Submitted to: BARC Poster Day
Publication Type: Abstract Only
Publication Acceptance Date: 3/22/2011
Publication Date: 4/27/2011
Citation: Holmes, T.R., Crow, W.T., Jackson, T.J. 2011. Development of a continuous multi-satellite land surface temperature product [abstract]. Abs. 15. BARC Poster Day.
Technical Abstract: Land surface temperature plays an obvious and important role in land surface processes. In addition, it is a key input in physically-based retrieval algorithms of important water and energy products, such as surface net radiation, evapotranspiration and soil moisture. To address these needs, satellite platforms are commonly equipped with at least one instrument that is sensitive to the physical temperature of the land surface. Consequently, there are currently several independent satellite sensors in orbit that can be used to derive land surface temperature at varying, and often multiple, times during the day. Relatively little work to date has focused on the integration of these individual multi-sensor and multi-satellite surface temperature datasets into a single coherent temperature product. The benefits of a well-validated and continuous surface temperature data set would be numerous: it would significantly decrease the extent of data gaps and increase the quality of the temperature product; it could be utilized in the interpretation of data from satellite missions that do not carry a sensor for retrieving physical temperature; and it could be used to study long term trends in temperature patterns and related processes (climate data record). The objective of this research is to develop a method to fuse temperature observations from multiple satellite sensors to create a spatially and temporally continuous dataset of diurnal land surface temperature. To do this, we will merge microwave and infrared derived temperature data from five different satellite platforms with backbone surface temperature forecasts obtained from a numerical weather prediction model. This merger of data from multiple satellites will provide a long-term record of well-validated surface temperature estimates with a high temporal resolution that can be used to improve models of the Earth’s surface energy budget and thereby contributes to studies of climate change. Two applications that could benefit from improved surface temperature data are satellite soil moisture retrieval and the estimation of evapotranspiration by thermal remote sensing. Both methods require surface temperature information but new dedicated soil moisture satellites no longer have an onboard means to measure surface temperature and thermal remote sensing is limited by clouds. By optimally fusing multi-sensor and platform datasets, we aim to alleviate these problems and enhance the utilization of temperature observations across disciplines.