## 6 Discussion  
In this work, four mono-window and six split-window algorithms were analysed in terms of their suitability for long time series processing of AVHRR data. To make the algorithms comparable, new coefficients were calculated for all algorithms in the same simulated data framework. It was found that the performance of the split-window algorithms were very similar in all cases. The MAD stays below 0.4 K in non-humid atmospheres. From the analysis of performance all six split-window algorithms could be recommended. The performance of the mono-window algorithms differed considerably amongst the algorithms. The [PERSON] et al. 2001 and the linear method thereby showed the best performance, while the [PERSON] 1983 and the Jimenez-Munoz and [PERSON] 2003 algorithm performed less accurate. Due to its best performance, the [PERSON] et al. 2001 algorithm is suggested for long time series processing aiming at climate relevant trends. Even though the coefficients were calculated for different ranges of columnar water vapour, the accuracy of the algorithms decreases with increasing columnar water vapour. Nevertheless, it is suggested to use humidity dependent coefficients, as the error rises especially in less humid atmospheres in case of coefficients that do not consider humidity dependent ranges (case A). The additional splitting of the coefficients to separate temperature ranges improved the accuracy mainly in very humid conditions. In such cases, the splitting might lower the MAD a few tenth degree. In less-humid conditions, the splitting did not improve the accuracy. The sensor view angle is considered in all cases (A, B, and C), nevertheless, accuracy is still lower in case of higher view angles. As such, view angle, humidity, and first guess LST could serve as proxy for the data quality. Due to differing spectral response curves, it was assumed that the stability of a long time series of LST from AVHRR data would suffer in case of nonconsideration of the different sensors characteristics. It was shown that the neglecting of the influence of the spectral response curves would produce maximum differences ranging from 0.19 K to 0.71 K in case of the split-window algorithms. Maximum differences ranging from 0.10 K to 1.8 K (36.7 K for [PERSON] and [PERSON] 2003) in case of the mono-window algorithms would occur. Even if neglecting the case of [PERSON] and [PERSON] 2003, the numbers suggest using sensor dependent coefficients, as significant additional noise would be introduced to a long time series in case of nonconsideration. For the processing of long time series data not only the performance of an algorithm is important, but also its sensitivity to its input data. The uncertainty inherent to all input data is an additional  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Tile Nr. **} & \multirow{2}{*}{**Nr of**************Pixels**} & **MAD** & **MEAN** & **STDEV** & **MAD** & **MAD Mean** & **MAD** \\  & & **LST\({}_{\text{new}}\)** & **LST\({}_{\text{new}}\)** & **LST\({}_{\text{new}}\)** & **LST\({}_{\text{orig}}\)** & **LST\({}_{\text{orig}}\)** & **Emissivity (\%)** & **VZA (\%)** & **TCW** \\  & & **(K)** & **(K)** & **(K)** & **(K)** & **(K)** & **(K)** & **(kg/m\({}^{2}\))** \\ \hline
1 & 1544 & 0.97 & 0.21 & 1.22 & 3.59 & 0.009 & 0.64 & 10.66 \\
2 & 144 & 2.94 & 2  
source of noise, lowering the overall accuracy of a final LST product. As the availability of input data is non ideal and even poor in case of the early years when AVHRR was flown, algorithms with lower sensitivity to their input data are to be preferred to algorithms with a high sensitivity. It is not always straightforward to know about the associated error in an input dataset. However, some studies might indicate about the magnitude of the error of a certain variable. Ref. [41] for example compared three land surface broadband emissivity datasets. They found land cover dependent differences and RMSEs between 0.009 and 0.011.