Estimation of Surface Temperature in Agricultural Lands Using Satellite Images (Case Study: Soleimanshah Irrigation Network)

Document Type : Original Article

Authors

1 Department of Water Engineering, Faculty of Agricultural Science and Engineering, Razi University, Kermanshah, Iran

2 Department of Water Engineering, Razi University, Kermanshah, Iran

3 Department of Water Engineering, Faculty of Science and Agricultural Engineering, Razi University, Kermanshah, Iran

Abstract

Land surface temperature is a significant variable involved in land surface energy and water balance and is a substantial component in many aspects of environmental research. The land surface temperature is usually calculated based on thermal bands. Landsat 8 satellite thermal bands are the newest infrared thermal bands, included two adjacent thermal bands with a spatial separation of 30 meters. There are several methods for calculating land surface temperature. These methods are of three groups: Methods that only need satellite data, methods that require satellite data and leaf area index (LAI), and Methods that require satellite data and meteorological data. In this study, the land surface temperature simulated by the Planck Inverse Function, SEBAL algorithm, Statistical Mono-Window algorithm, Split Window Algorithm, Mono-Window Algorithm, Radiation Transfer Equation, Sabrino Split Window Algorithm, National Oceanic and Atmospheric Administration Joint Polar Satellite System, And the Single-Channel Algorithm and compared with the surface temperature measured in the LPT2 construction area of ​​Soleimanshah irrigation network during the growing season of nut sunflower in 2020 based on two criteria of R 2 and RMSE. The results showed the Planck Inverse Function, SEBAL algorithm Statistical Mono-Window algorithm, Split Window algorithm, and Mono Window algorithm respectively have high accuracy (Those approaches are not dependent on meteorological data). Among them, the Planck Inverse Function with values of R2 and RMSE of 0.6 and 4.2 ° C, respectively has the highest accuracy. The Sabrino Split Window algorithm, National Oceanic and Atmospheric Administration Joint Polar Satellite System, and the Single-Channel algorithm, respectively have low accuracy.

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References

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Irrigation and Water Engineering
Volume 13, Issue 1 - Serial Number 49
October 2022
Pages 141-160

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