Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Palomaki, Ross T.; Sproles, E. A.

doi:10.1016/j.rse.2023.113744

Cited by 4 publications

(7 citation statements)

References 45 publications

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“…For the corresponding days of 21 January, 22 January, 29 January, 17 February, 18 February, 24 February, and 4 March, the contractors DJ&A reported vertical DSM uncertainties of 6.8, 7.7, 7.6, 7.9, 9.2, 6.3, and 3.9 cm, respectively. Other work at this site performed by Palomaki and Sproles (2023) comparing the DJ&A lidar from 21 January to point observations from 20 January showed uncertainties on the range of approximately 20 cm, higher than any reported in the DJ&A report. However, this uncertainty was calculated from manual snow depth observations on different, consecutive days, because the lidar flights had to be postponed due to strong winds.…”

Section: Resultsmentioning

confidence: 55%

“…Multiple sources of uncertainty apply to the lidar‐derived SWE estimates that we used to evaluate the CRNS SWE estimates. Palomaki and Sproles (2023) compared snow depth derived from the same lidar data at the CARC for one date, 21 January, to in situ snow depth transect measurements and found lidar RMSE of approximately 20 cm, higher than any RMSEs reported by DJ&A. The ground truth snow depth values used by Palomaki and Sproles (2023) were collected one day before the lidar flight (20 January vs. 21 January, respectively) because the UAV flights were postponed due to high winds.…”

Section: Discussionmentioning

confidence: 99%

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Evaluating Cosmic Ray Neutron Sensor Estimates of Snow Water Equivalent in a Prairie Environment Using UAV Lidar

Woodley,

Kim,

Sproles

et al. 2024

Water Resources Research

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Monitoring snow cover in prairie environments is important for understanding water and energy fluxes, agricultural production, and flooding, but difficult due to shallow snowpack and considerable snow heterogeneity. Cosmic ray neutron sensors (CRNS) are sensitive to snow within a radius of 150–250 m, which allows for continuous estimation of snow water equivalent (SWE) over a large footprint and may better represent area‐averaged snow cover in prairies than conventional SWE instruments, such as snow pillows. A CRNS was installed at Montana State University's Central Agricultural Research Center (CARC; 47.06°, −109.95°) in Moccasin, MT in coordination with NASA's SnowEx 2021 field campaign. This work assesses the feasibility of a CRNS for SWE monitoring in prairies by comparing CRNS SWE estimates to spatially distributed SWE derived from uninhabited aerial vehicle lidar snow depths within the sensor's footprint and manual snow pit measurements. Lidar observations show snow cover was highly spatially variable, with the largest snow accumulation near barriers and the least in barren fields. Additionally, we evaluate our CRNS SWE estimates using Ultra Rapid Neutron Only Simulation (URANOS) Monte Carlo simulations. Comparisons of SWE estimates derived from lidar, CRNS, and URANOS for shallow snowpack at the site yielded root mean square values of about 2 mm (approximately 30% of the mean SWE). These results suggest that the CRNS is effective at integrating over significant spatial variability within its footprint at this site. However, the spatial distribution of snow exerts a strong influence on the CRNS signal and must be considered when interpreting CRNS observations.

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Section: Resultsmentioning

confidence: 55%

Section: Discussionmentioning

confidence: 99%

Evaluating Cosmic Ray Neutron Sensor Estimates of Snow Water Equivalent in a Prairie Environment Using UAV Lidar

Woodley,

Kim,

Sproles

et al. 2024

Water Resources Research

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“…Previous studies have established that UAVSAR ΔSWE retrievals resemble the spatial patterns of lidar-derived ΔSWE retrievals, but differences between the two datasets were not systematic (Marshall et al, 2021;Palomaki and Sproles, 2023). Marshall et al (2021) evaluated UAVSAR ΔSWE retrievals over a 4 km 2 relatively flat and non-forested region of Grand Mesa, Colorado using airborne lidar and found very low error for the technique (RMSE = 9 mm).…”

Section: Accuracy Of L-band Insar δSwe Retrievalsmentioning

confidence: 99%

“…Recent UAVSAR studies (Hoppinen et al, 2023;Marshall et al, 2021;Palomaki and Sproles, 2023;Tarricone et al, 2023), including this study, have largely focused on ΔSWE retrievals in open environments. We found that ΔSWE retrievals were 66% higher on average in the open areas around the MR field site than below forest cover.…”

Section: Remaining Questions For the L-band Insar δSwe Retrieval Tech...mentioning

confidence: 99%

“…CC BY 4.0 License. the technique has been tested for multi-year, season-long ΔSWE retrievals from a tower mounted platform in Finland at Ku-, X-, C-, and L-band frequencies (Leinss et al, 2015;Ruiz et al, 2022), by several studies emphasizing one or two interferometric pairs (Conde et al, 2019;Marshall et al, 2021;Nagler et al, 2022;Palomaki and Sproles, 2023;Tarricone et al, 2023), and by two season-long studies that used a time series of interferometric pairs (Hoppinen et al, 2023;Oveisgharan et al, 2023). In general, these studies have found that InSAR ΔSWE retrievals are highly correlated with in situ measurements, but accuracy has varied on a case-by-case basis, and in situ measurements for validation have been few in number.…”

Section: Introductionmentioning

confidence: 99%

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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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Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

Hakim,

Sakina,

Fadhillah

et al. 2024

Geosci J

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Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack

Cited by 4 publications

References 45 publications

Evaluating Cosmic Ray Neutron Sensor Estimates of Snow Water Equivalent in a Prairie Environment Using UAV Lidar

Evaluating Cosmic Ray Neutron Sensor Estimates of Snow Water Equivalent in a Prairie Environment Using UAV Lidar

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

Monitoring activity in Mount Melbourne, Antarctica, by multi-temporal SAR interferometry based on the ICOPS algorithm

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