NDVI-based split-window algorithm for precipitable water vapour retrieval from Landsat-8 TIRS data over land area

Abstract

An algorithm for the retrieval of precipitable water vapour (PWV) from Landsat-8 thermal infrared sensor (TIRS) data over land area has been developed in this paper. This method is based on the split-window covariance-variance ratio (SWCVR) theory and introduces normalized difference vegetation index (NDVI) to improve PWV retrieval from the relatively high-resolution thermal infrared data. Validation of the method is performed with meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) total column PWV product (MOD05), and comparisons between NDVI-based SWCVR method and previous SWCVR method are employed. The root mean square error (RMSE) between PWV retrieved and that provided by meteorological data is 0.39 and 0.57 g cm−2 respectively for the proposed and previous method. The RMSE is 0.55 and 0.69 g cm−2 respectively for the proposed and previous method as validated with MOD05. It is concluded that the proposed method for retrieval of PWV from Landsat-8 TIRS data attained a better accuracy than the previous SWCVR method. PWV obtained from the proposed method is of great value as an input for land surface temperature retrieval and atmospheric correction for the Landsat-8 data.