English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Land surface temperature is one of the most important parameters in environmental studies. Having satellite imageries with spatiotemporal resolution leads to better interpretation, analysis and clarity of images; therefore the best way to solve this problem is to combine images with high spatial and temporal resolution. There is no satellite that captures thermal band with both spatial and temporal resolution simultaneously due to technical difficulties and considerable cost. Therefore, the aim of this article is using SADFAT algorithm for providing land surface temperature images with spatial resolution of Landsat and temporal resolution of Modis. This paper uses seven dates of Modis and Landsat including 24th May, 9th June, 11th July, 27th July, 12th Aug, 28Aug and 13th September of Salman Farsi sugar cane Industry. The results are evaluated with four indexes of correlation coefficient, Average difference, Mean Absolute Error and Universal Image Quality Index. Comparison of predicted and observed images indicate that the value of indexes correlation coefficient, Root Mean Square Eroor, Mean Absolute Error and Universal Image Quality Index are between 0.85-0.99, 0.731.32, 0.58-1.73, 0.9124-0.9973. The results showed high, reliable and precession of SADFAT algorithm for providing daily land surface temperature with spatial resolution of Landsat in case study.

Derivation daily and high spatial resolution Land Surface Temperature using Fusion of Landsat and Modis Satellite Imagery