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.

Dissolved oxygen (DO) depression in urban rivers appears to be increasing in developing countries, which causes severe aquatic ecosystem stresses. One urban river which suffers DO depression under low flow conditions and requires systematic research for effective mitigation strategies is the Nanfei River (Hefei, China). We investigated its longitudinal profiles of DO and other related water constituents with high spatial resolution monitoring at low flow. A mechanistic DO model for the reach was customized and calibrated with the data obtained. We found that the daily average DO levels within the 11 km study reach shifted from supersaturation (11.5 mg L⁻¹) upstream of the Wangtang Wastewater Treatment Plant (WWTP) to serious depletion (3.6 mg L⁻¹) downstream. Process analysis indicated that DO production via strong algal photosynthesis overwhelmed the DO consumptions upstream from the WWTP. In contrast, DO sources could not compensate for DO consumptions, wherein carbonaceous deoxygenation was the largest consumer of the DO (approximately 70%) downstream the WWTP. Rather than directly contributing labile organics, the WWTP effluent affected the DO balance by shifting the metabolism from upstream autotrophy to downstream heterotrophy. Finally, mitigation strategies for DO depression in rivers in rapidly-urbanizing regions were suggested accordingly.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R² = 0.68) but lower performances for TSM and NO3-N (R² = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.