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Comparison of Different Retrieval Temperature algorithms With different Emissivity by Using remote sensing images
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Ali Hossingholizade *   1, Parviz Zeaiean , Parisa Beyranvand |
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Abstract: (3153 Views) |
Abstract:
Land surface temperature (LST) is the main factor in the energy balance of the Earth, and is used as input for climate and environmental changes models. In the past and even now, due to the lack or insufficient number meteorological stations, temperature has not been measured and recorded in many parts of the country, therefore, there is no accurate information on temperature changes in these regions. So there is a need for a method to measure this parameter that is fast and accurate as well as being the low cost and covering a wide area. Using remote sensing data due to its wide coverage, the availability of many images and rich archives is a good option for estimating this parameter. In this research, various methods for extracting land surface temperature including Mono-window, Artis and Stefan-Boltzman were investigated. In each method, emissivity was calculated from different ways, including NDVI, Classification and MODIS Product, and then entered into their equations. Landsat series including TM, ETM +, OLI and MODIS emissivity products were also used. To convert the surface temperature to one and half meters height temperature, a precise environmental thermometer with a precision of 0.2 ° C and a linear relationship (air temperature= 0.44 × LST + 8.8) were used. Then, the results were tested using weather station data via RMSE and t-test. The final results showed that the best method with RMSE 1.09 is using the ETM + image and the Stefan-Boltzman method, and the worst method with RMSE 2.64 is belonging to the use the MODIS image and the Mono-Window method. Therefore, it is recommended to use Stefan-Boltzman method for other regions which are geographically similar to this studied area. |
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| Keywords: Artis, Stefan_Boltzman, Mono_Window, Tehran, LST |
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Full-Text [PDF 1396 kb]
(707 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2019/03/16 | Accepted: 2019/07/14 | Published: 2021/02/28
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