Research On Rice Microwave Scattering Mechanism And Parameter Inversion

Radar remote sensing, which has characteristics as penetration and is available inall weather and all time, can be applied to detecting cloudy, foggy, rainy areas in aquick, macro and quantitative method. However, since microwave scatteringmechanism of the different surface features especially vegetation is complicated, whichmade the theoretical research lack of support; it is serious impediment to the radarremote sensing application potentiality. In recent years, several space-borne SARobservation platforms were constructed quickly, so it is possible to exploit moreefficient and accurate potential applications by studying the scattering mechanism of thesurface features and the quantitative inversion algorithm of the parameters. In thedissertation, with the theoretical basis of modeling and inversion algorithms, microwavescattering properties of rice, parameters and sensitivity was analyzed in detail, and thenempirical, semi-empirical and theoretical scattering model of rice were establishedbased on synchronization observations of space-borne SAR and ground-basedscatterometer data. Moreover, focus on neural network inversion algorithm based on thescattering model; the rice coverage area mapping and biomass inversion were achievedfrom SAR images. The main work of the dissertation is summarized as follows:(1) A microwave scattering measurement system was constructed with differentfrequencies, different angles and different polarization measurement capability. Thedistortion matrix of the transceiver antennas caused by the unbalanced channel andantenna crosstalk and other errors were eliminated after research on the non-planarwave antenna near-field electromagnetic wave scattering and suppression technology ofmulti-path superimposed effect. Enough independent sampling and precise calibrationguaranteed accurate measurement of the land-based scatterometer.(2) Eight different scattering measurements experiments were executed during tworice growing seasons (2010and2012). The measured C-band backscatteringcoefficients included that of full polarization (HH, HV, VH and VV) and differentincident angles (0°-90°). Sampling parameters of rice paddies included rice biomass,height, LAI, density, leaf and stem parameters, underlying surface parameters,waterlogged or soil parameters. The rice growth model was established and verified with the rice growth parameters, and the results were effective and reasonable. Thescattering properties of rice were analyzed, which included scattering characteristics ofangle at different rice growth periods, scattering characteristics of rice time domain, andthe correlation coefficient between scattering coefficients and the rice growthparameters.(3) The empirical, semi-empirical and theoretical microwave backscatteringmodels of rice were established. Based on different input parameters, the idea ofmulti-parameter nonlinear modeling was proposed, and then a multi rice growthparameters empirical model was established. According to the different scatteringmechanism, three semi-empirical model of rice, WC (Water Cloudy) model, theimproved WC model and the simplified MIMICS were established. These modelsparameters were acquired from the measured data and were compared and analyzedtheir accuracy separately. With Monte-Carlo method, the theory of microwavescattering model of rice was constructed and the model was modified with ricestructural characteristics. The rice growth model was used to provide input data for themodel simulation, and then the accuracy of the model was verified by comparing thesimulated data with that measured. The sensitivity between backscattering coefficientand the main rice parameters was analyzed by using the theoretical model.(4) Inversion algorithms of rice parameters were established according to the ricescattering model, including empirical, semi-empirical and neural network, which werebuilt based on Monte-Carlo theoretical models for single, dual and full polarizationsdata, respectively. The parameters of rice empirical inversion model and semi-empiricalinversion model at different polarization were obtained by using the measured data fromrice scattering experiments. The advantages and disadvantages of WC, improved WCand simplified MIMICS inversion models were compared and analyzed. The BP neuralnetwork (NN) was studied, including parameter settings of NN, the training datageneration, network training accuracy verification. The measured scattering dataverified the accuracy of NN inversion model based on Monte-Carlo model.(5) The semi-empirical model as simplified MIMICS and NN inversion algorithmbased on Monte-Carlo model were used to inverse rice biomass from dual polarizationASAR images and full polarization RADARSAT-2images, respectively. The ricemappings were achieved by combining the dual polarization ASAR images with opticalTM images, and then backscattering coefficients of rice area were extracted from SAR images. The rice biomass distribution of different periods was inversed by using thesemi-empirical inversion algorithm, and the inversion results were verified with theground measured data. Biomass inversion process of full polarization RADARSAT-2image was achieved, which involved the rice scattering measurement experiments,growth model and the Monte-Carlo scattering model, neural network training,multi-temporal RADARSAT-2image processing and biomass inversion. Rice areamappings were obtained by using the multi-temporal RADARSAT-2images, and ricebiomasses were inversed and validated by using the trained neural network andmeasured biomass data. The inversion algorithm was extended to the larger observationarea, and the rice biomass inversion of large area and growth monitoring was achieved.The complex nonlinear relationship between rice growth parameters andbackscattering coefficient is a typical ill-posed inversion problem, when the limitedradar data were used to extract these rice parameters, which is difficult to obtainaccurate quantitative results. The dissertation studied rice microwave scatteringcharacteristics and parameters inversion algorithm, which can enrich theoretical andexperimental research of the vegetation scattering mechanisms, and promote the SARimage research of quantification inversion algorithm of vegetation parameters. theresearch also expands the application field of radar remote sensing technology, and isproved to own enormous potential economic values for crop growth monitoring,yieldestimation, and even vegetation ecological environment monitoring.