Aguirre M., Berruti B., Bezy J-L., Drinkwater M., Heliere F., Klein U., Mavrocordatos C., Silvestrin P., Greco B., Benveniste J. (2007): Sentinel-3 - The Ocean and Medium-Resolution Land Mission for GMES Operational Services. ESA Bulletin 131, 25-29.

Coll C., Hook S. J., Galve J. M. (2009): Land Surface Temperature From the Advanced Along-Track Scanning Radiometer: Validation Over Inland Waters and Vegetated Surfaces. IEEE Transactions on Geoscience and Remote Sensing, 47, Issue 1, 350-360.

Ghent D., Kaduk J., Remedios J., Ardö J., Balzter H. (2010). Assimilation of land surface temperature into the land surface model JULES with an ensemble Kalman filter. Journal of Geophysical Research, Vol. 115, D19112, doi:10.1029/2010JD014392.

Ghent, D., Kaduk, J., Remedios, J., and Balzter, H. (2011) Data assimilation into land surface models: the implications for climate feedbacks. International Journal of Remote Sensing 32(3): 617-632

Kogler C., Pinnock S., Arino O., Casadio S., Corlett G., Prata F., Bras T. (2012). Note on the quality of the (A)ATSR land surface temperature record from 1991 to 2009. International Journal of Remote Sensing, Volume 33, Issue 13. 

Ghent D. (2011): LST User Exploitation Document. University of Leicester, UK.

LST User Exploitation Document:
This document concerns the promotion of AATSR LST data, and the liaison with users and other researchers working in the field of satellite LST to increase publicity and user exploitation and summarises user exploitation of LST.

Schneider P. et al. (2012): LST Validation Protocol. Norwegian Institute for Air Research, Kjeller, Norway; University of Leicester, UK.

LST Validation Protocol:
This document provides a classification and protocol for various methods of validating Land Surface Temperature (LST) derived from spaceborne thermal infrared instruments. Four categories of LST validation are established, namely (A) validation with in situ data, (B) radiance-based validation, (C) multi-sensor intercomparison, and (D) time series analysis. Each category is further subdivided into several classes, which approximately reflect the validation accuracy that can be achieved by the different approaches, as well as the complexity involved with each method. Advice on best practices is given for methodology common to all categories. Each category further gives recommendations on specific methodology that has proven to be valuable for each approach. Selection criteria used for distinguishing the accuracy classes are established for each category. Examples are provided for validation classes in each category, where available.

Schneider P. et al. (2012): LST Validation Site Requirements. Norwegian Institute for Air Research, Kjeller, Norway; University of Leicester, UK.

LST Validation Site Requirements:
This document concerns the production of a long term validation plan to be implemented for the validation of LST products within the framework of the Advanced Along-Track Scanning Radiometer (AATSR) and successive ESA instruments. An overview is given of sites with in situ measurements suitable for validating Land Surface Temperature (LST) derived from the Along-Track Scanning Radiometer (ATSR) series of instruments and the upcoming Sea and Land Surface Temperature Radiometer (SLSTR) instrument. Both active and inactive sites which are currently used by the University of Leicester for validating the AATSR LST product are listed. Furthermore, an overview of existing in situ LST data sets that are not currently used by UL is given. The coverage of biomes with in situ data is presented and gaps in the biome coverage are discussed. Finally, recommendations are given for inclusion of currently unused in situ dataset in the operational UL validation scheme and for the establishment of future long-term in situ sites.

Schneider P. et al. (2012): LST Validation Implementation Plan. Norwegian Institute for Air Research, Kjeller, Norway; University of Leicester, UK.

LST Validation Implementation Plan:
This document provides information about the strategic requirements and the pratical implementation of validating the Land Surface Temperature product from the Advanced Along-Track Scanning Radiometer (AATSR) series of instruments as well as the future Sea and Land Surface Temperature Radiometer (SLSTR) to be launched on the Sentinel-3 platform. The current validation status of the AATSR LST product is provided and the strategy for ongoing and future validation of the LST product by the University of Leicester is provided. Finally, the proprosed implementation of this LST validation strategy is described.

Prata F. (2011): Water Vapour Sensitivity of the (A)ATSR LST Algorithm. Norwegian Institute for Air Research, Kjeller, Norway.

Water vapour affects all of the infrared channels of the (A)ATSR, causing greater absorption at the shorter wavelength channel (11 µm) than at the longer wavelength channel (12 µm). This differential water vapour absorption effect is exploited in the (A)ATSR LST algorithm by assuming it is almost linear, and assigning a certain amount of the observed brightness temperature difference (T11-T12) due to water vapour absorption and another amount (of varying sign) to effects of the surface. Acknowledging that the water vapour effect is not always linear, a weak direct water vapour parametrization was introduced into the algorithm. The form of the parametrization is such that there is a dependence on the precipitable water amount (integrated total column) and the viewing angle. In this study we investigate the sensitivity of the parametrization to precipitable water using climatological values based on the NVAP climatology, and consider what improvements might be made by using different ancillary PW data-sets from NCEP re-analyses, and from ECMWF water vapour fields. Satellite-based water vapour measurements are also considered.

