Search

Land surface temperature (LST) is a key parameter in land surface physics and depends on energy and water exchange processes with the atmosphere. The generalized single-channel (GSC) algorithm is an operational approach for retrieving LST from satellite sensors with only one thermal infrared channel. Until now, validations of the GSC algorithm usingin situdata were limited. In this study, Landsat 8 imagery and SURFace RADiation budget network (SURFRAD) ground observations were employed to test the accuracy of the GSC algorithm. The validation results show that the GSC algorithm can obtain high LST retrieval accuracy when the total atmospheric water vapour content is between 0.5 and 3 g cm−2, with coefficient of determination (R2) between the retrieved LST and the ground LST greater than 0.97 and mean absolute error (MAE) and root mean square error (RMSE) values of 1.57 K and 1.96 K, respectively. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and thein situLST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system. [ABSTRACT FROM PUBLISHER]