# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 6 Conclusions  
Earth observation satellites offer an opportunity to obtain global coverage of LST, with the appropriate exploitation of data from multiple instruments providing a capacity to resolve the diurnal cycle on a global scale. To confront such a challenge, as a minimum, both a consistent algorithm and a consistent approach to uncertainty analysis, which distinguishes and quantifies the different components: random, locally correlated and large-scale systematic, should be employed. An accurate assessment of the uncertainty budget is thus critical. Any observation of LST from a satellite or any other observing instrument is subject to an incomplete lack of knowledge. Even though the physical process of retrieving LST from split-window radiances is well established, any algorithm will have inherent uncertainties--ranging from systematic uncertainty in the radiative transfer code employed to generate retrieval coefficients, to imperfect identification of cloud contamination. Furthermore, a consistent uncertainty budget with a common nomenclature for uncertainty and accuracy is a powerful tool enabling users to explore and compare the differences between LST datasets. We present in this study a comprehensive and consistent approach to determining an uncertainty budget for LST products, which distinguishes and quantifies the different components: random, locally correlated and large-scale systematic. This approach is applied to MODIS data and necessarily requires a new implementation of the GSW algorithm. Validation of this new LST product shows a mean absolute daytime bias with respect to the reference in situ data of 0.37 K. The mean absolute bias for nighttime is 0.73 K. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. These statistics show improvement on the corresponding biases and standard deviations for the MYD11_L2 operational product. Goodness-of-fit tests show that the uncertainty model provides a good representation of the observed uncertainties. The developments made here not only result in a new dataset of LST from the MODIS instruments, but also provide a framework for quantifying uncertainties for LST estimation from satellite observedTOA radiances that is equally applicable across different sensors and different retrieval approaches. Future work will focus on the impact on the LST uncertainty model as a result of missed clouds, high aerosol loading, and instrument calibration, utilising techniques being developed in studies such as FIDUCEO [75], and Land Surface Temperature CCI ([[http://cci.esa.int/lst/](http://cci.esa.int/lst/)]([http://cci.esa.int/lst/](http://cci.esa.int/lst/))). The framework we present will enable datasets of LST to be compared with each other with greater certainty, hence increasing confidence in our knowledge of recent surface temperature changes over land. This approach underlines a new direction for LST research, demonstrating that LST datasets can achieve uncertainties of better than 1.0 K, particularly in mid-latitudes. The error framework provides a clear guide to areas of focus where improvements may results in lower errors, e.g., the Sahara and where errors are dominated by natural phenomena, such as mostly cloudy regions. For the first time, exploitation of the data can be secured by knowledge of uncertainties appropriate to the region of the world under study. Conceptualization, D.G. and J.R.; Formal analysis, T.T. and H.S. ; Investigation, K.V. and E.D. ; Methodology, D.G. and K.V. ; Software, K.V. and E.D. ; Supervision, J.R.; Validation, T.T. ; Writing--original draft, D.G. ; Writing--review & editing, K.V., E.D. and J.R. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 640171 with additional funding received from the European Space Agency within the framework of the GlobTemperature project under the Data User Element of ESA's Fourth Earth Observation Envelope Programme (2013-2017), contract number 4000109452/13/I-AM.