Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China

Feng, Tianwen; Hao, Xiaohua; Wang, Jian; Luo, Siqi; Huang, Guanghui; Li, Hongyi; Zhao, Q.

doi:10.1016/j.jhydrol.2022.129006

Cited by 5 publications

(3 citation statements)

References 53 publications

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“…Thus, GPR may have increased uncertainty in its scalability due to a difficulty of repeating complicated survey designs. Lidar is scalable to coarser resolutions (e.g., 50 m; Painter et al, 2016) and TLS and dronemounted lidar (e.g., Feng et al, 2023) may be valuable tools for evaluating InSAR ΔSWE retrievals at small field sites. However, at larger scales, comprehensive airborne lidar surveys may be required to fully evaluate NISAR ΔSWE retrievals.…”

Section: Considerations For Future Evaluations Of Insar δSwe Retrievalsmentioning

confidence: 99%

Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado

Bonnell,

McGrath,

Tarricone

et al. 2024

Preprint

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Abstract. Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given current and projected climate change impacts. Synthetic Aperture Radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow and SAR interferometry (InSAR) can be used to estimate changes in SWE (ΔSWE) between SAR acquisitions. We calculated ΔSWE retrievals from 10 NASA L-band Uninhabited Aerial Vehicle SAR (UAVSAR) acquisitions in northern Colorado during the winters of 2020 and 2021 and evaluated the retrievals against measurements of SWE from ground-penetrating radar (GPR) and terrestrial lidar scans (TLS) collected as part of the NASA SnowEx 2020 and 2021 Time Series Campaigns. Next, we evaluated the full UAVSAR time series at the northern Colorado sites using SWE measured at seven automated stations and ascertained whether coherence can be used as an accuracy metric for ΔSWE retrievals. For single InSAR pairs, UAVSAR ΔSWE retrievals displayed high correlation with TLS and GPR ΔSWE retrievals (overall r = 0.72–0.79) and yielded an RMSE of 19–22 mm. When compared to SWE at seven automated stations, cumulative SWE from UAVSAR retrievals exhibited poor agreement in 2020, but high agreement in 2021. We found that SWE can be reliably retrieved, even for lower coherences, as RMSE values ranged by <10 mm from coherences of 0.10 to 0.90. The upcoming NASA-ISRO SAR satellite mission, with a 12-day revisit period, offers an exciting opportunity to apply this methodology globally, but further quantification of limitations is necessary, particularly in forested environments and as the snowpack begins to melt.

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Section: Considerations For Future Evaluations Of Insar δSwe Retrievalsmentioning

confidence: 99%

Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado

Bonnell,

McGrath,

Tarricone

et al. 2024

Preprint

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“…Most research initiatives involving the aforementioned processes are primarily based on the assessment of spectral radiation reflected by the affected regions, with a relatively small number of studies also including the quantification of light transmitted through snow cover. From a standard remote sensing point of view, it is comprehensible that more attention is given to reflected light [19][20][21][22], albeit the knowledge about factors affecting the light transmitted by target materials can be particularly relevant for challenging applications such as the remote detection of relatively thin snow layers [23,24]. From a broader scientific perspective, however, it is necessary to make inroads toward a more comprehensive understanding of the intertwined light transport mechanisms affecting snow reflectance and transmittance [1,2], notably with respect to the visible (photosynthetic) spectral domain.…”

Section: Introductionmentioning

confidence: 99%

Environmentally Induced Snow Transmittance Variations in the Photosynthetic Spectral Domain: Photobiological Implications for Subnivean Vegetation under Climate Warming Conditions

Baranoski,

Varsa

2024

Remote Sensing

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Variations in the productivity of subnivean vegetation can substantially affect the ecology of regions more susceptible to increasing warming levels and lead to significant feedback effects on the global climate. Due to its importance, this topic is at the center of a broad scope of interdisciplinary studies supported by field and remote sensing observations. However, the current knowledge about environmental factors affecting the penetration of photosynthetically active radiation (PAR) through snow is still constrained by the paucity of transmittance data. In this work, we aim to further the understanding about these interconnected processes. We conduct a systematic investigation about the effects of independent and combined changes in key nivological characteristics, namely thickness, saturation, density and grain size, on snow transmittance in the photosynthetic spectral domain. Our investigation is carried out through controlled in silico (computational) experiments supported by measured radiometric data. Its outcomes unveil fundamental quantitative and qualitative trends related to the role played by these nivological characteristics on the spectral quality of transmitted PAR, which is quantified in terms of red to blue (R/B), red to far-red (R/FR) and blue to far-red (B/FR) ratios. These trends include increases in the R/B ratio as well as decreases in the R/FR and B/FR ratios following thickness reductions or grain size increases, with opposite variations in these ratios being observed for saturation or density increases. Accordingly, the pairing of our findings with in situ and remotely collected information contributes to cement the scientific foundation required for the effective assessment of cause-effect loops linking accentuated vegetation greening to accelerated rates of snow cover recession.

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“…There may be differences in return density under snow-free and snow-cover conditions that contribute to uncertainty in snow depth measurements. For example, it was found that point cloud density may be lower for snow-covered surfaces as compared to bare surfaces [31]. In mixed vegetation landscapes, differences in a vegetated canopy's structure result in a differential in returns among vegetation types.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape

Sullivan,

Hunsaker,

Palace

et al. 2023

Remote Sensing

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Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.

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Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China

Cited by 5 publications

References 53 publications

Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado

Evaluating L-band InSAR Snow Water Equivalent Retrievals with Repeat Ground-Penetrating Radar and Terrestrial Lidar Surveys in Northern Colorado

Environmentally Induced Snow Transmittance Variations in the Photosynthetic Spectral Domain: Photobiological Implications for Subnivean Vegetation under Climate Warming Conditions

Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape

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