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

海陆表面温度辐射计（Sentinel 3）：任务前开发晴云气溶胶分类

• 批准号: NE/H003924/1
• 负责人: Martin Wooster
• 金额: $ 2.61万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FH003924%2F1
• 关键词: Sea; Land; Surface; Temperature; Radiometer

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.

从2013年开始，一系列称为海和陆地表面温度辐射仪（SLSTR）的传感器将用于欧洲卫星。这些SLSTR将具有长期观察地球表面和大气的独特功能，尤其是对于气候应用。 SLSTR将从两个观看方向捕获每个立交桥的地球图像，而不是捕获单个图像，这大大增加了可以从图像中推导的科学信息。 SLSTR的观察结果也将比大多数可比传感器的观测值更准确。从SLSTR中获得的科学信息的示例是陆地表面温度（LST），火的发生和强度（森林和草原的燃烧），表面反射率（反照率和植被产品）以及大气中的烟雾和矿物质粉尘的量。使用当前的技术，这些精度将因“分类不足”而损害。要解释：为了获得最佳结果，必须针对图像的每个区域进行准确的解释，以了解是否存在烟雾，其他气溶胶或云。即使对于人类专家来说，这有时也很困难，而且当前的软件技术甚至不太可靠。因此，我们建议找到解决此分类问题的更好解决方案，以最大程度地利用SLSTR在观察地表温度（LST），火灾，表面反射率（反照率和植被产品）以及大气气溶胶的科学益处。没有这个项目，这些参数的SLSTR估计将被妥协用于气候应用。我们将开发并证明有效的技术，将图像分类为陆地，云覆盖和高架气溶胶（烟雾和矿物灰尘）的区域。我们将通过建立一种基于物理的，概率的方法来做到这一点，该方法已证明对云/透明的天空歧视有效，并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的贝叶斯分类器，这些分类器的透明云层条件，满足LST，火灾，表面反射率和气溶胶检索的不同需求。我们的目标在科学上很重要，因为这些参数在气候系统中的重要性，尤其是对地球的辐射平衡和碳循环的重要性。在全球范围内的准确和代表性的基于空间的观察对于对这些过程的充分理解和建模至关重要。这也是从事这项工作的合适时机。假设成功，我们将尝试确保从启动第一个SLSTR的时间开始使用新技术。这项工作也可能提供更直接的好处，因为新技术将使用现有类似传感器的图像进行原型。因此，在过去的二十年中，新技术还可以用来改善这些参数的估计值。