Prata F. (2011). Linearity of the (A)ATSR LST Algorithm. Norwegian Institute for Air Research, Kjeller, Norway. 

In some research studies of land surface temperature derivation from satellites, it has been suggested that there is a significant non-linear effect in the dependence of the brightness temperature difference (T11-T12) with ground temperature. Although there seems scant observational evidence for this and the theoretical justification is lacking, it seems relevant to investigate the validity of the claims. Here ground validation data from the Australian validation sites at Hay, NSW and Walpeup, Victoria are used together with coincident ATSR/ATSR-2 measurements to investigate the non-linearity effect. These data are arguably the most complete and accurate land surface temperature data currently available, and span a range of temperatures from 270 K to 330 K. No evidence is found for a significant non-linearity, the root-meansquare errors for the entire data set are ±4.49 K for a linear fit and ±4.50 for a quadratic fit. Furthermore, the non-linear effect only becomes apparent at quite high temperatures (Ts>320 K), and since the ATSR-2 11 µm channel saturates at about this temperature, the idea of using a questionable non-linear term is moot.

 Prata F. (2009): Scale Dependencies in the (A)ATSR LST Algorithm. Norwegian Institute for Air Research, Kjeller, Norway.

The (A)ATSR land surface temperature (LST) algorithm utilizes 1x1 km2 satellite measurements and ancillary data used to make corrections for land surface emissivity and atmospheric effects at much lower spatial resolution of 0.5o longitude x 0.5o latitude.  These scale differences cause artifacts in the LST products which often appear as sharp, straight boundaries aligned with lines of latitude and longitude.  The effect appears to be directly related to the way the LST coefficients are specified from the landcover map employed.  This study investigates the use of a higher spatial resolution landcover map with more biomes (or land classes) than the current map, and introduces a methodology for redistributing the classes so that new LST regression coefficients can be specified without the need for recalculation or further radiative transfer modeling.  The methodology is implemented and testes on a limited (A)ATSR data set covering a region of SE Australia for which LST validation data are available.  Suggestions for further research are given.

 Prata F. (2002): Land Surface Temperature Measurement from Space - AATSR Algorithm Theoretical Basis Document. CSIRO Atmospheric Research, Aspendale, Australia.

The theoretical basis for a land surface temperature (LST) product using the split window channels of the Advanced Along-Track Scanning Radiometer (AATSR) is given. Several restrictions imposed by the near-real time processor have affected the choice of algorithm. These are: (1) the algorithm must not utilise the visible channels of the AATSR, (2) the algorithm must be computationally fast, must not occupy large amounts of computer memory and must not utilise large amounts of disk space, (3) a method for cloud detection over the land must be implemented, and (4) a protocol for validation of the product must exist. The algorithm proposed uses pixel-by-pixel top-of-the-atmosphere cloud-free, calibrated and navigated day and night brightness temperatures from the 11 and 12 µm AATSR channels to produce a global LST product. Additional seasonally-dependent land cover classification, fractional vegetation and precipitable water data are required for the operation of the algorithm. The algorithm is fast and can be used globally by employing regression coefficients in a look-up table that is updatable. This document describes the mathematical basis for the algorithm, the ancillary data-sets required, exception handling, quality control flags and the validation strategy.

Noyes E J., Corlett G .K., Remedios J. J., Kong X., Llewellyn-Jones D. T. (2007): An Accuracy Assessment of AATSR LST Data using Empriical and Theoretical Methods. Proceedings of the ENVISAT Symposium 2007, Montreux, Switzerland, 23-27 April 2007.

Prata F. (2002):
The AATSR Global Land Surface Temperature Algorithm. Presentation at AATSR Working Group Meeting, 17 September 2002. (40 MB)

Remedios J., Comyn-Platt E., Corlett G., Veal K. (2011): Realising long-term data sets of land surface temperature.
Prata F., Zeller O., Corlett G., Remedios J., Kogler C. (2010): Land Surface Temperature Determination from the ATSR-Family of Instruments and the Sentinel-3 SLSTR. Poster presentation at the International TOVS Study Conferences ITSC-XVII, Monterey, California, 14-20 April 2010.

Zeller O., Remedios J., Corlett G., Prata F. (2011): Improved retrieval of land surface temperature by AATSR measurements.