предыдущий|следующий

Surface microwave scattering model evaluation and soil moisture retrieval based on ground-based radar data

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

    College of Resources and Environment,University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：gengdy@radi.ac.cn
  • Introduction：耿德源，1994年生, 男, 硕士研究生,研究方向为雷达土壤水分反演。E-mail: gengdy@radi.ac.cn
GENG Deyuan12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

  • Email：zhaotj@radi.ac.cn
  • Introduction：赵天杰，1985年生, 男, 副研究员,研究方向为微波遥感。E-mail: zhaotj@radi.ac.cn
ZHAO Tianjie1*,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

SHI Jiancheng1,  
  • Affiliation：

    State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China

HU Lu1,  
  • Affiliation：

    Institute of Aerospace Electronic Communication Equipment, Shanghai 201109, China

XU Hongxin3,  
  • Affiliation：

    Duolun Hydrology Bureau, Xilin Gol League Hydrographic Survey Bureau, Xilin Gol, Inner Mongolia 027300,China

HU Jianfeng4

реферат

In the process of the earth's water cycle, the status of soil moisture is very important. Soil moisture is an important parameter that controls the exchange of water, heat and energy within the various layers of the earth. In recent years, microwave remote sensing has become one of the important methods for monitoring surface soil moisture. There is a mathematical correlation between the amount of soil moisture and the dielectric constant of the soil. At the same time, the dielectric constant information of the soil directly has a systematic influence on the microwave backscattering intensity information, so by obtaining the backscattering information of the radar microwave signal, the dielectric information of the observation surface can be estimated, so as to carry out the soil moisture monitoring. It can be seen that using mathematical models to describe the correlation between backscatter information and soil moisture is helpful to obtain soil moisture information. A ground-based radar observation experiment is conducted at Xinyuan Ranch Station in the lightning river basin of Inner Mongolia to investigate the temporal and spatial variations of backscattered signals of ground-based synthetic aperture radar and study the influencing factors of radar soil moisture inversion. The radar backscattering coefficients are analyzed on the basis of the ground-based radar data from the above observation tests, including radar bands, incident angles, polarization channels, and other radar parameters. Then, the results of the preceding analysis are used to select a surface microwave surface scattering model. Lastly, an artificial neural network data set is constructed using the selected surface microwave surface scattering model to retrieve surface soil moisture. The results are as follows: (1) In the ground-based radar field of view, the simulation results of the surface microwave surface scattering model and the L-band full polarization data measured using the ground-based radar are the best fit for the AIEM-Oh model. (2) The absolute residual analysis of the AIEM-Oh model simulation results of radar incident angles in the range of 20°–60° indicated that the simulation results are closest to the radar measured values when the radar incident angles are 25°, 41°, and 53°. (3) The results of soil moisture inversion show that when the radar incident angle is 41°, the soil moisture inversion accuracy is highest, the correlation coefficient R is 0.8080, and the RMSE is 0.0385 m³/ m³. The conclusion of this paper is that the radar backscatter signal is affected by the combination of the radar incident angle and surface roughness. Therefore, a reasonable selection of radar incident angle by considering surface roughness can improve the accuracy of soil moisture retrieval. On the one hand, this research uses the surface microwave surface scattering model to simulate the neural network training data set, which is equivalent to using the practical physical simulation data set to embed the mathematical model (neural network) with the physical foundation, so as to reasonably explain the effectiveness of the training data set. On the other hand, through the sensitivity analysis of radar measurement data, the law of backscattering strength with the radar incident angle is obtained, which weakens the inversion error caused by the spatial heterogeneity of radar data. The improvement of soil moisture retrieval methods also provides new ideas for improving soil moisture retrieval.

ключеви́че слова́

remote sensing;ground-based radar observation test;surface microwave scattering model;neural network;soil moisture;cGBSAR

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