Authors: Monica J. Cook and Dr. John R. Schott
The Landsat series is the longest body of continuously acquired, moderate resolution satellite imagery. The spatial and temporal resolution and coverage of Landsat make it an intriguing instrument for a land surface temperature product, which is an important earth system data record for a number of fields including climate, weather, and agriculture. Because current archived Landsat imagery has only a single thermal band, generation of a land surface temperature product requires an emissivity estimation and atmospheric compensation. This work, assuming imagery from a characterized and calibrated sensor and integration with ASTER derived emissivity data, focuses on the atmospheric compensation component by using reanalysis data and radiative transfer code to generate estimates of radiative transfer parameters. Along with an estimation of land surface temperature, the goal is to provide a confidence estimation for every pixel in a scene. Using water temperatures from buoy data, actual temperatures have been compared to predicted temperatures as validation of performance. These comparisons have shown acceptable performance when the atmosphere is well characterized, but larger errors when the atmosphere is not as well understood. The reanalysis data, radiative transfer code, bulk to skin temperature conversion, and lack of knowledge of atmospheric variation all complicate traditional error analysis. Various methods have been attempted, including error propagation based on perturbed atmospheres, regressions between metrics and error values, and thresholds based on atmospheric variables. Because of complications not faced by other large-scale products, a novel approach to error analysis will be developed by combining multiple approaches and data sources.
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