中文摘要：

利用遥感植被指数、典型植被样方和地面观测信息进行塔里木河千流植被监测是目前的主要方法。由于塔里木河干流具有流域下垫面均匀性差，自然植被随机分布的特点，使得现有研究方法局限在特定的时间和空间尺度，很难使用地面的观测数据和不同尺度的遥感数据进行植被生长状态的协同分析。针对这些问题，本文提出了利用不同分辨率遥感数据和地面观测数据进行多尺度协同分析的方法MSSA（Multiple Scale Synergy Analysis）。该方法包括以下几个步骤：①通过低空间分辨率的遥感数据构建时间序列的塔里木河干流植被指数分布图像，在分析图像特征的基础上划分塔里木河遥感监测单元；（爹对监测单元内部不同组分的时间和空间状态参数进行量化与率定；③根据几何光学模型原理和植被随机分布特性，采用线性混合模型模拟单元植被指数；④根据模拟结果和遥感数据的对比分析，获得地面植被参量的可靠估计。该方法将地面组分的状态参量和遥感数据通过模拟模型相关联，实现了不同时空尺度遥感数据以及地面样方或者点观测数据的协同分析，为塔里木河干流植被监测进行长期、细致的研究建立了海量数据综合分析的方法体系。

英文摘要：

Using NDVI, typical vegetation sampling and on - site field measuring are the major methods in vegetation monitoring at the mainstream of the Tarim River. However, those methods are limited within a certain temporal and spatial scale due to the sparse and random distribution of the natural vegetation and the heterogeneity of the surfaces along the mainstream of the river, which makes it very difficult to correlate the observed data with the remotely sensed data at various scales when analyzing the vegetation growth status. In an effort to solve this limitation, the multiple scale synergy analysis （MSSA） method is presented in this paper, which is consisted of the following pro- eedures ： （ 1 ） to construct the image of vegetation index distribution of the mainstream of the Tarim River based on the time series by using the remotely sensed data of lower spatial resolution in order to define the remotely sensed monitoring units according to the characteristics of the built - up image ; （2） to quantify and calibrate the parameters of the temporal and spatial status of the different components within one monitoring unit; （3）to use the linear hybrid model to simulate the unit vegetation index; （4）to obtain the reliable estimation about the vegetation param- eters based on the comparison between the simulated results and the remotely sensed data. Correlating the parameters of the vegetation growth status with the remotely sensed data via the simulation model, this method provides the mechanism in monitoring the vegetation by making use of the remotely sensed data at various temporal and spatial scales along with the sampling data, which can be used as the methodological foundation in the vegetation monitoring at the mainstream of the Tarim River.