Comparing the applicability of five typical spatio-temporal information fusion algorithms based on remote sensing data in vegetation index reconstruction of wetland areas
This study aims to explore the applicability of various spatio-temporal information fusion algorithms based on remote sensing data to wetland areas characterized by frequent land-water conversion and diverse surface features.With the Poyang Lake sample area as the study area,this study examined five typical spatio-temporal information fusion algorithms(STARFM,ESTARFM,FSDAF,Fit-FC,and STNLFFM).Considering the differences in surface features among different periods,Landsat and MODIS remote sensing data were selected to conduct image fusion experiments for normalized difference vegetation indices(NDVIs)during low-and normal-water periods.Moreover,the accuracy of these algorithms was evaluated in spatial and spectral dimensions.The results of this study are as follows:① In the case of only one pair of coarse-and fine-resolution images as input,the FSDAF exhibited the optimal fusion prediction effect for the low-water period,with an overall error of 0.433 5,whereas the STNLFFM manifested the optimal fusion prediction effect for the normal-water period,with an overall error of 0.514 7;② In the case of two pairs of coarse-and fine-resolution images of low-and normal-water periods as input,the ESTARFM demonstrated the optimal fusion prediction effect,with an overall error of 0.467 0;③ The applicability of different algorithms to a wetland area is associated with the proportion of water bodies in the study area.The STNLFFM displayed the optimal fusion prediction effect for water bodies.
spatio-temporal information fusionPoyang Lake wetlandFSDAFSTNLFFMESTARFM
万方数据
维普
