Zachary Hoppinen, S. Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, C. Vuyovich
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The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.\n","PeriodicalId":509217,"journal":{"name":"The Cryosphere","volume":"59 22","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry\",\"authors\":\"Zachary Hoppinen, S. Oveisgharan, Hans-Peter Marshall, Ross Mower, Kelly Elder, C. Vuyovich\",\"doi\":\"10.5194/tc-18-575-2024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. 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引用次数: 0
摘要
摘要。本研究评估了在低频合成孔径雷达(SAR)图像上使用干涉测量法监测季节和天气尺度的雪水当量(SWE)的情况。作为 NASA SnowEx 2020 年和 2021 年活动的一部分,我们从爱达荷州中部上空由 L 波段航空平台(NASA 无人驾驶飞行器合成孔径雷达 (UAVSAR))捕获的九对 SAR 图像(平均 8 天时间基线)中检索了 SWE 变化。将检索到的 SWE 变化与重合的原地测量值(SNOTEL 和 SnowEx 野外作业中的雪坑)以及 100 米网格化 SnowModel 模拟的 SWE 变化进行了比较。原位测量值与检索测量值的比较结果显示,雪深变化的皮尔逊相关性强(R=0.80),均方根误差小(0.1 米,n=64),而 SWE 变化的结果类似(均方根误差 = 0.04 米,R=0.52,n=57)。检索到的 SWE 变化与 SnowModel SWE 变化之间的比较也显示出良好的相关性(R=0.60,RMSD = 0.023 m,n=3.2×106),尤其是对于没有模型融化和树木覆盖率低的像素子集,相关性更高(R=0.72,RMSD = 0.013 m,n=6.5×104)。最后,我们对各种因素的检索结果进行了分类,结果表明,在海拔较低、入射角度较高、树木比例和高度较高以及累积融化量较大的情况下,建模值与检索值之间的相关性降低。本研究在以往干涉测量工作的基础上,利用大空间范围内整个冬季的 L 波段合成孔径雷达图像时间序列,对照原地和模拟结果,评估了 SWE 变化检索的准确性,以及检索准确性的控制因素。
Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry
Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.