Signal decomposition of hypertemporal Sentinel-1 time series to optimize the information gain for land applications

超时态 Sentinel-1 时间序列的信号分解，以优化陆地应用的信息增益

• 批准号: 407231386
• 负责人: Professor Dr. Christian Thiel
• 金额: --
• 依托单位: Lehrstuhl für Fernerkundung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/407231386?language=en
• 关键词: Signal; decomposition; hypertemporal; Sentinel; time

With the launch of the European Copernicus Program and the related launch of the satellites Sentinel-1A & B for the first time dense C-band time series are available reliably and free of charge. By combining the data of both satellites repetition rates of six days are feasible, which introduces a new temporal dimension to the SAR data acquisitions. This new dimensions allows and requires innovative data analysis strategies that optimize the SAR data processing as well as the gain of information.Overall aim of this project is the development and the validation of a suited Sentinel-1 data analysis strategy. The proposed approach aims at the temporal decomposition of these dense SAR time series. The gained spatiotemporal information will be analyzed in terms of its suitability as database for specific applications. At the same time a novel speckle filter will be developed based on the temporal decomposition approach. This novel filter has the characteristic to filter only in the time domain. Thus, the original geometric resolution of the original data will be maintained. Additionally, an extension of this filter which operates in the spatiotemporal domain will be developed as well.The temporal signal will be decomposed into components of different frequencies. High frequency components represent random backscattering variations caused by speckle or precipitation effects. Medium and low frequency components are related to biophysical processes such as soil moisture changes or plant growth. The investigation of which component is related to which biophysical process is scope of this project. Furthermore, the minimum requirements with regard to the temporal sampling rate to allow for the detection of the impact of biophysical processes will be investigated. Additionally the potential of the C-band phase related parameters InSAR coherence and phase closure error as soil moisture indicators will be analyzed.According to the results the usability of the temporal components will be investigated for several applications. Major focus is put on landcover mapping including REDD+ requests, the detection of landcover change, the analysis of wetland dynamics and the delineation of soil moisture indicators.

随着欧洲哥白尼方案的启动和哨兵-1A和B卫星的相关发射，密集的C波段时间序列第一次可靠地免费提供。通过合并两颗卫星的数据，六天的重复率是可行的，这为合成孔径雷达数据采集引入了一个新的时间维度。这个新的维度允许并需要创新的数据分析策略，优化SAR数据处理以及信息的增益，该项目的总体目标是开发和验证一个合适的哨兵-1数据分析策略。所提出的方法的目的是在这些密集的SAR时间序列的时间分解。将分析所获得的时空信息是否适合作为特定应用的数据库。同时，本文还提出了一种基于时间分解的散斑滤波器。该滤波器具有只在时域滤波的特点。因此，将保持原始数据的原始几何分辨率。此外，我们亦将此滤波器扩展至时空域，将时域信号分解为不同频率的分量。高频分量表示由散斑或降水效应引起的随机后向散射变化。中低频成分与土壤湿度变化或植物生长等生物物理过程有关。本项目的范围是调查哪种成分与哪种生物物理过程有关。此外，还将研究为探测生物物理过程的影响而对时间采样率的最低要求。此外，还分析了C波段相位相关参数干涉合成孔径雷达的相干性和相位闭合误差作为土壤湿度指示器的潜力，并根据分析结果研究了时间分量在多种应用中的可用性。主要重点是土地覆盖物测绘，包括降排+请求、土地覆盖物变化检测、湿地动态分析和土壤湿度指标的划定。