利用多参数雪后散射模型的 PolSAR 数据雪深检索方法

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
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引用次数: 0

摘要

雪深(SD)是雪的一个重要属性,其时空变化对全球变化、融雪径流模拟、灾害预测和淡水储量估算具有重要意义。极坐标合成孔径雷达(PolSAR)可以精确描述目标的后向散射,是一种有效的标度检索工具。干雪的后向散射成分主要由雪堆的体积散射和雪地界面的表面散射组成。然而,现有的利用 PolSAR 数据检索自毁的方法存在过度依赖原地数据和忽略雪地界面表面散射的问题。我们通过充分考虑雪的主要后向散射成分,并通过多参数估计来求解雪的后向散射模型,提出了一种新型的 PolSAR 数据自毁率检索方法。首先,结合小介电常数体积散射模型和密歇根半经验表面散射模型,模拟雪的不同散射分量,形成雪的后向散射模型,并利用山口分解法提取相应的后向散射系数。然后,通过广义体积参数计算雪的介电常数,并通过建模进一步估算消光系数。最后,通过这些参数求解雪的反向散射模型,从而得到标度。通过在新疆阿勒泰地区获取的 Ku 波段无人机合成孔径雷达数据对所提出的方法进行了验证,并通过原位数据对其精度进行了评估。相关系数、均方根误差和平均绝对误差分别为 0.80、4.49 厘米和 3.95 厘米。同时,分析了不同标度、模型参数估计、求解方法和底面产生的不确定性,以增强所提方法的通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Snow depth retrieval method for PolSAR data using multi-parameters snow backscattering model

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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