ABSTRACT

Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.

摘要

合成孔径雷达（SAR）遥感是用于降雪监测和降雪参数估计的最新工具。已经证明基于SAR遥感技术，例如干涉SAR（InSAR）和极化SAR（PolSAR）可用于估计雪的地球物理参数。基于InSAR的技术利用来自重复遍历数据集的干涉相位信息进行雪参数检索。在雪域监视期间，重复遍历之间的较大时间间隙会导致积雪中的时间去相关，从而导致干涉相干性的损失。因此，需要一种可以用于零时间基线数据集的雪参数估计技术。这项研究致力于开发基于极化SAR干涉术（PolInSAR）的建模方法，该模型用于使用以追踪单基地模式（时间基线= 10秒）获取的TerraSAR-X / TanDEM-X数据集进行雪深估计。这项工作的研究领域是位于比斯盆地的喜马al尔邦的马纳里地区。利用两个追踪单点TanDEM-X干涉式四极点数据集对（日期为2015年1月21日和2015年1月22日），对雪深变化进行了多时间分析。在本研究中，基于PolInSAR的相干振幅反演模型方法用于雪深检索。在建模实施过程中会使用复杂干涉相干的幅度。在PinInSAR建模期间，估计了雪的消光系数并将其用作输入。此外，在模型实现期间用于雪深估计的过程中，将计算出的体积相干量值与观察到的体积相干量值进行比较。估计的降雪深度在范围和方位方向上分别为15 m×15 m。这两个日期的估计积雪深度显示出与地面数据集的精确相关性。在此期间，观测到的模型检索到的雪深值从0.84 m上升到1.24 m。使用来自雪和雪崩研究机构（SASE），国防研究与发展组织（DRDO）和印度遥感学会（IIRS）安装的自动气象站（AWS）的雪深地面数据验证了检索结果。研究日期的Dhundi地区为相同日期。在模型实现期间，对雪深估算进行了计算的体积相干量值与观测到的体积相干量值的比较。估计的降雪深度在范围和方位方向上分别为15 m×15 m。这两个日期的估计积雪深度显示出与地面数据集的精确相关性。在此期间，观测到的模型检索到的雪深值从0.84 m上升到1.24 m。使用来自雪和雪崩研究机构（SASE），国防研究与发展组织（DRDO）和印度遥感学会（IIRS）安装的自动气象站（AWS）的雪深地面数据验证了检索结果。研究日期的Dhundi地区为相同日期。在模型实现期间，对雪深估算进行了计算的体积相干量值与观测到的体积相干量值的比较。估计的降雪深度在范围和方位方向上分别为15 m×15 m。这两个日期的估计积雪深度显示出与地面数据集的精确相关性。在此期间，观测到的模型检索到的雪深值从0.84 m上升到1.24 m。使用来自雪和雪崩研究机构（SASE），国防研究与发展组织（DRDO）和印度遥感学会（IIRS）安装的自动气象站（AWS）的雪深地面数据验证了检索结果。研究日期的Dhundi地区为相同日期。