The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 μm, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm^-2. For values higher than 3 g middotcm^-2, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measured in situ at the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

Land surface temperature (LST) is one of the sources of input data for modeling land surface processes. The Landsat satellite series is the only operational mission with more than 30 years of archived thermal infrared imagery from which we can retrieve LST. Unfortunately, stray light artifacts were observed in Landsat-8 TIRS data, mostly affecting Band 11, currently making the split-window technique impractical for retrieving surface temperature without requiring atmospheric data. In this study, a single-channel methodology to retrieve surface temperature from Landsat TM and ETM+ was improved to retrieve LST from Landsat-8 TIRS Band 10 using near-surface air temperature (T_a) and integrated atmospheric column water vapor (w) as input data. This improved methodology was parameterized and successfully evaluated with simulated data from a global and robust radiosonde database and validated with in situ data from four flux tower sites under different types of vegetation and snow cover in 44 Landsat-8 scenes. Evaluation results using simulated data showed that the inclusion of T_a together with w within a single-channel scheme improves LST retrieval, yielding lower errors and less bias than models based only on w. The new proposed LST retrieval model, developed with both w and T_a, yielded overall errors on the order of 1 K and a bias of −0.5 K validated against in situ data, providing a better performance than other models parameterized using w and T_a or only w models that yielded higher error and bias.

Air temperature is involved in many environmental processes such as actual and potential evapotranspiration, net radiation and species distribution. Ground meteorological stations provide important local data of air temperature, but a continuous surface for large and heterogeneous areas is also needed. In this paper we present a hybrid methodology between Remote Sensing and Geographical Information Systems to retrieve daily instantaneous, mean, maximum and minimum air temperatures (2002–2004) as well as monthly and annual mean, maximum and minimum air temperatures (2000–2005) on a regional scale (Catalonia, northeast of the Iberian Peninsula) by means of multiple regression analysis and spatial interpolation techniques. To perform multiple regression analysis we have used geographical and multiresolution remotely sensed variables as predictors. The geographical variables we have included are altitude, latitude, continentality and solar radiation. As remote sensing predictors, we have selected those variables that are most closely related with air temperature such as albedo, land surface temperature (LST) and NDVI obtained from Landsat‐5 (TM), Landsat‐7 (ETM+), NOAA (AVHRR) and TERRA (MODIS) satellites. The best air temperature models are obtained when remote sensing variables are combined with geographical variables: averaged R² = 0.60 and averaged root mean square error (RMSE) = 1.75°C for daily temperatures, and averaged R² = 0.86 and averaged RMSE = 1.00°C for monthly and annual temperatures. The results also show that combined models appear in a higher frequency than only geographical or only remote sensing models (87%, 11% and 2% respectively) and that LST and NDVI are the most powerful remote sensing predictors in air temperature modeling.