Yin Cong, Lopez-Baeza Ernesto, Martin-Neira Manuel, Fernandez-Moran Roberto, Yang Lei, Navarro-Camba Enrique A, Egido Alejandro, Mollfulleda Antonio, Li Weiqiang, Cao Yunchang, Zhu Bin, Yang Dongkai
Department of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Meteorological Observation Centre, China Meteorological Administration, Beijing 100081, China.
Sensors (Basel). 2019 Apr 22;19(8):1900. doi: 10.3390/s19081900.
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with -3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m/m. The determination coefficient between the retrieved soil moisture and in situ measurements was R = 0.90 for Oceanpal and R = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good.
本文介绍了在巴伦西亚锚定站进行的土壤湿度监测站（SOMOSTA）实验，该实验旨在对比全球导航卫星系统反射测量（GNSS-R）信号与被动L波段微波辐射计观测的土壤湿度。GNSS-R仪器有一个向上的天线用于接收卫星的直接信号，还有一个双极化向下的天线用于接收来自土壤表面的左旋圆极化（LHCP）和右旋圆极化（RHCP）反射信号。通过Oceanpal GNSS-R接收机的两个通道从三个不同天线收集数据，此外，还进行了校准以减少不同通道的影响。由此测量了反射率，并反演了土壤湿度。由欧洲航天局（ESA）资助的ELBARA-II（ESA L波段辐射计II）是一个L波段辐射计，有两个带宽为11 MHz、中心频率分别为1407.5 MHz和1419.5 MHz的通道。ELBARAII天线是一个大型双模尖顶喇叭，宽1.4 m，长2.7 m，-3 dB全波束宽度为12°（在天线主方向周围±6°），增益为23.5 dB。通过比较GNSS-R和ELBARA-II辐射计数据，发现GNSS-R测量的LHCP反射率与辐射计的水平/垂直反射率之间存在高度相关性（相关系数范围为0.83至0.91）。使用神经网络拟合进行GNSS-R土壤湿度反演，均方根误差（RMSE）为0.014 m/m。反演的土壤湿度与原位测量值之间的决定系数，对于Oceanpal为R = 0.90，对于Elbara II为R = 0.65，无偏均方根误差（ubRMSE）分别为0.0128和0.0734。两种L波段遥感方法反演的土壤湿度彼此显示出良好的一致性，并且它们与原位测量值以及降雨量之间的相互对应关系也很好。
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