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

从2013年起，一系列称为海洋和陆地表面温度辐射计的传感器将在欧洲卫星上运行。这些SLSTR将具有长期观测地球表面和大气层的独特能力，特别是在气候应用方面。SLSTR将从两个观察方向捕捉每个立交桥上的地球图像，而不是捕捉单个图像，这大大增加了可以从图像中推断出的科学信息。SLSTR的观测结果也将比大多数可比传感器的观测结果更准确。将从SLSTR获得的科学信息的例子有：地表温度、火灾发生和强度（森林和草原的燃烧）、地表反射率（植被和植被产品）以及大气中烟雾和矿物粉尘的数量。使用目前的技术，这些的准确性将受到不充分的“分类”。解释如下：为了获得最佳结果，必须对图像的每个区域进行准确的解释，以确定是否存在烟雾、其他气溶胶或云。这有时甚至对人类专家来说都是困难的，并且当前的软件技术甚至更不可靠。因此，我们建议找到一个更好的解决方案，这个分类问题，最大限度地提高科学效益的SLSTR观测的地表温度（LST），火，表面反射率（植被和植被产品），和大气气溶胶。如果没有这个项目，这些参数的SLSTR估计将在气候应用中受到影响。我们将开发和证明有效的技术，将陆地上空的图像分为晴空、云层覆盖和高气溶胶（烟雾和矿物尘埃）区域。我们将通过建立一个基于物理的概率方法来做到这一点，该方法已被证明对云/晴空判别有效，并将通过先进的气溶胶建模和拟合技术来增强。该项目将开发一个多路贝叶斯分类器的清晰的云气溶胶条件，满足不同的需求，LST，火灾，地面反射率和气溶胶反演。我们的目标在科学上是重要的，因为这些参数在气候系统中非常重要，特别是对地球的辐射平衡和碳循环。在全球范围内进行准确和有代表性的天基观测，对于充分了解这些过程并建立模型至关重要。现在也正是开展这项工作的时候。如果成功，我们将努力确保新技术从第一个SLSTR启动时就开始使用。这项工作也可能带来更多直接的好处，因为新技术将使用现有的类似传感器的图像进行原型制作。因此，新技术也可以用来改善过去二十年来对这些参数的估计。

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.