Assessments of Seasonal Variability of Land Surface Temperature Using Multi-Resolution Satellite Data of the Year 2021 In Case of Tana Sub Basin North West Ethiopia
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Date
2022-02-05
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Addis Ababa University
Abstract
Quantifying Land Surface Temperature (LST) has a great role for biophysical and landscape monitoring like that of hydrology, urban management and environment. LST is a fundamental physical property relevant to many hydrological and atmospheric processes. The objective of this study is to assess the Spatial inter-seasonal variability of LST using Multi-resolution satellite Data of dry and wet (rainy) season of the year 2021 in the case of Tana Sub Basin, North West Ethiopia. Split Window Algorithm (SW) was used to retrieve LST, Mann-Kendall trend test for inter season trend analysis and the Pearson correlation coefficient (r) for correlation analysis. In this study, LST from three satellite images was retrieved and downloaded to see the Spatial and inter-seasonal LST variation. The maximum LST was gained from Sentinel-3 in April with LST 51°C, whereas the minimum LST was 5°C from Landsat 8 during February of the dry season. Similarly, the maximum LST in wet (rainy) season was extracted from Landsat 8 in June with LST 42°C and the minimum was in Sentinel-3 in July with 8°C. Spatially the maximum LST was observed in the periphery of the study area. The minimum LST was observed in central parts of the study area, this is due to Lake Tana. The relationship between LST obtained from Landsat 8, MODIS, and Sentinel-3 and mean temperature shows strong values of r >= 0.5 in dry and wet (rainy) season, except July of Sentinel-3. The correlation result shows a better fit between temperature and LST results obtained from Landsat 8 followed by MODIS in dry season. Similarly, Landsat 8 has a better correlation followed by MODIS in wet (rainy). In general, LST retrieved from Landsat 8 thermal bands shows better than the other two. From Mann-Kendall trend test for both Landsat 8 and MODIS LST there was statistically insignificant increasing, whereas for Sentinel-3 no trend in dry season. While, in wet (rainy) season the Mann-Kendall trend tests for both Landsat 8 and MODIS LST, there was a statistically insignificant decreasing trend. In contrast Sentinel-3 mean LST revealed statistically insignificant decreasing trend. The study result showed that, satellite based LST retrieval is time and cost effective. Therefore, it is recommended to be used with caution.
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Keywords
LST, SCA, Seasonal Variability, SWA