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Assessment of Mono- and Split-Window Approaches for Time Series Processing of LST from AVHRR—A TIMELINE Round Robin

机译：评估AVHRR的LST时间序列处理的单窗口方法和分割窗口方法的评估—时间轴循环法

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Processing of land surface temperature from long time series of AVHRR (Advanced Very High Resolution Radiometer) requires stable algorithms, which are well characterized in terms of accuracy, precision and sensitivity. This assessment presents a comparison of four mono-window (Price 1983, Qin et al., 2001, Jiménez-Muñoz and Sobrino 2003, linear approach) and six split-window algorithms (Price 1984, Becker and Li 1990, Ulivieri et al., 1994, Wan and Dozier 1996, Yu 2008, Jiménez-Muñoz and Sobrino 2008) to estimate LST from top of atmosphere brightness temperatures, emissivity and columnar water vapour. Where possible, new coefficients were estimated matching the spectral response curves of the different AVHRR sensors of the past and present. The consideration of unique spectral response curves is necessary to avoid artificial anomalies and wrong trends when processing time series data. Using simulated data on the base of a large atmospheric profile database covering many different states of the atmosphere, biomes and geographical regions, it was assessed (a) to what accuracy and precision LST can be estimated using before mentioned algorithms and (b) how sensitive the algorithms are to errors in their input variables. It was found, that the split-window algorithms performed almost equally well, differences were found mainly in their sensitivity to input bands, resulting in the Becker and Li 1990 and Price 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The Qin et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the Becker and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound.
机译：从长时间序列的AVHRR（高级超高分辨率辐射计）处理地表温度需要稳定的算法，这些算法在精度，精度和灵敏度方面都有很好的特征。该评估比较了四个单窗口算法（Price 1983，Qin等人，2001，Jiménez-Muñoz和Sobrino 2003，线性方法）和六个分割窗口算法（Price 1984，Becker and Li 1990，Ulivieri等人）。 （1994年，Wan和Dozier，1996年，Yu，2008年，Jiménez-Muñoz和Sobrino，2008年），从大气亮度，发射率和柱状水蒸气的顶部估算LST。在可能的情况下，估计新系数以匹配过去和现在的不同AVHRR传感器的光谱响应曲线。为了避免在处理时间序列数据时出现人为异常和错误趋势，必须考虑独特的光谱响应曲线。使用基于大型大气剖面数据库的模拟数据，该数据库涵盖了大气，生物群落和地理区域的许多不同状态，评估了（a）使用上述算法可以估算出LST的准确性和精密度，以及（b）敏感度如何该算法是在其输入变量中的错误。结果发现，分割窗口算法的性能几乎相同，差异主要体现在对输入频带的敏感性上，从而使Becker和Li 1990和Price 1984分割窗口算法表现最佳。在单窗口算法中，在准确性，精度和灵敏度方面出现较大偏差。 Qin et al。，2001算法被认为是性能最好的单窗口算法。将Becker和Li 1990系数与MODIS LST产品应用于AVHRR的简短比较证实了该方法在物理上是合理的。

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