High-resolution multispectral estimation of sea surface salinity and temperature in coastal areas

沿海地区海面盐度和温度的高分辨率多光谱估算

• 批准号: 2438948
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2438948
• 关键词: resolution; multispectral; estimation; sea; surface

Covering around 71% of the Earth's surface, oceans play a major role in the global climate system, [1]. The study of sea surface temperature and salinity is important to understand how oceans communicate with land and atmosphere, but also for the understanding of marine ecosystems and weather prediction, [2]. Sea surface salinity (SSS) and temperature (SST) are also relevant in the study of estuarine processes (mixing of fresh and sea water), stratification, hypoxia, organic matter, or algal blooms, among others, [3]. The SSS and SST data collection has typically been done by means of static buoys, drifters, and ship-based systems, [4]. The estimation of SST and SSS near the coast, where the detail needed might be higher due to the development of different near-shore processes and human activities, is difficult.Different satellite missions have focused over the past decades on the measurement of oceanographic characteristics to overcome the issues that the in situ measuring techniques present. Satellites provide worldwide coverage of ocean and land phenomena, which is particularly relevant for the extraction of time series and general trends, but also for the study of localised events.This PhD project will develop and compare techniques to extract SSS and SST from Landsat 8, [5] and Sentinel-2 data, [6, 7]. The student will focus on machine learning techniques to match satellite observations with in situ data provided by buoys, vessels and other. The methodology will extend the work developed by [7] and validate it worldwide. Moreover, the machine learning method for SSS will be extended to the Landsat 8 dataset, and compared with the Sentinel-2 approach.Landsat 5, 7 and 8 and ASTER satellites are equipped with 100 m resolution thermal bands with moderate spectral resolution. Using atmospheric and radiative models to predict atmospheric correction (e.g. [8]), it is possible to obtain SST errors below 1 K. This methodology will be compared to results obtained by using big data and machine learning. Refinement techniques will be developed to obtain higher resolution values in coastal areas.This PhD project will be structured as follows:Year 1: Training in data analysis. Familiarisation with satellite data acquisition and processing techniques. Machine learning training.Year 2: Development of data analysis algorithms. In situ data acquisition/collection and processing.Year 3: Data analysis. Dissemination of results via peer-reviewed publications and presentations in scholarly conferences.

海洋约占地球表面的71%，在全球气候系统中发挥着重要作用[1]。海洋表面温度和盐度的研究对于了解海洋如何与陆地和大气沟通非常重要，而且对于了解海洋生态系统和天气预测也很重要。海表盐度（SSS）和温度（SST）也与河口过程（淡水和海水的混合），分层，缺氧，有机物或藻类水华等的研究有关，[3]。SSS和SST数据收集通常通过静态浮标、漂流器和船基系统进行[4]。由于近海过程和人类活动的发展，估计近海SST和SSS所需的细节可能更高，这是困难的，过去几十年来，不同的卫星任务都集中在测量海洋学特征上，以克服现场测量技术存在的问题。卫星提供全球范围内的海洋和陆地现象，这是特别相关的时间序列和一般趋势的提取，但也为局部事件的研究。这个博士项目将开发和比较技术，以提取SSS和SST从Landsat 8，[5]和哨兵-2数据，[6，7]。学生将专注于机器学习技术，以匹配卫星观测与浮标，船只和其他提供的现场数据。该方法将扩展[7]开发的工作，并在全球范围内验证它。此外，SSS的机器学习方法将扩展到Landsat 8数据集，并与Sentinel-2方法进行比较。Landsat 5，7和8以及ASTER卫星配备了100 m分辨率的热波段，具有中等光谱分辨率。使用大气和辐射模式预测大气校正（例如[8]），有可能获得低于1 K的SST误差。该方法将与使用大数据和机器学习获得的结果进行比较。将开发精细化技术，以获得沿海地区更高的分辨率值。该博士项目的结构如下：第一年：数据分析培训。熟悉卫星数据获取和处理技术。机器学习培训。第2年：数据分析算法开发。现场数据采集/收集和处理。第3年：数据分析。通过同行评审的出版物和学术会议上的演讲传播成果。