Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas.In this study,Advanced Synthetic Aperture Radar(ASAR)observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin,Northwest China.A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter(EnKF),the forward radiative transfer model,crop model,and the Distributed Hydrology-Soil-Vegetation Model(DHSVM)was developed.The crop model,as a semi-empirical model,was used to estimate the surface backscattering of vegetated areas.The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape.Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June20 to July 15,2008.The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model.Compared with the simulation and in situ observations,the assimilated results were significantly improved in the surface layer and root layer,and the soil moisture varied slightly in the deep layer.Additionally,EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data.Moreover,to improve the assimilation results,further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed,also improving estimation accuracy of model operator is important.
提出一种基于贝叶斯理论和马尔科夫随机场MRF(Markov Random Fields)的主被动遥感数据协同分类方法。该方法依据光学与微波遥感数据在地物提取中的各自优势,首先对ASAR后向散射系数进行入射角归一化,然后构建一种基于贝叶斯理论和MRF的分类器,以归一化后的ASAR双极化数据与TM7个波段共同参与分类。分别对ASAR入射角归一化的有效性和主被动协同的必要性进行验证,结果表明,采用本文方法的分类精度达到89.4%,较未进行角度校正的主被动数据协同分类的精度提高了4.1%,较单独TM分类的精度提高了11.5%,体现出主被动遥感数据协同在分类上的潜力。
