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Emissivity Determination for Land Surface Temperature estimation of Iran using AVHRR Thermal Infrared Data
Conclusion
Accurate LST maps are strongly required for many applications, notably agrometeorology climate and environmental studies. Satellite remote sensing in the infrared provides an interesting alternative for the global and continuous measurements of this parameter. One of the problems in LST estimation using remote sensing data is the emissivity effect. The emissivity mainly depends on land surface type, condition, and the wavelength considered, and hence rather difficult to apply. Neglecting these variations could introduce large errors in LST estimation. In order to construct LST model, emissivity effect has been accounted in this study. The emissivity values for different land covers have been determined using three complementary NDVI, geological and classification maps using AVHRR images. The coefficient values for LST algorithm in desert regions in Iran have been computed using a least-square linear regression and obtained RMSE is around 1.13. However, determination of exact coefficients for LST algorithm for the whole country requires more ground observations and analysis, hence, the model coefficients is at final calibration stage.
Acknowledgment
The authors would like to acknowledge Islamic Republic of Iran Meteorological Organization (IRIMO) for providing meteorological data and also fruitful suggestions and help from Mrs.Mehdizadeh of Organization of Geological Survey of Iran.
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