标题: A Multifactor Eigenvector Spatial Filtering-Based Method for Resolution-Enhanced Snow Water Equivalent Estimation in the Western United States

作者: Chen, YJ (Chen, Yuejun); Chen, YM (Chen, Yumin); Wilson, JP (Wilson, John P.); Yang, JX (Yang, Jiaxin); Su, H (Su, Heng); Xu, R (Xu, Rui)

来源出版物: REMOTE SENSING 卷: 15 期: 15 文献号: 3821 DOI: 10.3390/rs15153821 出版年: AUG 2023

摘要: Accurate snow water equivalent (SWE) products are vital for monitoring hydrological processes and managing water resources effectively. However, the coarse spatial resolution (typically at 25 km from passive microwave remote sensing images) of the existing SWE products cannot meet the needs of explicit hydrological modeling. Linear regression ignores the spatial autocorrelation (SA) in the variables, and the measure of SA in the data assimilation algorithm is not explicit. This study develops a Resolution-enhanced Multifactor Eigenvector Spatial Filtering (RM-ESF) method to estimate daily SWE in the western United States based on a 6.25 km enhanced-resolution passive microwave record. The RM-ESF method is based on a brightness temperature gradience algorithm, incorporating not only factors including geolocation, environmental, topographical, and snow features but also eigenvectors generated from a spatial weights matrix to take SA into account. The results indicate that the SWE estimation from the RM-ESF method obviously outperforms other SWE products given its overall highest correlation coefficient (0.72) and lowest RMSE (56.70 mm) and MAE (43.88 mm), compared with the AMSR2 (0.33, 131.38 mm, and 115.45 mm), GlobSnow3 (0.50, 100.03 mm, and 83.58 mm), NCA-LDAS (0.48, 98.80 mm, and 81.94 mm), and ERA5 (0.65, 67.33 mm, and 51.82 mm), respectively. The RM-ESF model considers SA effectively and estimates SWE at a resolution of 6.25 km, which provides a feasible and efficient approach for SWE estimation with higher precision and finer spatial resolution.

作者关键词: snow water equivalent estimation; resolution-enhanced; eigenvector spatial filtering; passive microwave brightness temperature; western United States

KeyWords Plus: ARTIFICIAL NEURAL-NETWORK; ENVIRONMENTAL-FACTORS; DEPTH RETRIEVAL; TREE MODELS; COVER; MOUNTAINS; AUTOCORRELATION; PARAMETERS; SCATTERING; ALGORITHM

地址: [Chen, Yuejun; Chen, Yumin; Su, Heng; Xu, Rui] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Wilson, John P.] Univ Southern Calif, Spatial Sci Inst, Los Angeles, CA 90089 USA.

[Yang, Jiaxin] Guangzhou Urban Planning & Design Survey Res Inst, Guangzhou 510060, Peoples R China.

通讯作者地址: Chen, YM (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: chenyuejun@whu.edu.cn; ymchen@whu.edu.cn; jpwilson@usc.edu; yangjiaxin@whu.edu.cn; 2017301110076@whu.edu.cn; 2022202050059@whu.edu.cn

影响因子：5