基于Sentinel-1 sar的全球1公里分辨率土壤湿度数据产品：算法与初步评估

IF 11.1 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2025-01-08 DOI:10.1016/j.rse.2024.114579

Dong Fan, Tianjie Zhao, Xiaoguang Jiang, Almudena García-García, Toni Schmidt, Luis Samaniego, Sabine Attinger, Hua Wu, Yazhen Jiang, Jiancheng Shi, Lei Fan, Bo-Hui Tang, Wolfgang Wagner, Wouter Dorigo, Alexander Gruber, Francesco Mattia, Anna Balenzano, Luca Brocca, Thomas Jagdhuber, Jean-Pierre Wigneron, Jian Peng

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引用次数: 0

摘要

基于卫星的高空间分辨率土壤湿度数据对于水文、气象、生态和农业研究至关重要。特别是在流域水文模拟和作物水分胁迫分析中，1km分辨率的SM数据受到了广泛关注。本文利用Sentinel-1合成孔径雷达（SAR）数据，提出了一种双极化估计算法（DPA），用于生成全球尺度的1 km分辨率SM数据集（S1-DPA）。具体而言，构建正演模型模拟Sentinel-1双极化SAR观测到的后向散射，并通过最小化不同土壤和植被状态下的模拟误差来实现SM的反演。生成的S1-DPA数据产品覆盖2016-2022年全球陆地表面，包括上升和下降数据，观测频率为欧洲3-6天，其他地区6-12天。验证结果表明，S1-DPA重现了地面观测SM的时空变化特征，无偏均方根差（ubRMSD）为0.077 m3/m3。生成的1km SM产品将促进高分辨率SM数据在水文、气象和生态领域的应用。

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A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment

High spatial resolution of satellite-based soil moisture (SM) data are essential for hydrological, meteorological, ecological, and agricultural studies. Especially, for watershed hydrological simulation and crop water stress analysis, 1-km resolution SM data have attracted considerable attention. In this study, a dual-polarization algorithm (DPA) for SM estimation is proposed to produce a global-scale, 1-km resolution SM dataset (S1-DPA) using the Sentinel-1 synthetic aperture radar (SAR) data. Specifically, a forward model was constructed to simulate the backscatter observed by the Sentinel-1 dual-polarization SAR, and SM retrieval was achieved by minimizing the simulation error for different soil and vegetation states. The produced S1-DPA data products cover the global land surface for the period 2016–2022 and include both ascending and descending data with an observation frequency of 3–6 days for Europe and 6–12 days for the other regions. The validation results show that the S1-DPA reproduces the spatio-temporal variation characteristics of the ground-observed SM, with an unbiased root mean squared difference (ubRMSD) of 0.077 m³/m³. The generated 1-km SM product will facilitate the application of high-resolution SM data in the field of hydrology, meteorology and ecology.

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.