Retrieval of the reflectance and land surface temperature of the newly-launched Landsat 8 satellite

The quality of Landsat 8sensor data has been improved after a series of corrections and data reprocessing since its successful launch on February 11,2013.These include the correction of all calibration parameters,the improvement of the radiance conversion coefficients for the all Operational Land Imager(OLI)sensor bands,the refinement of the OLI detector linearization,the radiometric offset correction for the two Thermal Infrared Sensor(TIRS)bands,the slight improvement to the geolocation of the TIRS data,and the reprocessing of all Landsat 8data held in the USGS archives.In addition,several algorithms specially developed for Landsat 8data have also been proposed over the pass year.This paper aims to assess the accuracy of the retrieved the reflectance of the OLI sensor and the land surface temperature(LST)of the TIRS sensor of the new satellite with those of the well-calibrated Landsat 7ETM+sensor and the ground-measured LST.This study retrieved the top of the atmosphere(TOA)reflectance of the OLI multispectral bands and the LST of the TIRS thermal bands with the most recent calibration parameters,algorithms and USGS-processed Landsat 8image data.In addition,the Chavez′s COST model has been introduced to correct the atmospheric effects on the OLI multispectral bands.Toexamine the performance of the calibration parameters and the developed algorithms,the retrieved TOA reflectance of each multispectral band and the computed LST of the thermal infrared bands have been compared with that of the corresponding band of the synchronized,wellcalibrated Landsat 7data and the in situ LST,respectively.The results show that the current Landsat 8calibration parameters of multispectral bands can achieve high accuracy for the retrieval of TOA reflectance.The proposed COST-based atmospheric correction algorithm can also have a nearly identical performance when compared with the Landsat 7 COST model′s result.Nevertheless,two recently-proposed split window algorithms for computing the LST from Landsat 8thermal infrared bands did not perform well,as they offered a large difference between the algorithm-modeled LST and the ground-measured LST.Given the scaling parameters of the TIRS thermal infrared band 11 is still unstable,as announced by the Landsat 8project team,it is recommended that at this stage users might use the single channel(SC)algorithm of Jimenez-Muoz and Sobrino to retrieve the LST from Landsat 8thermal band 10(like working on Landsat TM/ETM + band 6)rather than attempt a splitwindow algorithm using both TIRS bands 10 and 11.However,care should be taken in the selection of correct atmosphere parameters for the SC-based LST computing,especially when a very high atmospheric water vapor condition occurs.