针对ETM 空间分辨率高和MODIS 时间分辨率高的特点, 选择官厅水库上游为实验区, 基于对STARFM 方法的改进, 构建不同时空分辨率NDVI 的时空融合模型-STAVFM, 使用该模型对ETM NDVI 与MODIS NDVI 融 合, 构建了高时空分辨率NDVI 数据集。研究结果表明, STAVFM 根据植被变化特点定义了有效时间窗口, 在考虑 物候影响的同时改进了时间维的加权方式, 通过MODIS NDVI 时间变化信息与ETM NDVI 空间差异信息的有机结 合, 实现缺失高空间分辨率NDVI 的有效预测(3 景预测NDVI 与实际NDVI 的相关系数分别达到了0.82、0.90 和 0.91), 从而构建高时空分辨率NDVI 数据集, 其时间上保留了高时间分辨率数据的时间变化趋势, 空间上又反映高 空间分辨率数据的空间细节差异。

To combine the high spatial resolution of Landsat and high temporal resolution of MODIS data, We selected an 18 km?18 km study area in upper reaches of Guanting reservoir. A new method—Spatial and Temporal Adaptive vegetation index Fusion Model (STAVFM) for blending NDVI of different spatial and temporal resolutions to produce high temporal-spatial resolution NDVI dataset has been developed based on STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model). STAVFM defined a time window according to the temporal variation of vegetation, put the vegetation phenophase into consideration and improve the temporal weighting algorithm. The result shows that the new method can combine the temporal information of MODIS NDVI and spatial difference information of ETM NDVI and can predict the missed ETM NDVI with a high accuracy (the correlation coefficients of three pairs of observed and predicted ETM NDVI are 0.82, 0.90 and 0.91). A high temporal and high spatial resolution NDVI dataset is constructed, which maintains the temporal trend of high temporal resolution data and the detailed spatial difference information of high spatial resolution data.