Estimating LST Using a Vegetation-Cover-Based Thermal Sharpening Technique

Abstract

Vegetation-cover-based thermal sharpening techniques have mostly been developed and tested in agricultural areas. Overlooking the impact of soil moisture on surface temperatures is a common problem in these algorithms. This letter developed a vegetation thermal sharpening method for the City of Indianapolis, Indiana, USA, and estimated land surface temperature by disaggregated Landsat Thematic Mapper thermal infrared data from 120 to 30 m. The root-mean-square error was yielded at 1.90°C and 1.91°C using NDVI and fractional vegetation cover as predictors, respectively. The error of the estimation was overlaid with a soil moisture map, which was derived based on the surface energy balance modeling. The pixels with large errors were largely distributed in the areas with low soil moisture. These areas were covered by impervious surfaces such as major roads, commercial land, and the airport. This result suggested that in the urban areas, besides vegetation cover and soil moisture, impervious surfaces must be incorporated in developing any future thermal sharpening techniques. The incorporation of population density and per capita consumption of energy may provide further improvements in the estimation.