Rego Steven A, Detenbeck Naomi E, Shen Xiao
Office of Research and Development, U.S. Environmental Protection Agency, Narragansett, RI 02882, USA.
College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT 2601, Australia.
Water (Basel). 2024 Sep 25;16(19):2721. doi: 10.3390/w16192721.
Researchers and environmental managers need big datasets spanning long time periods to accurately assess current and historical water quality conditions in fresh and estuarine waters. Using remote sensing data, we can survey many water bodies simultaneously and evaluate water quality conditions with greater frequency. The combination of existing and historical water quality data with remote sensing imagery into a unified database allows researchers to improve remote sensing algorithms and improves understanding of mechanisms causing blooms. We report on the development of a water quality database "EstuarySAT" which combines data from the Sentinel-2 multi-spectral instrument (MSI) remote sensing platform and water quality data throughout the coastal USA. EstuarySAT builds upon an existing database and set of methods developed by the creators of AquaSat, whose region of interest is primarily larger freshwater lakes in the USA. Following the same basic methods, EstuarySAT utilizes open-source tools: R v. 3.24+ (statistical software), Python (dynamic programming environment), and Google Earth Engine (GEE) to develop a combined water quality data and remote sensing imagery database (EstuarySAT) for smaller coastal estuarine and freshwater tidal riverine systems. EstuarySAT fills a data gap that exists between freshwater and estuarine water bodies. We are able to evaluate smaller systems due to the higher spatial resolution of Sentinel-2 (10 m pixel image resolution) vs. the Landsat platform used by AquaSat (30 m pixel resolution). Sentinel-2 also has a more frequent revisit (overpass) schedule of every 5 to 10 days vs. Landsat 7 which is every 17 days. EstuarySAT incorporates publicly available water quality data from 23 individual water quality data sources spanning 1984-2021 and spatially matches them with Sentinel-2 imagery from 2015-2021. EstuarySAT currently contains 299,851 matched observations distributed across the coastal USA. EstuarySAT's primary focus is on collecting chlorophyll data; however, it also contains other ancillary water quality data, including temperature, salinity, pH, dissolved oxygen, dissolved organic carbon, and turbidity (where available). As compared to other ocean color databases used for developing predictive chlorophyll algorithms, this coastal database contains spectral profiles more typical of CDOM-dominated systems. This database can assist researchers and managers in evaluating algal bloom causes and predicting the occurrence of future blooms.
研究人员和环境管理者需要跨越很长时间段的大型数据集,以便准确评估淡水和河口水中当前及历史时期的水质状况。利用遥感数据,我们可以同时对多个水体进行调查,并更频繁地评估水质状况。将现有和历史水质数据与遥感影像整合到一个统一的数据库中,能让研究人员改进遥感算法,并增进对导致水华的机制的理解。我们报告了一个水质数据库“河口卫星数据库(EstuarySAT)”的开发情况,该数据库结合了哨兵 - 2 多光谱仪器(MSI)遥感平台的数据以及美国沿海地区的水质数据。河口卫星数据库建立在一个现有数据库和由水卫星数据库(AquaSat)创建者开发的一组方法之上,水卫星数据库的关注区域主要是美国较大的淡水湖。遵循相同的基本方法,河口卫星数据库利用开源工具:R v. 3.24 +(统计软件)、Python(动态编程环境)和谷歌地球引擎(GEE),为较小的沿海河口和淡水潮汐河流系统开发了一个水质数据与遥感影像相结合的数据库(河口卫星数据库)。河口卫星数据库填补了淡水和河口水体之间存在的数据空白。由于哨兵 - 2(10 米像素图像分辨率)比水卫星数据库所使用的陆地卫星平台(30 米像素分辨率)具有更高的空间分辨率,我们能够评估较小的系统。哨兵 - 2 还有更频繁的重访(过境)计划,每 5 至 10 天一次,而陆地卫星 7 是每 17 天一次。河口卫星数据库纳入了来自 23 个独立水质数据源的公开可用水质数据,时间跨度为 1984 - 2021 年,并将它们与 2015 - 2021 年的哨兵 - 2 影像在空间上进行匹配。河口卫星数据库目前包含分布在美国沿海地区的 299,851 条匹配观测数据。河口卫星数据库的主要重点是收集叶绿素数据;然而,它还包含其他辅助水质数据,包括温度、盐度、pH 值、溶解氧、溶解有机碳和浊度(如有)。与用于开发预测叶绿素算法的其他海洋颜色数据库相比,这个沿海数据库包含更典型的以有色溶解有机物为主导系统的光谱剖面。该数据库可以帮助研究人员和管理者评估藻华成因并预测未来藻华的发生。