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

从2013年起，一系列被称为海洋和陆地表面温度辐射计（SLSTRs）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的独特能力，特别是用于气候应用。SLSTRs将从两个观测方向捕获每个立交桥上的地球图像，而不是捕获单个图像，这大大增加了可以从图像中推断出的科学信息。SLSTR的观测也将比大多数可比传感器的观测更准确。将从slstr获得的科学资料包括陆地表面温度、火灾的发生和强度（森林和草地的燃烧）、地表反射率（反照率和植被产品）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”的影响。解释一下：为了获得最好的结果，必须对图像的每个区域做出准确的解释，以确定是否有烟雾、其他气溶胶或云存在。即使是人类专家有时也很难做到这一点，而当前的软件技术甚至更不可靠。因此，我们建议寻找一个更好的方法来解决这一分类问题，使SLSTR在地表温度（LST）、火灾、地表反射率（反照率和植被产品）和大气气溶胶观测方面的科学效益最大化。如果没有这个项目，SLSTR对这些参数的估计将受到气候应用的影响。我们将开发和证明有效的技术，将陆地上的图像分为晴空、云层覆盖和高空气溶胶（烟雾和矿物粉尘）区域。为此，我们将建立一种基于物理的概率方法，这种方法已被证明对云/晴空的区分是有效的，并将通过先进的气溶胶模拟和拟合技术加以加强。本项目将开发晴空云气溶胶条件的多路贝叶斯分类器，满足地表温度、火灾、地表反射率和气溶胶检索的不同需求。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的重要性，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和模拟这些过程至关重要。这也是进行这项工作的恰当时机。假设成功，我们将尝试确保从第一个SLSTR发射时就使用新技术。这项工作还可能带来更直接的好处，因为新技术将使用现有的类似传感器的图像作为原型。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.