Snow strategic science plan

Vuyovich, Carrie; Deeb, E. J.; Polashenski, C.; Courville, Z.; Hiemstra, Christopher A.; Wagner, A. M.; Eylander, John; Davis, Robert E.

doi:10.21079/11681/29554

Cited by 5 publications

(3 citation statements)

References 197 publications

(222 reference statements)

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“…The strategic plan uses a combination of observations, remote sensing, and numerical modeling. A broad set of snow science collaborators at other national agencies, including NASA, a number of universities, and the USAF 16th Weather Squadron, evaluated the draft science plan (Vuyovich et al 2018).…”

Section: Terrain Analysis Numerical Modeling Testbedmentioning

confidence: 99%

Army Terrestrial Environmental Modeling and Intelligence System, ARTEMIS

Eylander¹,

Shoop²,

Deeb³

et al. 2020

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The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engineering and environmental challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the Department of Defense, civilian agencies, and our nation's public good. Find out more at www.erdc.usace.army.mil. To search for other technical reports published by ERDC, visit the ERDC online library at http://acwc.sdp.sirsi.net/client/default.

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Section: Terrain Analysis Numerical Modeling Testbedmentioning

confidence: 99%

Army Terrestrial Environmental Modeling and Intelligence System, ARTEMIS

Eylander¹,

Shoop²,

Deeb³

et al. 2020

Self Cite

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“…Mountain snowpacks are challenging for remote sensing because they change rapidly. Moderate-resolution sensors such as MODIS and VIIRS image Earth daily but at resolutions (463-750 m) that cannot resolve slope-scale features of interest to a variety of scientific users ranging from hydrologists (Blöschl, 1999) to the military (Vuyovich et al, 2018) to wildlife biologists (Conner et al, 2018). Finer-resolution multispectral sensors such as Landsat 8 and 9 provide spatial resolutions of 30 m but at 16 d revisits, during which time the snow cover can change considerably.…”

Section: Introductionmentioning

confidence: 99%

How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?

Bair¹,

Dozier²,

Rittger³

et al. 2023

The Cryosphere

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Abstract. Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: (1) a fused MODIS–Landsat product with daily 30 m spatial resolution and (2) a harmonized Landsat 8 and Sentinel 2A and B (HLS) product with 3–4 d temporal and 30 m spatial resolution. State-of-the-art spectral unmixing techniques are applied to surface reflectance products from 1 and 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size) but greater mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts, e.g., basin boundary delineation. Other explanations are (1) the importance of daily acquisitions and (2) the limitations of downscaled forcings for reconstruction. Conclusions are as follows: (1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models and (2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations, but existing moderate-resolution sensors still offer value.

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“…Mountain snowpacks are challenging for remote sensing because they change rapidly. Moderate resolution sensors such as MODIS and VIIRS image Earth daily, but at resolutions (463 m -750 m) that cannot resolve slope scale features of interest to a variety of scientific users ranging from hydrologists (Blöschl, 1999), to the military (Vuyovich et al, 2018), to wildlife that 100 m spatial resolution is needed to accurately simulate snow melt. Rittger et al (2021) used a random forest to fuse spectrally unmixed snow cover from MODIS with Landsat 5 and 7 ETM+.…”

Section: Introductionmentioning

confidence: 99%

Does higher spatial resolution improve snow estimates?

Bair

Dozier

Rittger

et al. 2022

Preprint

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Abstract. Given the tradeoffs between spatial and temporal resolution, questions about resolution optimality are fundamental to the study of global snow. Answers to these questions will inform future scientific priorities and mission specifications. Heterogeneity of mountain snowpacks drives a need for daily snow cover mapping at the slope scale (≤ 30 m) that is unmet for a variety of scientific users, ranging from hydrologists to the military to wildlife biologists. But finer spatial resolution usually requires coarser temporal or spectral resolution. Thus, no single sensor can meet all these needs. Recently, constellations of satellites and fusion techniques have made noteworthy progress. The efficacy of two such recent advances is examined: 1) a fused MODIS - Landsat product with daily 30 m spatial resolution; and 2) a harmonized Landsat 8 - Sentinel 2A/B (HLS) product with 2–3 day temporal and 30 m spatial resolution. State-of-art spectral unmixing techniques are applied to surface reflectance products from 1 & 2 to create snow cover and albedo maps. Then an energy balance model was run to reconstruct snow water equivalent (SWE). For validation, lidar-based Airborne Snow Observatory SWE estimates were used. Results show that reconstructed SWE forced with 30 m resolution snow cover has lower bias, a measure of basin-wide accuracy, than the baseline case using MODIS (463 m cell size), but higher mean absolute error, a measure of per-pixel accuracy. However, the differences in errors may be within uncertainties from scaling artifacts e.g., basin boundary delineation. Other explanations are 1) the importance of daily acquisitions and 2) the limitations of downscaled forcings for reconstruction. Conclusions are: 1) spectrally unmixed snow cover and snow albedo from MODIS continue to provide accurate forcings for snow models; and 2) finer spatial and temporal resolution through sensor design, fusion techniques, and satellite constellations are the future for Earth observations.

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Snow strategic science plan

Cited by 5 publications

References 197 publications

Army Terrestrial Environmental Modeling and Intelligence System, ARTEMIS

Army Terrestrial Environmental Modeling and Intelligence System, ARTEMIS

How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?

Does higher spatial resolution improve snow estimates?

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