Research Of Cropland Soil Moisture Inversion Method Based On GNSS Single Antenna Technology

Soil moisture is an important variable of global water cycle and a key parameter to quantify the energy exchange between land and atmosphere.Timely and accurate acquisition of soil moisture data is very important for rational irrigation in agricultural production to reduce waste of water resources for achieving low cost and high yield in agricultural.Research activities of this dissertation are funding by the national 863 project"Multi-scale cropland information acquisition and fusion technology",general program of national natural science foundation of China"Cropland soil moisture inversion method research based on GNSS-IR data fusion",and the Beihang university crosswise task"Experimental base construction of Beidou soil remote sensing system",etc.This dissertation carries out the research on GNSS-IR soil moisture inversion method from the aspects of electromagnetic wave reflection analysis,signal processing,inversion model building,multi-satellite and multi-frequency data fusion algorithm,field experiment design and experimental data verification.Moreover,this dissertation propose a GA-SVM model of bare soil moisture based on GNSS single antenna,and constructs the soil moisture inversion model of single-satellite and dual-frequency band of Beidou system based on the entropy fusion and the carrier phase fusion respectively.Furthermore,an adaptive weighting fusion algorithm based on GPS multi-satellite and multi-frequency observations is proposed,and a soil moisture inversion model based on this algorithm is built.Finally,the performance of each model is compared and the applicable scenarios are evaluated.The main contents and conclusions of this research are as follows:(1)The principle of in-situ GNSS-IR soil moisture remote sensing is analyzed,and the data processing flows are discussed.Firstly,the system composition,constellation structure and signal characteristics of the four global navigation satellites systems are analyzed and compared from the signal source viewpoint.Then,the polarization mode and geometry of GNSS signal are analyzed in detail,and the mathematical expression of reflection signal is given,including the definition and calculation method of specular reflection point,first Fresnel reflection region and other related reflection region.Finally,this dissertation summed up the current two approaches of GNSS-R soil moisture inversion,as well as the dual antenna mode which utilizes the direct and reflected signal correlation power ratio,and the single antenna mode which utilizes the interference of direct and reflected signals.The basic principle of soil moisture inversion based on single antenna pattern is emphatically analyzed and the data processing flow is given.(2)The soil moisture inversion method of GNSS single antenna based on single-satellite and single-frequency observations is studied,and the unitary linear regression inversion model and GA-SVM model of bare soil moisture based on GNSS single antenna is presented respectively.Study of GNSS single antenna soil moisture inversion model research under the condition of bare soil is carried out.The method of deprive direct from SNR data to get the multipath component which can be used to build soil moisture model is studied theoretically.and a unitary linear regression inversion model of bare soil moisture based on GNSS single antenna technology is presented.The in-situ experimental campaign is performed for verification.From the inversion results of the unary regression model,it can be seen that the three interference characteristic parameters of oscillation frequency,oscillation amplitude and initial phase all have a good correlation with soil moisture.The regression correlation coefficient R of soil moisture inversion value and measured value reaches 0.7861-0.9353,and the root-mean-square error RMSE is 0.593 cm~3/cm~3-0.841 cm~3/cm~3.For the purpose of suppressing the noise caused by soil roughness and vegetation and improving the inversion accuracy,a single antenna soil moisture inversion model based on GA-SVM is proposed.In this model,radial basis kernel function is chosen for its good universality,and genetic algorithm introduced for SVM parameter optimization to avoid the uncertainty caused by artificial adjustment.The results show that the regression correlation coefficient R between the GA-SVM inversion results of single satellite GPS PRN 12 L1 band and the measured values of soil moisture reaches 0.9782,and the root-mean-square error is0.182 cm~3/cm~3.Comparing with the unary linear regression model constructed by three interference characteristic parameters respectively,the correlation coefficient R increases by4.59%-24.44%,and the root-mean-square error RMSE decreases by 69.3%-78.36%.RMSE of the GA-SVM model is reduced by 68.57%and 84.09%respectively compared with other machine learning methods such as PSO-SVM and BP neural network under the same data set configuration,which proves that the GA-SVM model can effectively improve the inversion accuracy.