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Estimating Temperature Fields from MODIS Land Surface Temperature and Air Temperature Observations in a Sub-Arctic Alpine Environment

机译:利用亚北极高山环境中MODIS地表温度和气温观测值估算温度场

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Spatially continuous satellite infrared temperature measurements are essential for understanding the consequences and drivers of change, at local and regional scales, especially in northern and alpine environments dominated by a complex cryosphere where in situ observations are scarce. We describe two methods for producing daily temperature fields using MODIS “clear-sky” day-time Land Surface Temperatures (LST). The Interpolated Curve Mean Daily Surface Temperature (ICM) method, interpolates single daytime Terra LST values to daily means using the coincident diurnal air temperature curves. The second method calculates daily mean LST from daily maximum and minimum LST (MMM) values from MODIS Aqua and Terra. These ICM and MMM models were compared to daily mean air temperatures recorded between April and October at seven locations in southwest Yukon, Canada, covering characteristic alpine land cover types (tundra, barren, glacier) at elevations between 1,408 m and 2,319 m. Both methods for producing mean daily surface temperatures have advantages and disadvantages. ICM signals are strongly correlated with air temperature (R2 = 0.72 to 0.86), but have relatively large variability (RMSE = 4.09 to 4.90 K), while MMM values had a stronger correlation to air temperature (R2 = 0.90) and smaller variability (RMSE = 2.67 K). Finally, when comparing 8-day LST averages, aggregated from the MMM method, to air temperature, we found a high correlation (R2 = 0.84) with less variability (RMSE = 1.54 K). Where the trend was less steep and the y-intercept increased by 1.6 °C compared to the daily correlations. This effect is likely a consequence of LST temperature averages being differentially affected by cloud cover over warm and cold surfaces. We conclude that satellite infrared skin temperature (e.g., MODIS LST), which is often aggregated into multi-day composites to mitigate data reductions caused by cloud cover, changes in its relationship to air temperature depending on the period of aggregation.
机译:空间连续的卫星红外温度测量对于了解局部和区域尺度的变化的后果和驱动因素至关重要,尤其是在缺乏实地观测的复杂冰冻圈为主的北部和高山环境中。我们描述了两种使用MODIS“晴空”白天陆地表面温度(LST)产生每日温度场的方法。插值曲线平均每日表面温度(ICM)方法使用一致的昼夜气温曲线将单个白天Terra LST值插值为每日平均值。第二种方法是根据MODIS Aqua和Terra的每日最大和最小LST(MMM)值计算每日平均LST。将这些ICM和MMM模型与4月至10月在加拿大育空地区西南部七个地点记录的每日平均气温进行了比较,覆盖了海拔1408 m至2319 m之间的特征性高山土地覆盖类型(苔原,贫瘠,冰川)。两种用于产生平均日表面温度的方法都具有优点和缺点。 ICM信号与气温密切相关(R 2 = 0.72至0.86),但变异性相对较大(RMSE = 4.09至4.90 K),而MMM值与气温之间的相关性更强(R 2 = 0.90)和较小的可变性(RMSE = 2.67 K)。最后,将MMM方法汇总的8天LST平均值与气温进行比较时,我们发现相关性较高(R 2 = 0.84),而变异性较小(RMSE = 1.54 K)。与每日相关性相比,趋势不那么陡峭,y轴截距增加了1.6°C。这种影响可能是LST温度平均值受到温暖和寒冷表面上的云层差异影响的结果。我们得出的结论是,卫星红外皮肤温度(例如MODIS LST)通常会聚合为多日合成数据,以减轻由于云层覆盖而导致的数据减少,其与空气温度的关系会根据聚合时间而变化。

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