S. Oveisgharan, Robert Zinke, Zachary Hoppinen, Hans Peter Marshall
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The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. 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Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. 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引用次数: 2
摘要
摘要雪水当量(SWE)被认为是影响河流流量和水循环的关键雪层要素。主动式和被动式微波遥感方法都被用来检索雪水当量,但目前还没有一种雪水当量产品能为山区地形提供有用的估算。有源传感器可提供更高分辨率的观测数据,但直到最近才有合适的雷达频率和时间重复间隔。干涉合成孔径雷达 (InSAR) 已被证明具有估算 SWE 变化的潜力。在本研究中,我们将这一技术应用于 2020-2021 年冬季的 6 天时间重复哨兵-1 C 波段长时间序列数据。检索结果表明,无论从时间上还是从空间上,SWE 都与独立的原地测量值有显著的统计相关性。在整个时间序列和所有原位站中,SWE 变化测量值在 -5.3 到 9.4 厘米之间。检索到的 SWE 变化观测值与原位站测量值之间的皮尔逊相关性和均方根误差分别为 0.8 厘米和 0.93 厘米。在整个 2020-2021 年时间序列中,9 个原地站的 SWE 误差均小于 2 厘米。此外,使用哨兵 1 号数据检索的西南降水量与激光雷达雪深数据相关性良好,相关性超过 0.47。低时间一致性被认为是降低使用 InSAR 数据检索 SWE 性能的主要原因。我们还发现,在低时间相干性区域,相位解包算法的性能也会下降。更高的频率(如 L 波段)可改善时间一致性和 SWE 模糊性。使用 C 波段哨兵-1 数据进行 SWE 检索证明是成功的,但需要更快的重访以避免低时间相干性。即将发布的 L 波段 12 d 重复通量的 NASA-ISRO 合成孔径雷达(NISAR)数据和未来的 L 波段 6 d 重复通量的欧洲雷达观测系统(ROSE-L)数据将为利用雷达干涉测量进行全球 SWE 检索提供绝佳机会。
Snow water equivalent retrieval over Idaho – Part 1: Using Sentinel-1 repeat-pass interferometry
Abstract. Snow water equivalent (SWE) is identified as the key element of the snowpack that impacts rivers' streamflow and water cycle. Both active and passive microwave remote sensing methods have been used to retrieve SWE, but there does not currently exist a SWE product that provides useful estimates in mountainous terrain. Active sensors provide higher-resolution observations, but the suitable radar frequencies and temporal repeat intervals have not been available until recently. Interferometric synthetic aperture radar (InSAR) has been shown to have the potential to estimate SWE change. In this study, we apply this technique to a long time series of 6 d temporal repeat Sentinel-1 C-band data from the 2020–2021 winter. The retrievals show statistically significant correlations both temporally and spatially with independent in situ measurements of SWE. The SWE change measurements vary between −5.3 and 9.4 cm over the entire time series and all the in situ stations. The Pearson correlation and RMSE between retrieved SWE change observations and in situ stations measurements are 0.8 and 0.93 cm, respectively. The total retrieved SWE in the entire 2020–2021 time series shows an SWE error of less than 2 cm for the nine in situ stations in the scene. Additionally, the retrieved SWE using Sentinel-1 data is well correlated with lidar snow depth data, with correlation of more than 0.47. Low temporal coherence is identified as the main reason for degrading the performance of SWE retrieval using InSAR data. We also show that the performance of the phase unwrapping algorithm degrades in regions with low temporal coherence. A higher frequency such as L-band improves the temporal coherence and SWE ambiguity. SWE retrieval using C-band Sentinel-1 data is shown to be successful, but faster revisit is required to avoid low temporal coherence. Global SWE retrieval using radar interferometry will have a great opportunity with the upcoming L-band 12 d repeat-pass NASA-ISRO Synthetic Aperture Radar (NISAR) data and the future 6 d repeat-pass Radar Observing System for Europe in L-band (ROSE-L) data.