become indispensable in snow research endeavors [8,9]. Common methods for snow identification using optical remote sensing data include the normalized difference snow index (NDSI) [10,11], band threshold segmentation, and image classification. NDSI, effectively identifying snow based on its reflectance properties in the visible spectrum, has been widely adopted.…”

Section: Introductionmentioning

confidence: 99%

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

Xi,

Mei

2023

Water

View full text Add to dashboard Cite

The Pemo Highway is a critical transportation road to Medog County in the Tibet Plateau (TP). Since its completion in 2021, the Pemo Highway has been prone to frequent avalanches due to heavy rainfall and snowfall. Despite the lack of monitoring stations along the highway and limited research conducted in this area, remote sensing imagery provides valuable data for investigating avalanche hazards along the highway. In this paper, we first investigated the spatiotemporal characteristics of snow cover along the Pemo Highway over the past two years based on the GEE platform. Second, we integrated snow, topography, meteorology, and vegetation factors to assess avalanche susceptibility in January, February, and March 2023 along the highway using the AHP method. The results reveal that the exit of the Duoshungla Tunnel is particularly susceptible to avalanches during the winter months, specifically from January to March, with a significant risk observed in March. Approximately 3.7 km in the direction of the tunnel exit to Lager is prone to avalanche hazards during this period. The recent “1.17 avalanche” event along the Pemo Highway validates the accuracy of our analysis. The findings of this paper provide timely guidance for implementing effective avalanche prevention measures on the Pemo Highway.

show abstract

“…In this context, multi-source techniques can improve the sampling time. Fusion techniques that merge LR and HR data have been proposed and provide daily snow cover maps with a resolution of 30 m [49], [50]. These methods are based on long NDSI time-series which are smoothed through interpolation techniques (e.g., cubic splines), which provide good performances if the accumulation and melting are not subjected to abrupt changes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

Premier

Marín

Steger

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

High-resolution (HR) snow cover maps derived by remotely sensed images are an asset for data assimilation in hydrological models. However, the current satellite missions do not provide daily HR multispectral observations suitable for an accurate snow monitoring in alpine environments. On the contrary, low-resolution (LR) sensors acquire daily information of snow cover fraction (SCF) but at an inappropriate spatial scale. This paper proposes a novel approach that combines multi-source and multi-scale acquisitions to infer the daily HR snow cover area (SCA) for mountainous basins. The approach builds on the assumption that inter-annual snow patterns are both affected by the local geomorphometry and meteorology. We derive these patterns through a hierarchical multi-step approach based on historical statistical analyses on a long and sparse HR time-series. At each step, we obtain HR snow cover maps with higher number of reconstructed pixels but decreasing level of confidence. Historical data are used to estimate the probability that a HR pixel is covered by snow according to two possible multi-scale strategies: i) HR gap-filling, or ii) LR downscaling. These analyses lead to the identification of the patterns that regularly appear given certain conditions. When no systematic patterns are observed, we reinforce the inference of the pixel class by a generalized additive model (GAM) that exploits not only the historical data but also explicit geomorphometric, global snow and multi-temporal properties. The proposed approach has been validated on a catchment in the Sierra Nevada, USA, for three hydrological years (2017)(2018)(2019) showing an average overall accuracy of 92%.

show abstract

Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Cited by 18 publications

References 81 publications

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

Remote sensing of the mountain cryosphere: Current capabilities and future opportunities for research

Avalanche Susceptibility Mapping by Investigating Spatiotemporal Characteristics of Snow Cover Based on Remote Sensing Imagery along the Pemo Highway—A Critical Transportation Road in Tibet, China

A Novel Approach Based on a Hierarchical Multiresolution Analysis of Optical Time Series to Reconstruct the Daily High-Resolution Snow Cover Area

Contact Info

Product

Resources

About