(3)The soil moisture inversion method of GNSS single antenna based on single-satellite and dual-frequency data fusion is studied,in order to fuse different soil information contained in different frequency band signals of GNSS reflection signals from the perspective of information entropy,the soil moisture inversion model under the single antenna mode of Beidou system based on single-satellite and dual-frequency entropy fusion is constructed,the data processing procedure of the model is given,and an in-situ experimental campaign has been carried out in Beijing Tongzhou for verification.The regression correlation coefficients between the soil moisture and the entropy fusion oscillation frequency of Beidou satellites PRN 9,PRN 10 and PRN 13 reaches 0.8118,0.8924 and 0.8609 respectively,and the root-mean-square error is 2.073 cm~3/cm~3,1.689 cm~3/cm~3 and 1.814 cm~3/cm~3 respectively.Comparing with the traditional regression method of oscillation frequency in B1 and B2frequency bands,the correlation coefficient R of PRN 9 is increased by 24.41%-37.11%,and the root-mean-square error RMSE is reduced by 3.4%-8.48%.PRN 10 inversion results show that the correlation coefficient R increases by 35.64%-48.07%and the RMSE decreases by10.82%-40.4%.PRN 13 inversion results show that the correlation coefficient R increases by37.59%-44.62%and the root-mean square error RMSE decreases by 11.56%-16.67%.Secondly,considering some of the GPS and Beidou receivers cannot provide SNR data,also to further enrich the methods of measuring soil moisture using the reflection signal,from the signal level,the soil moisture inversion model under the single antenna mode of Beidou system based on single-satellite and dual-frequency carrier phase fusion model is proposed,which eliminates the geometric information and tropospheric delay errors in carrier phase by dual frequency carrier phase combination method,.Then the data processing flow of the model is given.The results on Tongzhou experimental data show that the regression correlation coefficients of the soil moisture and the main oscillation frequency after the carrier phase fusion of Beidou PRN 9,PRN 10 and PRN 13 is 0.5641,0.6122 and 0.5796respectively,the root-mean-square error is 2.269 cm~3/cm~3,1.722 cm~3/cm~3 and 2.897 cm~3/cm~3respectively,which proves that the carrier phase fusion method is effective in soil moisture inversion,but the inversion accuracy still needs to be further improved.(4)The soil moisture inversion method of GNSS single antenna based on multi-satellite and multi-frequency data fusion is studied,from the information level viewpoint,in order to fully fusion the satellite's observation information of different orbits and different frequency bands,an adaptive weighted fusion algorithm based on multi-satellite and multi-frequency observations of GPS system is proposed,and the soil moisture inversion model based on multi-satellite and multi-frequency adaptive weighted fusion is constructed,data processing flow is introduced,and the model is verified by Tongzhou experimental data.The results show that the correlation coefficient R between the soil moisture and the amplitude observations of L1,L2 and L5 frequencies of GPS satellites PRN 1,PRN 6 and PRN 8 after weighted fusion is 0.8059,and the root-mean-square error RMSE is 2.075 cm~3/cm~3.Comparing with the traditional single satellite and single frequency regression method,the inversion results of the triple-satellite and triple-frequency fusion method show that the correlation coefficient R is increased by 24.69%-79.21%and the root-mean-square error RMSE is reduced by 22.28%-33.58%.Comparing with the mean fusion method which averaging amplitude observations in all frequency bands,the correlation coefficient R increases by 26.77%and the root-mean-square error RMSE decreases by 23.26%,the validity of the multi-satellite and multi-frequency data fusion model is proved.Finally,comprehensive evaluation and comparison of the four proposed inversion model is carried out based on the evaluation indexes such as inversion precision and algorithm complexity,and the applicable condition and scenario of each model is analyzed.Comparing with the traditional unary linear regression,the inversion precision of GA-SVM model improves the most,but its algorithm complexity and hardware requirements are higher.Furthermore,the dual frequency entropy fusion model has a good compromise between algorithm complexity and inversion precision.Moreover,the dual frequency carrier phase fusion model can make full use of the early GPS stations that cannot store SNR to expand the scope of global observation network with low complexity.Finally,the multi-satellite and multi-frequency fusion model improves both the inversion precision and observation range obviously,and the inversion value represents the average level of soil moisture in the detection area better.In the future,the model should be selected according to the demands and resources.