Assessment of wet snow dynamics on the territory of Urupema, Brazil based on SAR and optical satellite data

Spasova, Temenuzhka; Nedkov, Roumen

doi:10.1117/12.2533684

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…In practice the NDSI values for areas covered by snow range from 0.0 to 1.0 when snow is present and registered by the sensor the NDSI will be > 0 3 . Generally accepted NDSI threshold value used to determine snow cover is 0.4, although many researchers suggest the NDSI can be set for particular situation with methods of threshold selection based on visual inspection/interpretation, empirical relationship or automated selection 4,5,6,7 .…”

Section: Normalized Difference Snow Index (Ndsi)mentioning

confidence: 99%

Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria

Stoyanov,

Avetisyan

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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The present study aims to monitor the Snow Cover Extent (SCE) of the mountainous region of Bulgaria (13905 km2), located 1000m above sea level, for eight years. Information is important for calculation of Snow Water Equivalent (SWE), hydrological runoff modeling, forecasting, and assessing flood events. Global Warming and Climate Change and their impacts, such as a constant increase in recorded high-temperature levels, frequent droughts, water scarcity in the summers, and less-snow winters, have a significant effect on agriculture, hydrology, forests, and ecology in Bulgaria. The present research uses the available cloudless optical data of Sentinel-2-MSI for snow cover monitoring concerning the decrease in snow distribution during the last decade. Sentinel-2 satellite imagery, from October to May, for the period between 2016 and 2023, was generated and exported from Google Earth Engine (GEE). Normalized Differential Snow Index (NDSI) and Snow Water Index (SWI) were calculated, and the resulting output indices rasters were post-processed and inspected additionally to obtain thresholding classifications, masking out the areas covered by shadows (topographic), water bodies, forests, etc., and snow cover area distribution. The results obtained in the study can be used and integrated for climate change observations and research at the local and regional levels.

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Section: Normalized Difference Snow Index (Ndsi)mentioning

confidence: 99%

Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria

Stoyanov,

Avetisyan

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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“…Spectral indices, derived from satellite data, are the most frequently applied methods for snow/ice cover mapping using Remote sensing technology. There are numerous spectral indices that can provide information about the snow cover distribution [5][6][7][8][9] . These indices utilize the reflectance and absorptive characteristics of snow in the visible, NIR, and SWIR bands.…”

Section: Introductionmentioning

confidence: 99%

Assessment of elevation and slope exposure impact on snow cover distribution in the mountainous region in Bulgaria using Sentinel-2 satellite data

Avetisyan,

Stoyanov

2023

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXV

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Snow cover is among the most important features of the Earth's surface and a crucial element of the cryosphere that affects the global energy balance, water, and carbon cycles. Accurate monitoring of this land surface component is of particular significance as snowmelt provides between 50%–80% of the annual runoff in the temperate (boreal) regions and significantly impacts the hydrological balance during the warm season. Limited reserves of soil moisture during the winter period can lead to all types of droughts, including green-water drought, which is expressed by reduced water storage in soil and vegetation. Green-water drought causes a variable effect across landscape components, on the functions and ecosystem services (ES) they provide. The present study aims to track the snow cover dynamics in the transitional seasons of the year when the snow cover is most unstable and to differentiate its territorial distribution depending on elevation and slope exposure. The study area covers the mountainous territories of Bulgaria and the seasons from 2016 to 2022. To achieve the aim of the study, we used Sentinel-2 images and calculated the Snow Water Index (SWI). SWI uses spectral characteristics of the visible, shortwave infrared (SWIR), and near-infrared (NIR) bands to distinguish snow and ice pixels from other pixels, including water bodies which is crucial for the accurate monitoring of snow cover dynamics. The obtained results were validated using VHR images for pre-selected test areas.

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Assessment of wet snow dynamics on the territory of Urupema, Brazil based on SAR and optical satellite data

Cited by 2 publications

References 3 publications

Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria

Application of optical data from Sentinel-2-MSI for snow cover monitoring on the territory of the mountainous region of Bulgaria

Assessment of elevation and slope exposure impact on snow cover distribution in the mountainous region in Bulgaria using Sentinel-2 satellite data

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