中文摘要：

快速准确地掌握降水的时空分布,对于区域气候、水文和生态环境等至关重要。以长江三角洲为研究区域,获取FY卫星影像光谱特征,对其进行特征分析并结合地面实况降水观测数据,获得卫星降水模拟参数的特征集,利用SVM（Support Vector Machine）优越的非线性回归性能,提出一种自适应、自学习的降水估计方法。实验结果表明：卫星云图适用于阐释云的降水机理,将其与SVM结合,可以很好地表达长江三角洲区域降水与云图特征间的非线性关系。此方法得到的降水估计量与地面实测降水数据的相关系数为0.85,表明本文方法可对该地区的降水估计发挥作用。

英文摘要：

To quickly and accurately grasp the spatial and temporal distribution of precipitation, for regional climate, hydrology and ecological environment is essential. The Yangtze River Delta is taken as the study area, extracting spectral characteristics of FY satellite imagery and performing characteristic analysis, combined with the receiving precipitation data, the characteristic set to describe the precipitation parameters was obtained and using the support vector machine superior performance of nonlinear regression put forward a kind of self-adaptive and self-learning precipitation estimation method. The results show that： satellite imagery is applied to interpretation cloud precipitation mechanism, combined with SVM, which can well express the nonlinear relationship of the Yangtze River Delta region between precipitation and cloud features. The results also demonstrate that correlation coefficient between values of modeling estimation data and receiving precipitation data is 0.85. It is shown that this method can estimate the precipitation in the region to play a role.