Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution.
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution.
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DOI：
10.3390/s17091966
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发表时间：
2017-08-26
期刊：
影响因子：
--
通讯作者：
Baghdadi N
中科院分区：
文献类型：
--
作者：
Gao Q;Zribi M;Escorihuela MJ;Baghdadi N
最近部署的欧空局哨兵业务卫星为遥感应用建立了新的范例。在此背景下，Sentinel-1 雷达图像使得以高空间和时间分辨率检索地表土壤湿度成为可能。本文提出了两种从空间分辨率为 100 m 的遥感 SAR 图像中反演土壤湿度的方法。这些算法基于对 VV 偏振中记录的 Sentinel-1 数据的解释，该数据与 Sentinel-2 光学数据相结合，用于分析 Urgell（西班牙加泰罗尼亚）某个地点的植被影响。第一个算法已由 Zribi 等人于 2008 年使用低空间分辨率 ERS 散射仪数据应用于西非的观测，并且基于变化检测方法。在本研究中，该方法应用于Sentinel-1数据，并利用Sentinel观测的高重复频率来优化反演过程。第二种算法依赖于一种新方法，该方法基于连续两天观测到的后向散射 Sentinel-1 雷达信号之间的差异，表示为 NDVI 光学指数的函数。这两种方法均适用于近 1.5 年的卫星数据（2015 年 7 月至 2016 年 11 月），并使用在研究地点获取的现场数据进行了验证。这导致第一种方法和第二种方法的体积湿度的 RMS 误差分别约为 0.087 m3/m3 和 0.059 m3/m3。这些技术不需要进行现场校准，并且可以应用于任何已记录 SAR 数据时间序列的植被覆盖区域。
The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.
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