Abstract: MODIS FAPAR products ranging from 2001 to 2010, which cover the whole basin of Heihe, were selected as the study objects. Combined with the land cover classification products in the same periods, MODIS FAPAR time series data of different species were decomposed into tendencies, cyclical fluctuations and residuals. Auto Regression models were then established for tendencies and cyclical fluctuations and Kalman filtering was used to remove the noises. At last, FAPAR time series with high quality were obtained. On this basis, the FAPAR variation differences of different vegetation types in upstream, midstream and downstream of Heihe Basin are analyzed. A region with different phase change characteristics was chosen to predict FAPAR value of each pixel at some point. Results show that FAPARs of different species own obvious seasonal variations. Because of different climates in different basins, FAPARs of same species also have slight differences. In addition, the proposed method can be used for different vegetation types and prediction results are quite similar to MODIS FAPAR values, with an error of about 3%.

Key words: AR model, FAPAR, Heihe Basin, Kalman filtering, time series analysis

摘要： 以2001?2010年黑河全流域MODIS FAPAR产品为研究对象, 结合同期MODIS土地覆盖分类产品, 提取FAPAR随时间变化的趋势项、周期项及残差, 对趋势和周期成分建立自回归模型, 并结合卡尔曼滤波方法过滤反演误差噪声, 获取高质量的FAPAR时间序列数据。在此基础上, 进一步分析黑河全流域不同植被类型FAPAR时间序列的变化差异, 并选取具有不同植被季相变化特征的研究区, 利用该方法预测某一时刻该区域各像元的FAPAR。结果表明: 黑河流域不同类型植被的FAPAR都具有明显的季节变化特征; 受气候等条件的影响, 流域不同区域的同种植被存在差异。提出的时间序列分析与预测方法适用于不同植被类型, FAPAR预测结果与MODIS当日产品较为相似, 预测误差约为3%。

关键词: AR模型, FAPAR, 黑河流域, 卡尔曼滤波, 时间序列分析