资源环境科技发展态势分析平台

In this study, we present an approach for improved estimation of atmospheric water vapor content (WVC) from Landsat-8 data. The initial estimates of WVC derived from the split window covariance variance ratio (SWCVR) method are sequentially improved by two exponential models. The first model is designed to apply orographic corrections to the SWCVR estimate using a 1-arcsec Shuttle Radar Topography Mission digital elevation model based on a scale and bias parameter for the change in elevation. This model is based on the local change in elevation derived using the directional Kirsch compass kernel gradient. A second model for additional improvement in orographically corrected WVC estimate is defined based on the change in the normalized differenced vegetation index (NDVI) with three parameters, a scale and bias parameter for the change in NDVI and one bias parameter for the WVC estimate. The scale and bias parameters in orographic correction and the NDVI-based correction are derived using regression between a selected training subset of WVC estimates (SWCVR estimate for orographic correction and orographically corrected WVC estimate for the NDVI-based correction) and the corresponding Sentinel-3 Ocean Land Color Instrument (OLCI) integrated water vapor (IWV) product. The study is conducted over the Himalayan regions around Kullu, Himachal Pradesh, India, for two dates, 20 September 2017 and 26 January 2018, corresponding to the autumn and peak winter season in the region. A reduction in error of 0.32and 0.04gm/cm(2) was observed in proposed WVC estimate and the Sentinel-3 integrated water vapor product, for the two data sets respectively.