Open AccessA split‐window algorithm for land surface temperature from advanced very high resolution radiometer data: Validation and algorithm comparison
Author(s) -
Coll César,
Caselles Vicente
Publication year - 1997
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/97jd00929
Subject(s) - advanced very high resolution radiometer , emissivity , remote sensing , brightness temperature , environmental science , algorithm , infrared window , radiative transfer , pathfinder , data set , sea surface temperature , radiometer , radiometry , atmospheric correction , calibration , weighting , meteorology , brightness , mathematics , computer science , geology , physics , satellite , optics , infrared , statistics , astronomy , library science , acoustics
A split‐window algorithm for deriving land surface temperatures (LSTs) from advanced very high resolution radiometer (AVHRR) channels 4 and 5 is proposed and validated with in situ measured temperatures. On the basis of the radiative transfer theory the algorithm defines a set of surface‐independent coefficients which are equivalent to the classical split‐window coefficients for sea surface temperature (SST). These coefficients are calculated using SST matchups (coincident AVHRR and buoy measurements) provided by the National Oceanic and Atmospheric Administration (NOAA)‐NASA Pathfinder Database of worldwide measurements. Thus calibration of the split‐window coefficients is done using real data. The variability of atmospheric attenuation is represented in the proposed algorithm by a quadratic dependence on the brightness temperature difference. For LST determination the emissivity effect is modeled through an additive coefficient which depends on surface emissivity in the AVHRR channels 4 and 5. The algorithm is validated for both SST and LST by using independent ground‐based and AVHRR data. The database used in the validation of LST was obtained for a wide range of surface types in a semiarid environment. The same databases are used to compare the accuracies of other published split‐window algorithms. The proposed algorithm yields standard errors of temperature estimate between ±1.0 and ±1.5 K, and no significant biases are observed. Although results are encouraging, more validation is required principally for moist atmospheric conditions.