基于灵活的时空融合模型的植被覆盖度与植被指数关系

Research on relationship between vegetation cover fraction and vegetation index based on flexible spatiotemporal data fusion model

时空数据融合模型被广泛地应用于获取高时间、高空间分辨率的植被指数与植被覆盖度,但是其反演的精度常常受输入的低空间分辨率影像(如MODIS影像)的影响。本研究基于灵活的时空数据融合方法(FSDAF),深入分析了赛里木湖流域与石河子地区两种不同情景的MODIS影像组合对FSDAF模型植被覆盖度提取精度的影响,并研究了6种植被指数与植被覆盖度的线性与非线性关系。研究结果表明,FSDAF模拟影像的植被覆盖度精度取决于2个时期MODIS影像的变化率,影像变化小时取得的精度明显好于影像差异大的情况。而采用植被指数对植被覆盖度模拟时,NDVI与OSAVI的线性拟合效果较好,可以获取较理想的结果。试验表明,采用时空模型用于研究区植被覆盖反演能取得较好的效果,具有一定的应用推广价值。

The spatiotemporal data fusion model has been widely used to obtain high temporal and spatial resolution vegetation indexes and vegetation cover fractions,but its accuracy is often affected by low spatial resolution images[e.g.,moderate resolution imaging spectroradiometer（MODIS）images].This study design was based on the flexible spatiotemporal data fusion model（FSDAF）,and investigated the effects of three different MODIS image pairs of the FSDAF model for vegetation cover fraction extraction in arid region of China.Furthermore,the linear and non-linear relationships between six vegetation indexes and vegetation cover fractions were investigated.The results showed that the retrieval accuracy of the vegetation cover fraction of FSDAF simulated images depended on the rate of change of the MODIS images in two periods.In addition,the accuracy of the image with a slight change was significantly higher than that of the image with a great difference.When vegetation indexes were used to simulate the vegetation cover fraction,the normalized difference vegetation index（NDVI）and green NDVI（GNDVI）linear fitting methods produced better results than other methods did,and provided the ideal results.The experimental results showed that the FSDAF model could be used to determine the retrieval of vegetation cover in arid area,and it had a good effect and applicability.