Abstract
High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is difficult to obtain remote sensing images with both high spatial and temporal resolution. The spatiotemporal fusion model is a crucial method to solve this problem. The spatial and temporal adaptive reflectivity fusion model (STARFM) and its improved models are the most widely used spatiotemporal adaptive fusion models. However, the existing spatiotemporal adaptive reflectivity fusion model and its improved models have great uncertainty in selecting neighboring similar pixels, especially in spatially heterogeneous areas. Therefore, it is difficult to effectively search and determine neighboring spectrally similar pixels in STARFM-like models, resulting in a decrease of imagery fusion accuracy. In this research, we modify the procedure of neighboring similar pixel selection of ESTARFM method and propose an improved ESTARFM method (I-ESTARFM). Based on the land cover endmember types and its fraction values obtained by spectral mixing analysis, the neighboring similar pixels can be effectively selected. The experimental results indicate that the I-ESTARFM method selects neighboring spectrally similar pixels more accurately than STARFM and ESTARFM models. Compared with the STARFM and ESTARFM, the correlation coefficients of the image fused by the I-ESTARFM with that of the actual image are increased and the mean square error is decreased, especially in spatially heterogeneous areas. The uncertainty of spectral similar neighborhood pixel selection is reduced and the precision of spatial-temporal fusion is improved.
Highlights
With the development of remote sensing applications, many studies about land use/cover change monitoring, cropping estimation, and flood mapping require remotely sensed data with high spatial and temporal resolution [1,2,3]
The existing spatial and temporal adaptive reflectance fusion model (STARFM)-like fusion models use the standard deviation of the pixel reflectivity of the entire image and the estimated number of land cover type as the spectral threshold for selecting similar pixels [5,8,9,10], which has a large uncertainty in selecting similar pixels, especially in spatially heterogeneous areas, decreasing the accuracy of spatiotemporal fusion
We firstly introduce the procedure of neighboring spectrally similar pixels selection and evaluate the performance of improved spatiotemporal fusion approach, followed by comparing the fusion results obtained by STARFM and ESTARFM models in the three study areas
Summary
With the development of remote sensing applications, many studies about land use/cover change monitoring, cropping estimation, and flood mapping require remotely sensed data with high spatial and temporal resolution [1,2,3]. By introducing two pairs of reference images, the fusion accuracy was improved in the area of heterogeneity landscape, and the detection ability of the surface cover mutation region was enhanced in ESTARFM [10] Research shows that this model can be applied to the fusion of Landsat and MODIS images, and to images of other sensors [11], indicating that STARFM and its improved methods have great potential in the applications of spatiotemporal fusion. The existing STARFM-like fusion models use the standard deviation of the pixel reflectivity of the entire image and the estimated number of land cover type as the spectral threshold for selecting similar pixels [5,8,9,10], which has a large uncertainty in selecting similar pixels, especially in spatially heterogeneous areas, decreasing the accuracy of spatiotemporal fusion.
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