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• W2955430653 abstract "Land surface temperature (LST) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) products has been widely applied in environmental studies, and natural disaster management. However missing data in space and time due to cloud contamination, cloud shadows and atmospheric conditions has hindered its application. Accurate gap filling algorithms for a large spatiotemporal scale of LST data are necessary to enhance the utility of this product. This study applied a three-dimensional (3-D) gap-filling method to fill gaps in 9 years of LST data over Australia (2002–2011). As the gap-filling method relies on a smoothing parameter that controls the accuracy of the reconstruction algorithm, we estimated optimal smoothing parameters to separately reconstruct daytime and nighttime LST products. The reconstructed LST were validated against ground-based LST obtained from the OzFlux network and recommendations made on appropriate smoothing parameters. The results demonstrate that the gap-filling algorithm provides an accurate approach to generating reconstructed LST products for a long period over large spatial scales." @default.
• W2955430653 created "2019-07-12" @default.
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• W2955430653 date "2019-10-01" @default.
• W2955430653 modified "2023-10-02" @default.
• W2955430653 title "Using 3D robust smoothing to fill land surface temperature gaps at the continental scale" @default.
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• W2955430653 doi "https://doi.org/10.1016/j.jag.2019.05.012" @default.
• W2955430653 hasPublicationYear "2019" @default.
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