Title :
A simplified method for estimating the total water vapor content over sea surfaces using NOAA-AVHRR channels 4 and 5
Author :
Sobrino, J.A. ; Jimenez, J.C. ; Raissouni, N. ; Soria, G.
Author_Institution :
Dept. of Thermodynamics, Valencia Univ., Spain
fDate :
2/1/2002 12:00:00 AM
Abstract :
A simplified method for estimating the total amount of atmospheric water vapor, W, over sea surfaces using NOAA-AVHRR Channels 4 and 5 is presented. This study has been carried out using simulated AVHRR data at 11 and 12 μm (with MODTRAN 3.5 code and the TIGR database) and AVHRR, PODAAC, and AVISO databases provided by the Louis Pasteur University (Strasbourg-France), NASA-NOAA, and Meteo France, respectively. The method is named linear atmosphere-surface temperature relationship (LASTR). It is based on a linear relationship between the effective atmospheric temperature in AVHRR Channel 4 and sea surface temperature. The LASTR method was compared with the linear split-window relationship (LSWR), which is based on a linear regression between W and the difference of brightness temperature measured in the same channels (ΔT=T4-TS). The results demonstrate the advantage of the LASTR method, which is capable of estimating W from NOAA-14 afternoon passes with a bias accuracy of 0.5 g cm-2 and a standard deviation of 0.3 g cm-2, compared with the W obtained by the AVISO database. In turn, a global bias accuracy of 0.1 g cm-2 and a standard deviation within 0.6 g cm-2 have been obtained in comparison with the W included in the PODAAC database derived from the special sensor microwave/imager (SSM/I) instrument
Keywords :
atmospheric humidity; atmospheric techniques; humidity measurement; remote sensing; 10 to 12.5 micron; AVHRR; IR radiometry; LASTR; channel 4; channel 5; humidity; infrared radiometry; linear atmosphere surface temperature relationship; linear relationship; marine atmosphere; measurement technique; meteorology; satellite remote sensing; water vapor; water vapour; Atmospheric measurements; Atmospheric modeling; Brightness temperature; Image databases; Image sensors; Linear regression; Ocean temperature; Sea measurements; Sea surface; Temperature measurement;
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on