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

海洋覆盖了地球表面约71%的面积，在全球气候系统中发挥着重要作用。海洋表面温度和盐度的研究对于理解海洋如何与陆地和大气交流很重要，而且对于理解海洋生态系统和天气预报也很重要。海面盐度（SSS）和温度（SST）也与河口过程（淡水和海水混合）、分层、缺氧、有机物或藻华等的研究有关，其中包括[3]。SSS和SST数据的收集通常是通过静态浮标、漂流器和船载系统[4]来完成的。由于不同近岸过程和人类活动的发展，近岸海温和海温的估算可能需要更高的细节，因此估算近岸海温和海温是困难的。在过去的几十年里，不同的卫星任务都把重点放在测量海洋学特征上，以克服就地测量技术所存在的问题。卫星提供海洋和陆地现象的全球覆盖，这对提取时间序列和一般趋势特别重要，而且对局部事件的研究也很重要。该博士项目将开发并比较从Landsat 8、[5]和Sentinel-2数据中提取SSS和SST的技术[6,7]。该学生将专注于机器学习技术，将卫星观测与浮标、船只和其他设备提供的现场数据相匹配。该方法将扩展[7]开发的工作，并在全球范围内进行验证。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、8和ASTER卫星配备了100米分辨率的中等光谱分辨率热波段。使用大气和辐射模式来预测大气校正（例如[8]），可以获得低于1k的海表温度误差。该方法将与使用大数据和机器学习获得的结果进行比较。将发展精细化技术，以在沿海地区获得更高的分辨率值。该博士项目的结构如下：第一年：数据分析培训。熟悉卫星数据采集和处理技术。机器学习训练。第2年：开发数据分析算法。现场数据采集/收集和处理。第三年：数据分析。通过同行评审的出版物和学术会议上的报告传播结果。

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.