Abstract
Abstract. In this study, three documented spatiotemporal data fusion models were applied to Landsat-7 and MODIS surface reflectance, and NDVI. The algorithms included the spatial and temporal adaptive reflectance fusion model (STARFM), sparse representation based on a spatiotemporal reflectance fusion model (SPSTFM), and spatiotemporal image-fusion model (STI-FM). The objectives of this study were to (i) compare the performance of these three fusion models using a one Landsat-MODIS spectral reflectance image pairs using time-series datasets from the Coleambally irrigation area in Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Results showed that the three fusion models predicted the synthetic Landsat-7 image with adequate agreements. The STI-FM produced more accurate reconstructions of both Landsat-7 spectral bands and NDVI. Furthermore, it produced surface reflectance images having the highest correlation with the actual Landsat-7 images. This study indicated that STI-FM would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration.
Highlights
Australia, and (ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements
This study indicated that spatiotemporal image-fusion model (STI-FM) would be more suitable for spatiotemporal data fusion applications such as vegetation monitoring, drought monitoring, and evapotranspiration
It shows that STI-FM is having higher r2 values and lower root mean square error (RMSE) values
Summary
(ii) quantitatively evaluate the accuracy of the synthetic images generated from each fusion model using statistical measurements. Given the tradeoff between spatial and temporal resolutions of satellite systems, several spatiotemporal remote sensing data fusion methods have been developed (Cammalleri et al, 2013; (Hilker et al, 2009; Gao, F., Masek, J., Schwaller, M., Hall, 2006; Zurita-Milla et al, 2011; Hazaymeh and Hassan, 2015a,b). These methods have been used as suitable cost-effective approaches to generate continuous time series consisted of original and synthetic remote sensing data.
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