Gnss-IR Soil Moisture Inversion Method Based On Machine Learning

Soil moisture is a physical quantity used to describe the degree of soil dryness and wetness,which reflects the water supply of crops.Accurate monitoring of soil moisture is an important basis for realizing stable and high agricultural yield.It is of great significance to the application of agrometeorology and global water cycle.Global Navigation Satellite SystemReflectometry(GNSS-R)technology,as a low-cost remote sensing technology,has attracted wide attention in recent years.This paper is based on the theoretical study of soil moisture acquisition by using the interference between direct and reflected signals of the global navigation satellite system.Then two algorithms,random forest and deep neural networks,were used to construct GNSS-IR soil moisture inversion model respectively,and compared and analyzed the data processing results of linear regression model and measured data.Finally,ground-based experiments were carried out to verify the results,and GPS PRN 9 was taken as an example to demonstrate the experimental results,and the RMSE was used as the evaluation index of satellite inversion results,and the final conclusion was obtained.The main research contents and results of this paper are as follows.(1)Established a GNSS-IR soil moisture inversion model based on random forestRandom forest algorithm was used to process the SNR frequency,amplitude and phase of two frequency bands of the GPS L1 and L2.Then single parameter and dual-parameters processing were carried out,and the inversion model was established,and the results were compared with linear regression processing.Taking PRN 9 as an example,the RMSE of frequency,amplitude and phase single parameter in L1 decreases 62.07%,49.09%,67.02%respectively,and the RMSE of frequency,amplitude and phase single parameter in L2 decreases 68.55%,71.22%,64.07%,respectively.The results show that the random forest algorithm has achieved good results in single parameter soil moisture inversion under the low algorithm complexity,but the performance in dual-parameter data fusion is insufficient.(1)Established a GNSS-IR soil moisture inversion model based on deep neural networkDeep neural network was used to process the SNR frequency,amplitude and phase of two frequency bands of the GPS L1 and L2.Then single parameter and dual-parameters processing were carried out,and the inversion model was established,and the results were compared with random forest algorithm processing.Taking PRN 9 as an example,the RMSE of frequency,amplitude and phase single parameter in L1 decreases 69.70%,67.86%,50.79%respectively,and the RMSE of frequency,amplitude and phase single parameter in L2 decreases 48.72%,30.00%,53.33% respectively.And the RMSE of frequency,amplitude and phase dual-parameter in L1 decreases 85.37%,95.59%,95.89% respectively,and the RMSE of frequency,amplitude and phase dual-parameter in L2 decreases 96.49%,87.88%,98.73%respectively.The results show that the deep neural network algorithm achieves better results than the random forest,especially the dual-parameter data fusion is more effective than the random forest algorithm,but its algorithm complexity is higher than the random forest algorithm,and the dual-parameter soil moisture inversion is more effective.In this paper,the random forest algorithm and deep neural network algorithm were used to establish the inversion model respectively,and the results of linear regression method and experimental measured data were compared.The results show that machine learning can effectively improve the accuracy of soil moisture inversion,and promote the wide application of GNSS-R in agricultural production.