A COMPARISON OF FOUR METHODS FOR DETERMINING PRECIPITABLE WATER VAPOR CONTENT FROM MULTI-SPECTRAL DATA

Determining columnar water vapor is a fundamental problem in remote sensing. This measurement is important both for understanding atmospheric variability and also from removing atmospheric effects from remotely sensed data. Therefore discovering a reliable and if possible automated method for determining water vapor column abundance is important. There are two standard methods for determining precipitable water vapor during the daytime from multi-spectral data. The first method is the Continuum Interpolated Band Ratio (CIBR) (see for example King et al. 1996). This method assumes a baseline and measures the depth of a water vapor feature as compared to this baseline. The second method is the Atmospheric Pre-corrected Differential Absorption technique (APDA) (see Schlaepfer et al. 1998); this method accounts for the path radiance contribution to the top of atmosphere radiance measurement which is increasingly important at lower and lower reflectance values. We have also developed two methods of modifying CIBR. We use a simple curve fitting procedure to account for and remove any systematic errors due to low reflectance while still preserving the random spread of the CIBR values as a function of surface reflectance. We also have developed a two-dimensional look-up table for CIBR; CIBR using this technique is a function of both water vapor (as with all CIBR techniques) and surface reflectance. Here we use data recently acquired with the Multi-spectral Thermal Imager spacecraft (MTI) to compare these four methods of determining columnar water vapor content