Validation of MODIS Land Surface Temperature Products with Ground-Based Measurements: A Case Study in the Bajestan Desert, Iran
محورهای موضوعی : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیاییMorteza Kaffash 1 , Seyed Hoseyn Sanaei Nejad 2
1 - Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran
2 - Department of Water Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Iran
کلید واژه: LST, SMT160, Satellite, Validation, MODIS ,
چکیده مقاله :
Objective: Land surface temperature (LST) is a critical parameter for environmental studies, including climate change analysis, soil moisture monitoring, evapotranspiration estimation, and surface energy balance evaluation. This study aims to validate the accuracy of MODIS LST products (MOD11A1 and MYD11A1) from Terra and Aqua satellites in the Bajestan Desert, Iran, by comparing them with ground-based measurements.
Methods: Ground-based LST measurements were conducted using six thermometers equipped with SMT160 temperature sensors over 15 clear-sky days and nights. MODIS LST products were validated using two approaches: (1) comparison with pixel-level ground-based data and (2) comparison with the average LST values of image windows larger than the pixel size (e.g., 3×3, 5×5). Statistical parameters, including root mean square error (RMSE), coefficient of determination (R²), and standard deviation, were calculated to assess the accuracy of satellite-derived LST.
Results: The results indicate that MODIS LST products systematically underestimate LST in the barren study area. Nocturnal LST exhibited higher accuracy (RMSE = 1.1) compared to diurnal measurements (RMSE = 3.38). Increasing the size of the window used for averaging resulted in higher standard deviations of pixel temperatures, while RMSE and R² values showed negligible changes, demonstrating the homogeneity of the selected study area.
Conclusion: This study validates the applicability of MODIS LST products in arid environments despite their systematic underestimation. The findings highlight the importance of incorporating homogeneous sampling areas and suggest the need for further improvements in MODIS algorithms for arid regions. The methodologies applied in this study provide a robust framework for LST validation in other arid and semi-arid environments.
Objective: Land surface temperature (LST) is a critical parameter for environmental studies, including climate change analysis, soil moisture monitoring, evapotranspiration estimation, and surface energy balance evaluation. This study aims to validate the accuracy of MODIS LST products (MOD11A1 and MYD11A1) from Terra and Aqua satellites in the Bajestan Desert, Iran, by comparing them with ground-based measurements.
Methods: Ground-based LST measurements were conducted using six thermometers equipped with SMT160 temperature sensors over 15 clear-sky days and nights. MODIS LST products were validated using two approaches: (1) comparison with pixel-level ground-based data and (2) comparison with the average LST values of image windows larger than the pixel size (e.g., 3×3, 5×5). Statistical parameters, including root mean square error (RMSE), coefficient of determination (R²), and standard deviation, were calculated to assess the accuracy of satellite-derived LST.
Results: The results indicate that MODIS LST products systematically underestimate LST in the barren study area. Nocturnal LST exhibited higher accuracy (RMSE = 1.1) compared to diurnal measurements (RMSE = 3.38). Increasing the size of the window used for averaging resulted in higher standard deviations of pixel temperatures, while RMSE and R² values showed negligible changes, demonstrating the homogeneity of the selected study area.
Conclusion: This study validates the applicability of MODIS LST products in arid environments despite their systematic underestimation. The findings highlight the importance of incorporating homogeneous sampling areas and suggest the need for further improvements in MODIS algorithms for arid regions. The methodologies applied in this study provide a robust framework for LST validation in other arid and semi-arid environments.
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