论文标题
哨兵1的干涉测量值对干旱区域的土壤水分估计
Soil moisture estimation from Sentinel-1 interferometric observations over arid regions
论文作者
论文摘要
我们提出了一种基于干涉合成孔径雷达（INSAR）时间序列分析的方法，该分析可以提供表面（前5厘米）的土壤水分（SSM）估计。 InsAR时间序列分析包括五个处理步骤。需要共同注册的单一外观复杂（SLC）SAR堆栈以及气象信息，作为建议的工作流程的输入。第一步，使用气象数据确定了无冰/无雪和零精确的SAR图像。在第二步中，执行了分布式散射器（DSS）（在裸露的土地上）的构建和相萃取。在第三个步骤中，计算了基于干涉相干性的SAR采集的表面土壤水分（SSM）水平的顺序。在第四步中，对于每个ds，计算了由于SSM变化引起的相干性。在第五步中，通过使用相干和相位闭合信息的分析干涉模型的约束反转来估计SSM。在www.github.com/kleok/insar4sm上提供了作为开源软件工具箱（INSAR4SM）提供的提议方法的实现。 提出了对加利福尼亚/亚利桑那州干旱地区的案例研究。所提出的工作流程应用于Sentinel-1（c波段）VV极化INSAR观察结果。通过从国际土壤水分网络（ISMN）（RMSE：0.027 $ M^3/m^3 $ r：0.88）和ERE5-LOND重新分析模型数据（RMSE：0.035 $ M^3/M^3/m^3 $ r：0.71）的独立SSM观测来评估估计的SSM结果。所提出的方法能够在高空间分辨率（〜250 m）下提供准确的SSM估计。对拟议方法论的益处和局限性的讨论突出了干涉测量值对干旱区域的SSM估计的潜力。
We present a methodology based on interferometric synthetic aperture radar (InSAR) time series analysis that can provide surface (top 5 cm) soil moisture (SSM) estimations. The InSAR time series analysis consists of five processing steps. A co-registered Single Look Complex (SLC) SAR stack as well as meteorological information are required as input of the proposed workflow. In the first step, ice/snow-free and zero-precipitation SAR images are identified using meteorological data. In the second step, construction and phase extraction of distributed scatterers (DSs) (over bare land) is performed. In the third step, for each DS the ordering of surface soil moisture (SSM) levels of SAR acquisitions based on interferometric coherence is calculated. In the fourth step, for each DS the coherence due to SSM variations is calculated. In the fifth step, SSM is estimated by a constrained inversion of an analytical interferometric model using coherence and phase closure information. The implementation of the proposed approach is provided as an open-source software toolbox (INSAR4SM) available at www.github.com/kleok/INSAR4SM. A case study over an arid region in California/Arizona is presented. The proposed workflow was applied in Sentinel- 1 (C-band) VV-polarized InSAR observations. The estimated SSM results were assessed with independent SSM observations from a station of the International Soil Moisture Network (ISMN) (RMSE: 0.027 $m^3/m^3$ R: 0.88) and ERA5-Land reanalysis model data (RMSE: 0.035 $m^3/m^3$ R: 0.71). The proposed methodology was able to provide accurate SSM estimations at high spatial resolution (~250 m). A discussion of the benefits and the limitations of the proposed methodology highlighted the potential of interferometric observables for SSM estimation over arid regions.
