水稻株高被广泛应用于物候监测、水稻健康评估及产量预测等领域,极化合成孔径雷达(Polarimetric Syn-thetic Aperture Radar,PoISAR)信号能穿透水稻冠层记录水稻垂直结构信息,有助于实现高分辨率、大范围水稻株高提取.该文提出一种适用于非干涉条件下PolSAR数据水稻株高反演方法:利用极化SAR分解技术分离冠层及地表散射信号,引入随机地体二层散射模型(Random Volume over Ground,RVoG)对分解得到的水稻冠层散射能量进行建模,从而建立水稻株高反演模型;最后,联合邻域同质像素并利用NSGA-Ⅱ遗传算法对模型进行解算.利用2019年13景Sentinel-1影像在西班牙地区进行试验,水稻株高反演精度达到0.1 m,R2达0.96以上,证明新方法能较好地适用于非干涉条件下PolSAR数据水稻株高反演.
Abstract
Rice plant height is widely used in the fields of climate monitoring,health assessment and yield prediction.Polarime-tric Synthetic Aperture Radar(PolSAR)signals can penetrate the rice canopy and record the vertical structure information of rice,which provides the feasibility of high-resolution and large-scale rice plant height extraction.To realize rice plant height esti-mation,a correlation between PolSAR observations and rice biophysical parameters needs to be established.In view of this,this paper proposes a rice plant height inversion method for PolSAR data under non-interferometric conditions:first,the dual-polari-zation decomposition technique is used to separate the scattered signals from the canopy and ground surface;after that,the ran-dom volume over ground(RVoG)scattering model is introduced to model the scattered energy of rice canopy obtained from the decomposition,and the rice height inversion model is established;finally,the model is solved by combining neighborhood homo-geneous pixels and NSGA-Ⅱ genetic algorithm.The experimental validation was carried out in the southern region of Seville in Spain using Sentinel-1 image in 2019,and the results showed that the accuracy of rice plant height inversion reached 0.1 m and R was above 0.96.It proves that the new method can be better applied to the rice plant height inversion of PolSAR data under non-interferometric conditions,and can provide strong support for realizing efficient rice plant height monitoring.
维普
万方数据