The Capability of GLOBE Data to Correct for Atmospheric Differences in Land Surface Temperature

Abstract

Surface temperature is a quantitative and continuous phenomenon, understood easily when using data collected remotely by satellites. Surface temperature has been studied to further investigate the warming of urban climates, also known as urban heat islands, which can impact urban planning, public health, pollution levels, and energy consumption. However, the full potential of remotely sensed images is limited, when analyzing land surface temperature, due to the daunting task of correcting for changes in the atmosphere. To help address this, Landsat 8 employed a new thermal infrared sensor which records land surface temperature in two wavelengths. With two bands in the infrared region, a split window algorithm (SWA) was applied to correct for atmospheric difference. Split window algorithms are commonly practiced when correcting for atmospheric differences observed by other satellites with two bands in the thermal infrared region. The purpose of this research was to use in situ surface temperature measurements from NASA’s ground observation program, the Global Learning and Observations to Benefit the Environment (GLOBE), to derive the correcting coefficients for use in the split window algorithm. The GLOBE database provided land surface temperature data that coincided with Landsat 8 overpasses to create a global model for correcting Landsat scenes across the world. While it is possible to derive the coefficients from the GLOBE data, the results indicate that more GLOBE data needs to be collected to create a model with complete statistically significant coefficients.