Generating Consistent Land Surface Temperature and Emissivity (LST&E) Products Between ASTER and MODIS Data for Earth Science Research

Land surface temperature and emissivity (LST&E) products are generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on NASA's Terra satellite. These products are generated at different spatial, spectral and temporal resolutions resulting in discrepancies between them that are difficult to quantify, compounded by the fact that different retrieval algorithms are used to produce them. For example, the MODIS LST product has good accuracy over graybody surfaces such as dense vegetation and water, but is less accurate over deserts or semi-arid regions, while the ASTER product provides accurate LST's over arid regions but less-accurate LST's over graybody surfaces. This lack of consistency between products makes intercomparison and validation difficult, and limits their usefulness in models and as climate data records which require accurate LST&E's over the entire land surface at a range of spatial, spectral and temporal scales. This study aims to address this problem by using the ASTER Temperature Emissivity Separation (TES) algorithm combined with an improved atmospheric correction method to retrieve LST&E products from ASTER and MODIS in a consistent manner. The synergy of multi-platform, multi-sensor products is essential for laying down a baseline quality metric to which future climate datasets and trends can be measured, and this method will open up the opportunity for generating a unified LSE product that can be used by the scientific community to provide accurate LST and other products in a consistent manner at multiple temporal, spatial and spectral scales.