# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 5 Discussion  
In this study, we have developed an uncertainty approach that attempts to quantify and appropriately propagate uncertainties from the individual effects whose errors have distinct correlation properties. We have applied this approach in a first instance to MODIS data, and a necessary prerequisite is to derive a set of retrieval coefficients from model fitting, from which components of the uncertainty model can be determined. While the functional form of the retrieval algorithm remains the same as the operational MxD11 algorithm the coefficients are derived from a different set of profile data. Furthermore, we have taken an alternative approach to the input emissivity estimates. While a focus of the study has been a consistent approach to uncertainty estimation for LST data, it is pertinent to also assess the quality of the LST data retrieved with this new implementation of the standard generalised split-window algorithm, which we have done through validation of LSTs against in situ data. In addition to the derivation of a fully characterised Level-2 uncertainty model for MODIS LST data, this study has also produced an improved Level-2 LST dataset relative to the operational Collection 5 MxD11 product. In particular, the differences against ground-based validation data are lower for this new implementation of the GSW. This new implementation is publically available on the UK CEDA Archive ([[http://archive.ceda.ac.uk/](http://archive.ceda.ac.uk/)]([http://archive.ceda.ac.uk/](http://archive.ceda.ac.uk/))) with a dataset name of \"EUSTACE/GlobTemperature: Global clear-sky land surface temperature data from MODIS Terra/Aqua on the satellite swath with estimates of uncertainty components, v2.1, 2000-2016\". While no immediate updates to recent years are scheduled, it is foreseen to update to the end of 2018 at minimum and to compare the validation performance against the most recent operational Collection 6 MODIS MxD11 product. The validation of the uncertainty model--the first such validation of LST uncertainties--shows the satellite uncertainty model for LST is reasonably capturing the main sources of uncertainty in the satellite observations. There is in general good agreement at low uncertainty values and consistent behaviour across many sites, which provides some evidence that the satellite uncertainty model is capturing most of the main sources of uncertainty. Future work will focus on the upscaling uncertainty, uncertainty due to instrument calibration, and assess the feasibility of integrating the most advanced approaches, such as [60], to cloud detection uncertainty into the uncertainty model for LST presented here. Such work would be intended to address any underestimation at low uncertainties and overestimation at high uncertainties. Previous quantification of LST uncertainty budgets have placed a caveat upon their uncertainty budgets due to lack of understanding of cloud detection errors [16; 27; 73]. While our study also adds this caveat, the consistent approach to uncertainty characterisation does however provide a more robust framework for incorporating additional, as yet unquantified, uncertainty components. One of these additional uncertainty components to be assessed in future work is the impact on LST as a result of the presence of high aerosol loading. The rationale here is that coarse mode aerosol, such as dust--a mineral tropospheric aerosol--can modify up-welling radiances over a broad range of wavelengths in the thermal infrared including the split-window radiances [74]. The estimation of LST from a satellite as--with any measurand--is bound by its uncertainty budget. This budget itself must also be treated as a measurand, in that each uncertainty component is itself an estimate of the true lack of knowledge. Thus validation of the uncertainty budget is recommended to facilitate a better understanding of this measurand. This study represents to our knowledge a first attempt to assess the uncertainty components of LST retrieval.