Sea and Land Surface Temperature Radiometer (Sentinel 3): Pre-mission development of clear-cloud-aerosol classification

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From 2013 onwards, a series of sensors called Sea and Land Surface Temperature Radiometers (SLSTRs ) will be operational on European satellites. These SLSTRs will have unique capabilities for long-term observation of Earth's surface and atmosphere, especially for climate applications. SLSTRs will capture images of Earth from each overpass from two viewing directions rather than capturing a single image, which greatly adds to the scientific information that can be deduced from the imagery. SLSTR observations will also be more accurate than those of most comparable sensors. Examples of the scientific information that will be obtained from SLSTRs are land surface temperature (LST), occurrence and intensity of fire (burning of forests and grasslands), surface reflectance (albedo and vegetation products), and the amount of smoke and mineral dust in the atmosphere. Using current techniques, the accuracy of these will be compromised by inadequate 'classification'. To explain: for the best results an accurate interpretation has to be made for each area of the image as to whether there is smoke, other aerosols, or clouds present. This is sometimes difficult even for a human expert, and the current software techniques are even less reliable. So, we propose to find a better solution for this classification problem, to maximize the scientific benefit of SLSTR for observation of land surface temperature (LST), fire, surface reflectance (albedo and vegetation products), and atmospheric aerosol. Without this project, the SLSTR estimates of these parameters will be compromised for climate applications. We will develop and prove effective techniques for the classification of imagery over land into areas of clear sky, cloud-cover and elevated aerosol (smoke and mineral dust). We will do this by building on a physically based, probabilistic approach that has proven effective for cloud/clear sky discrimination , and which will be enhanced with advanced aerosol modelling and fitting techniques. The project will develop a multi-way Bayesian classifier of clear-cloud-aerosol conditions, meeting the different needs of LST, fire, surface reflectance and aerosol retrieval. Our objective is scientifically important because of the importance of these parameters in the climate system, particularly to Earth's radiative balance and carbon cycle. Accurate and representative space-based observations on a global scale are essential to adequate understanding and modelling of these processes. It is also just the right time to undertake this work. Assuming success, we will try to ensure that the new techniques are used right from the time the first SLSTR is launched. The work may also offer more immediate benefits, since the new techniques will be prototyped using images from an existing, similar sensor. So, the new techniques could also be used to improve estimates of these parameters over the last two decades.