Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment
Single-Pass Soil Moisture Retrieval Using GNSS-R at L1 and L5 Bands: Results from Airborne Experiment
复制标题
使用 L1 和 L5 频段 GNSS-R 进行单程土壤湿度反演：机载实验结果
DOI：
10.3390/rs13040797
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
A. Monerris
中科院分区：
文献类型：
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作者：
J. Muñoz;Raul Onrubia Ibáñez;D. Pascual;Hyuk Park;M. Pablos;Adriano Camps;C. Rüdiger;J. Walker;A. Monerris
全球导航卫星系统反射仪(GNSS-R)已经证明，它有潜力反演一些地球物理参数，包括土壤水分。然而，单程GNSS-R土壤水分反演仍然是一个挑战。这项研究比较了使用微波干涉仪反射计(MIR)、机载双频段(L1/E1和L5/E5a)、多星座(GPS和伽利略)GNSS-R仪器获得的两种不同数据集，该仪器具有两个19元天线阵列，每个阵列有四个电子控制波束。该仪器在澳大利亚新南威尔士州南部的OzNet土壤水分监测网上空飞行了两次：第一次飞行是在长时间没有降雨后进行的，第二次飞行是在一次降雨事件之后。在这项工作中，评估了地表粗糙度和植被衰减对L1和L5波段GNSS-R信号反射率的影响。分析了不同积分时间下的反射率，最后利用人工神经网络从反射率值中提取土壤水分。对该算法进行了训练，并将其与根据SMOS土壤水分、Sentinel-2归一化植被指数(NDVI)数据和ECMWF地表温度得出的20米分辨率的土壤水分估计进行了比较。
Global Navigation Satellite System—Reflectometry (GNSS-R) has already proven its potential for retrieving a number of geophysical parameters, including soil moisture. However, single-pass GNSS-R soil moisture retrieval is still a challenge. This study presents a comparison of two different data sets acquired with the Microwave Interferometer Reflectometer (MIR), an airborne-based dual-band (L1/E1 and L5/E5a), multiconstellation (GPS and Galileo) GNSS-R instrument with two 19-element antenna arrays with four electronically steered beams each. The instrument was flown twice over the OzNet soil moisture monitoring network in southern New South Wales (Australia): the first flight was performed after a long period without rain, and the second one just after a rain event. In this work, the impact of surface roughness and vegetation attenuation in the reflectivity of the GNSS-R signal is assessed at both L1 and L5 bands. The work analyzes the reflectivity at different integration times, and finally, an artificial neural network is used to retrieve soil moisture from the reflectivity values. The algorithm is trained and compared to a 20-m resolution downscaled soil moisture estimate derived from SMOS soil moisture, Sentinel-2 normalized difference vegetation index (NDVI) data, and ECMWF Land Surface Temperature.