中文摘要：

以黄河三角洲HJ-1A CCD遥感数据和滨海湿地翅碱蓬生物量实测数据为数据源，通过对比分析参数回归模型（单变量线性和非线性回归模型，多元线性逐步回归模型）和人工神经网络模型（BP网络、RBF网络、GRNN网络），构建黄河三角洲湿地翅碱蓬生长初期的生物量湿重遥感估算最优模型。研究表明：基于遥感信息变量能够建立生长初期翅碱蓬生物量湿重估算模型。尽管基于RDVI、MSAVI和PC2的3个变量的多元线性回归模型的拟合效果较优，但是以SAVI、MSAVI、RVI、DVI、RDVI和PC2等7个遥感信息变量构建的BP神经网络模型的精度更高，平均相对误差为12．73％，估算效果最优，能够满足较高精度的生物量湿重估算需求。翅碱蓬生长初期生物量湿重最优估算模型的建立，为滨海地区植被生物量监测、区域翅碱蓬生物量季节动态模拟以及黄河三角洲生态系统功能评价提供技术支持与基础。

英文摘要：

Vegetation biomass is one of the key issues for accurate estimation of wetland ecosystems. The seasonal dynamics of vegetation biomass is vital for the study of ecosystem carbon fixation and the carbon cycle. Suaeda salsa is a common plant found in the Yellow River Delta （YRD） that plays an important role in coastal wetland ecosystems. Because S. salsa mainly grows in tidal flats, it is difficult to make long-term field observations, and so there are few systematic estimations of S. salsa biomass. In this paper, different models, including parametric models and artificial intelligence models, were tested and analyzed for estimating fresh weight of S. salsa biomass based on remote sensing images from the Chinese environmental satellite HJ-1A CCD and measured data. According to the spatial distribution of S. salsa cover type, coverage and growing patterns, 20 plots of 90m×90m were randomly selected for sampling. In each plot, 5 quadrats of lmxlm （in the four comers and center of the plot） were sampled and measured. The total biomass of each plot was calculated by the average value of those 5 quadrats. Vegetation indices were then extracted and the components of K-L transform and K-T transform were calculated from the preprocessed HJ-1A CCD images. The correlation between biomass fresh weight, dry weight and remote sensing information variables were analyzed to determine the variables that significantly related to the biomass.Finally, parameter and nonparametric models were built based on these significant variables. The parameter models used in this study include univariate linear, nonlinear regression and stepwise regression models. The non-parameter models used in this research are artificial neural network （ANN） models, including BP （Back propagation） networks, RBF （Radial Basis Function） networks and GRNN （General Regression Neural Network ） networks. The optimal model was determined by comparison of the mean relative error （MRE） of regression models and ANN models. The major c