Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval
Quasi-Global Assessment of Deep Learning-Based CYGNSS Soil Moisture Retrieval
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DOI：
10.1109/jstars.2023.3287591
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发表时间：
2023
影响因子：
5.5
通讯作者：
Moin Nabi;V. Senyurek;Fangni Lei;M. Kurum;A. Gurbuz
中科院分区：
文献类型：
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作者：
Moin Nabi;V. Senyurek;Fangni Lei;M. Kurum;A. Gurbuz
高时空分辨率的全球土壤湿度产品对于了解水文和气象过程以及加强农业应用至关重要。全球导航卫星系统（GNSS）在L波段频率的信号，反射出的土地表面可以传达高分辨率的土地表面的信息，包括表面土壤水分（SM）。气旋全球导航卫星系统（CYGNSS）星座生成的延迟-多普勒地图（DDM）包含来自GNSS反射测量的重要地球表面信息。DDM受土壤湿度和其他因素的影响，如复杂的地形，土壤质地和覆盖植被。包括整个DDM信息可以帮助减少SM估计在不同条件下的不确定性沿着遥感地球物理数据。这项工作通过利用处理后的DDM测量（模拟功率，有效散射面积和双基地雷达截面）和辅助数据（海拔，坡度，含水率，土壤特性和植被含水量）将我们之前开发的深度学习（DL）框架扩展到全球范围。DL模型的训练和评估使用土壤水分主动被动（SMAP）使命的增强SM产品在9公里的分辨率。本研究全面评估DL模型对公开的CYGNSS为基础的SM产品在准全球范围内。除了与现场测量的典型比较外，还使用了一种强大的三重配置技术来评估基于DL的SM产品和其他CYGNSS衍生的SM产品。
A high spatial and temporal resolution global soil moisture product is essential for understanding hydrologic and meteorological processes and enhancing agricultural applications. Global navigation satellite system (GNSS) signals at L-band frequencies that reflect off the land surface can convey high-resolution land surface information, including surface soil moisture (SM). Cyclone global navigation satellite system (CYGNSS) constellation generates Delay-Doppler Maps (DDMs) that contain important Earth surface information from GNSS reflection measurements. DDMs are affected by soil moisture and other factors such as complex topography, soil texture, and overlying vegetation. Including entire DDM information can help reduce the uncertainty of SM estimation under different conditions along with remotely sensed geophysical data. This work extends our previously developed deep learning (DL) framework to a global scale by utilizing processed DDM measurements (analog power, effective scattering area, and bistatic radar cross-section) and ancillary data (elevation, slope, water percentage, soil properties, and vegetation water content). The DL model is trained and evaluated using the Soil Moisture Active Passive (SMAP) mission's enhanced SM products at 9-km resolution. This study comprehensively evaluates the DL model against publicly available CYGNSS-based SM products at a quasi-global scale. In addition to the typical comparison against in-situ measurements, a robust triple collocation technique is used to evaluate the DL-based SM product and other CYGNSS-derived SM products.