遥感,作为水管理决策的驱动力,需要与水质监测计划进一步整合,尤其是在发展中国家。此外,遥感方法的使用尚未广泛应用于监测例程中。因此,有必要评估可用传感器的功效,以补充此类程序中通常有限的现场测量,并建立支持监测任务的模型。这里,我们将墨西哥国家水质监测系统(RNMCA)的现场测量值(2013-2019年)与Landsat-8OLI的数据相结合,Sentinel-3OLCI和Sentinel-2MSI训练极限学习机(ELM),用于估计叶绿素a(Chl-a)的支持向量回归(SVR)和线性回归(LR),浊度,总悬浮物(TSM)和Secchi磁盘深度(SDD)。此外,将Chl-a和TSM的OLCI2级产品与RNMCA数据进行比较。我们观察到OLCILevel-2产品与RNMCA数据相关性较差,仅依靠它们来支持监控操作是不可行的。然而,OLCI大气校正数据有助于使用ELM开发准确的模型,特别是对于浊度(R2=0.7)。我们得出的结论是,遥感对支持监测系统任务很有用,其逐步整合将提高水质监测项目的质量。
Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013-2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.