hamid salehi; Ali shamsoddini; Seyed Majid Mirlatifi
Volume 10, Issue 3 , January 2019, , Pages 123-140
Abstract
Satellites acquire data in low, medium, and high spatial resolutions. Freely-available high temporal resolution images are often acquired in medium (or low) spatial resolution and high spatial resolution images usually suffer from a low temporal resolution or from high costs. Moreover, high spatial resolution ...
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Satellites acquire data in low, medium, and high spatial resolutions. Freely-available high temporal resolution images are often acquired in medium (or low) spatial resolution and high spatial resolution images usually suffer from a low temporal resolution or from high costs. Moreover, high spatial resolution images are prevented to use in modeling of processes such as evapotranspiration due to the lack of thermal bands. Evapotranspiration mapping with a high spatial and temporal resolutions have been always one of the main subjects in the field of remote sensing. Daily evapotranspiration mapping with a 30 meter spatial resolution is the aim of current study. The case study of the research is Amir-Kabir agro-industrial farms. For this purpose, among 36 bands of MODIS image, those being more spectrally similar to Landsat bands were selected. Then, SADFAT and STARFM algorithms were applied on Landsat 8 and MODIS images to simulate visible and infrared bands with daily temporal resolution and 30-m spatial resolution. Afterward, the simulated bands were used as input for SEBAL algorithm to calculate actual evapotranspiration. Comparing the results with the actual evapotranspiration derived from FAO-Penman-Monteith equation indicated a RMSE of 2.53 mm/day and R2 of 0.69. Also, an RMSE of 0.68 mm/day and R2 of 0.94 were derived when the actual evapotranspiration derived from the downscaled bands were compared with that derived from the Landsat-8 bands. Accordingly, these results showed the efficient performance of the downscaling framework proposed in this study.
