Remote sensing of snow extent

Hall, Dorothy K.; Frei, Allan; Déry, Stephen J.

doi:10.1002/9781118368909.ch3

Cited by 8 publications

(3 citation statements)

References 69 publications

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“…Over the past decades, a number of studies have advanced our understanding of spatiotemporal changes in snow cover extent (e.g., refs. [4][5][6][7][8][9][10][11]; however, snow cover observations do not directly provide information about changes in the amount of SWE and the water storage in the snowpack. Despite significant progress in the remote sensing of snow, many challenges remain when estimating the distribution of SWE both in the Northern Hemisphere, where an estimated 98% of the global snow cover occurs (12), as well as globally (13)(14)(15)(16)(17).…”

mentioning

confidence: 99%

Global snow drought hot spots and characteristics

Huning

AghaKouchak

2020

Proc. Natl. Acad. Sci. U.S.A.

131

114

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Snow plays a fundamental role in global water resources, climate, and biogeochemical processes; however, no global snow drought assessments currently exist. Changes in the duration and intensity of droughts can significantly impact ecosystems, food and water security, agriculture, hydropower, and the socioeconomics of a region. We characterize the duration and intensity of snow droughts (snow water equivalent deficits) worldwide and differences in their distributions over 1980 to 2018. We find that snow droughts became more prevalent, intensified, and lengthened across the western United States (WUS). Eastern Russia, Europe, and the WUS emerged as hot spots for snow droughts, experiencing ∼2, 16, and 28% longer snow drought durations, respectively, in the latter half of 1980 to 2018. In this second half of the record, these regions exhibited a higher probability (relative to the first half of the record) of having a snow drought exceed the average intensity from the first period by 3, 4, and 15%. The Hindu Kush and Central Asia, extratropical Andes, greater Himalayas, and Patagonia, however, experienced decreases (percent changes) in the average snow drought duration (−4, −7, −8, and −16%, respectively). Although we do not attempt to separate natural and human influences with a detailed attribution analysis, we discuss some relevant physical processes (e.g., Arctic amplification and polar vortex movement) that likely contribute to observed changes in snow drought characteristics. We also demonstrate how our framework can facilitate drought monitoring and assessment by examining two snow deficits that posed large socioeconomic challenges in the WUS (2014/2015) and Afghanistan (2017/2018).

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mentioning

confidence: 99%

Global snow drought hot spots and characteristics

Huning

AghaKouchak

2020

Proc. Natl. Acad. Sci. U.S.A.

131

114

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“…NDSI was calculated with the observed Landsat image and used to identify areas with snow (NDSI ≥ 0.4; [45]) and without snow (NDSI < 0.4). The RMSE for STARFM predictions in snow-covered areas was compared with non-snow areas to determine if errors were larger.…”

Section: Error Analysismentioning

confidence: 99%

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

et al. 2017

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Water-limited ecosystems encompass approximately 40% of terrestrial land mass and play a critical role in modulating Earth's climate and provisioning ecosystem services to humanity. Spaceborne remote sensing is a critical tool for characterizing ecohydrologic patterns and advancing the understanding of the interactions between atmospheric forcings and ecohydrologic responses. Fine to medium scale spatial and temporal resolutions are needed to capture the spatial heterogeneity and the temporally intermittent response of these ecosystems to environmental forcings. Techniques combining complementary remote sensing datasets have been developed, but the heterogeneous nature of these regions present significant challenges. Here we investigate the capacity of one such approach, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, to map Normalized Difference Vegetation Index (NDVI) at 30 m spatial resolution and at a daily temporal resolution in an experimental watershed in southwest Idaho, USA. The Dry Creek Experimental Watershed captures an ecotone from a sagebrush steppe ecosystem to evergreen needle-leaf forests along an approximately 1000 m elevation gradient. We used STARFM to fuse NDVI retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat during the course of a growing season (April to September). Specifically we input to STARFM a pair of Landsat NDVI retrievals bracketing a sequence of daily MODIS NDVI retrievals to yield daily estimates of NDVI at resolutions of 30 m. In a suite of data denial experiments we compared these STARFM predictions against corresponding Landsat NDVI retrievals and characterized errors in predicted NDVI. We investigated how errors vary as a function of vegetation functional type and topographic aspect. We find that errors in predicting NDVI were highest during green-up and senescence and lowest during the middle of the growing season. Absolute errors were generally greatest in tree-covered portions of the watershed and lowest in locations characterized by grasses/bare ground. On average, relative errors in predicted average NDVI were greatest in grass/bare ground regions, on south-facing aspects, and at the height of the growing season. We present several ramifications revealed in this study for the use of multi-sensor remote sensing data for the study of spatiotemporal ecohydrologic patterns in dryland ecosystems.

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“…A pixel with an NDSI > 0.4 is assumed to have snow cover (binary result: snow or no snow), while a pixel with an NDSI ≤ 0.4 is snowfree [19]. This global snow cover algorithm has been subjected to several refinements over the years and different thresholds have been incorporated into its general formula [14]. Nevertheless, it is widely known that the NDSI has problems in differentiating snow from clouds [15,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Snow Cover Reconstruction in the Brunswick Peninsula, Patagonia, Derived from a Combination of the Spectral Fusion, Mixture Analysis, and Temporal Interpolation of MODIS Data

Aguirre,

Bozkurt,

Sauter

et al. 2023

Remote Sensing

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Several methods based on satellite data products are available to estimate snow cover properties, each one with its pros and cons. This work proposes and implements a novel methodology that integrates three main processes applied to MODIS satellite data for snow cover property reconstruction: (1) the increase in the spatial resolution of MODIS (MOD09) data to 250 m using a spectral fusion technique; (2) a new proposal of snow-cloud discrimination; (3) the daily spatio-temporal reconstruction of snow extent and its albedo signature using the endmembers extraction and spectral mixture analyses. The snow cover reconstruction method was applied to the Brunswick Peninsula, Chilean Patagonia, a low-elevation (<1500 m a.s.l.) mid-latitude area. The results show a 98% agreement between MODIS snow detection and ground-based snow measurements at the automatic weather station, Tres Morros (53.3174°S, 71.2790°W), with fractional snow cover values between 20% and 50%, showing a close relationship between snow and vegetation type. The number of snow days compiled from the MODIS data indicates a good performance (Pearson’s correlation of 0.9) compared with the number of skiing days at the Cerro Mirador ski center, Punta Arenas. Although the number of seasonal snow days showed a significant increasing trend of 0.54 days/year in the Brunswick Peninsula during the 2000–2020 period, a significant decrease of −4.64 days/year was detected in 2010–2020.

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Remote sensing of snow extent

Cited by 8 publications

References 69 publications

Global snow drought hot spots and characteristics

Global snow drought hot spots and characteristics

Assessing a Multi-Platform Data Fusion Technique in Capturing Spatiotemporal Dynamics of Heterogeneous Dryland Ecosystems in Topographically Complex Terrain

Snow Cover Reconstruction in the Brunswick Peninsula, Patagonia, Derived from a Combination of the Spectral Fusion, Mixture Analysis, and Temporal Interpolation of MODIS Data

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