###### Abstract  
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 ([PERSON] 1983, [PERSON] et al., 2001, [PERSON] and [PERSON] 2003, linear approach) and six split-window algorithms ([PERSON] 1984, [PERSON] 1990, [PERSON] et al., 1994, [PERSON] 1996, [PERSON] 2008, [PERSON] and [PERSON] 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 [PERSON] and [PERSON] 1990 and [PERSON] 1984 split-window algorithm to perform best. Amongst the mono-window algorithms, larger deviations occurred in terms of accuracy, precision and sensitivity. The [PERSON] et al., 2001 algorithm was found to be the best performing mono-window algorithm. A short comparison of the application of the [PERSON] and Li 1990 coefficients to AVHRR with the MODIS LST product confirmed the approach to be physically sound. L 20179 722017 9 722017 10.3390/rs9010072 10.  
models (RTMs) and assumptions of the composition of the atmosphere. Given that the atmosphere is well characterized, this method can be very accurate. However, main constraint of such approaches is the high CPU usage of the RTMs, which makes them unsuitable for the processing of extensive time series with pixel-based approaches. More straightforward is the application of methods, which are based on pre-computed functions and coefficients. These allow an effective data processing. A selection of such methods is described in this article specifically for the case of AVHRR bands. A more complete overview of methods is given for example in [1]. Another fast option to retrieve LST are neural networks as described for example in [2]. The estimation of LST from AVHRR/2, AVHRR/3 (13 sensors on the NOAA satellites and now AVHRR/3 on Metop-A and Metop-B), as well as AVHRR-heritage sensors, can be accomplished taking advantage of the splitting of the thermal domain into two channels. The two channels are in wavelength ranges with different absorption features of mainly water vapour. As such, the difference of the two channels can be used to eliminate these atmospheric effects in the data. The two channels are usually located around 11 and 12 \(\upmu\)m. Measured brightness temperatures of these two channels can be directly used with the respective surface emissivities to deduct LST. The split-window technique is simple and robust [3] and therefore favoured by many researchers and also operational providers of LST (e.g., Land Surface Analysis Satellite Applications Facility (LSA SAF) [4], Copernicus Land [5], NASA MODIS LST [6]). AVHRR/1 however carries only one band in the thermal domain, only mono-window algorithms are applicable here. These algorithms are missing any measured information about atmospheric absorption; correction procedures are fully dependent on external data. Due to reduction to one band the accuracy of mono-band algorithms is expected to be lower than split-window algorithms [7]. Nevertheless, [8] reported accuracies for mono-window algorithms which are comparable to what was found for split-window algorithms.