草业科学2017，Vol.34Issue(2)：264-272,9.DOI:10.11829/j.issn.1001-0629.2016-0256
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
摘要
Abstract
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.关键词
时空融合模型/灵活的时空融合模型/植被覆盖度/植被指数/LandsatTM/MODIS/线性与非线性拟合Key words
Spatiotemporal Data Fusion Model/Flexible Spatiotemporal Data Fusion Model/Vegetation Cover Fraction/Vegetation Index/Landsat TM/Moderate Resolution Imaging Spectroradiometer/Linear and Nonlinear Fitting分类
农业科技引用本文复制引用
王杰,李卫朋..基于灵活的时空融合模型的植被覆盖度与植被指数关系[J].草业科学,2017,34(2):264-272,9.基金项目
西华师范大学博士科研启动基金(412546、412547) （412546、412547）
四川省教育厅自然科学重点项目(17AZ0387) （17AZ0387）
国家自然科学基金资助项目(41101348) （41101348）
