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This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jiménez-Muñoz 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 • cm −2 . For values higher than 3 g • cm −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 "ground-truth" map provided an rmse of 1.5 K.

[1] 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)(2003)(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 R 2 = 0.60 and averaged root mean square error (RMSE) = 1.75°C for daily temperatures, and averaged R 2 = 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.Citation: Cristóbal, J., M. Ninyerola, and X. Pons (2008), Modeling air temperature through a combination of remote sensing and GIS data,

[1] Land surface temperature (LST) is involved in many land surface processes such as evapotranspiration, net radiation, or air temperature modeling. In this paper we present an improved methodology to retrieve LST from Landsat 4 TM, Landsat 5 TM, and Landsat 7 ETM+ using four atmospheric databases covering different water vapor ranges (from 0 to 8 g cm À2 ) to build the LST retrieval models and using both water vapor and air temperature as input variables. We also compare this with LST retrievals using only water vapor or only air temperature, as well as with an existing LST retrieval algorithm. In order to validate the results, we have selected 77 Landsat images taken between 2002 and 2006 (Catalonia, northeast of the Iberian Peninsula) and two sources of water vapor (radiosounding data and remote sensing estimations) and air temperature (radiosounding data and air temperature modeling). The best results using radiosounding data are obtained when both air temperature and water vapor are present in the LST retrieval models with a mean RMSE of 0.9 K, followed by only water vapor models with a mean RMSE of 1.5 K and only air temperature models with a mean RMSE of 5.6 K. The results obtained using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 water vapor product and at-satellite-pass air temperature modeling as input data also show that this kind of input data offers best results, with a mean RMSE of 0.9 K, followed by water vapor models with a mean RMSE of 2.1 K and only air temperature models with a mean RMSE of 5.6 K. Similar errors when using radiosounding or modeled water vapor and air temperature as input data suggest the avoidance of radiosounding data to retrieve LST over extensive areas. Finally, when comparing the presented methodology with another methodology also using water vapor and air temperature as input data, the improvement is of more than 0.5 K.Citation: Cristóbal, J., J. C. Jiménez-Muñoz, J. A. Sobrino, M. Ninyerola, and X. Pons (2009), Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature,

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.