Algorithm of emissivity spectrum and temperature separation based on TASI data

建立了适合于TASI的热红外高光谱发射率的经验关系,以此修正了分离方法(TES)中的MMD模型,在对温度和发射率进行精度评价的基础上,将反演结果应用于城市地表温度的日较差分析。结果表明:(1)反演的温度与实测温度的平均绝对误差为1.8K,平均相对误差为0.59%;(2)地面实测宽波段发射率与反演发射率的平均差值为0.036,差值的标准差为0.032;(3)同种地物的发射率曲线的反演结果与实验室测量发射率曲线的波形是一致的;(4)温度日较差分析结果是合理的。因此,本文的反演精度能满足研究需求,将该方法应用于TASI数据的温度与发射率的分离是可行的。

The retrieval and application of emissivity spectrum and temperature are key issues in thermal infrared remote sensing. Thermal airborne hyperspectral imager has 32 bands from 8 ktm to I 1.5 [am, which can provide abundant useful information for the retrieval of emissivity spectrum and temperature. This paper establishes regression between MMD and 13 min using 274 laboratory reflectance and field emissivity spectra, analyzes its accuracy using the data field measuring, and evaluates urban surface diurnal temperature range. The result shows that： （l） the average absolute difference between the temperature of retrieval and the measurement is 1.8 K, and the relative difference is 0.59%; （2） the mean difference between the broadband emissivity and the mean emissivity from TASI is 0.036, and the standard error of difference is 0.032. Because of the effects of scale and atmosphere, the predicted value does not equal to the laboratory measurement, but the pattern of predicts is similar to the laboratory measurement; （3） the result of diurnal temperature range is reasonable. Therefore, the accuracy of this method can satisfy operational application and it is feasible to retrieve the emissivity spectrum and temperature for TASI data.