Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.

/pdf/a-review-of-remote-sensing-for-water-quality-retrieval-34k7h8nj.pdf

A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

https://www.mdpi.com/2072-4292/15/7/1938/pdf?version=1680611014

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

Application and recent progress of inland water monitoring using remote sensing techniques

Clean Water and Sanitation, the sixth goal of Sustainable Development Goals (SDGs 6) is a call for action by the United Nations aiming at balancing the water cycle for sustainable life on the earth. For water security and regional sustainable development, the quantity and quality of inland waters are key variables. Over the past decades, satellite remote sensing offers global information about inland water dynamics in a real-time and low-cost way. Amongst, the Sentinel satellites designed by the European Space Agency can provide global monitoring with a spatial resolution of up to 10 m and several days of revisit time. Although Sentinel satellites have been explored in inland water monitoring for a long time period, a systematical review on the research progress and challenges of their applications has not been documented well. This review aims to present a comprehensive review of the Sentinel satellites (especially for Sentinel-1, Sentinel-2, and Sentinel-3) in monitoring inland water, both on the quantity and quality dimensions, including the water extent, level, depth, volume and water quality (e.g., chlorophyll-a, phycocyanin, suspended particulate matter, colored dissolved organic matter, and Secchi disk depth). A total of 690 publications are involved and the bibliometric quantitative approach is used to analyze the areas in which Sentinel instrument excelled and their performance with different processing methods. The implications for virtual constellation construction using Sentinel satellites from different missions and the contribution of a virtual constellation in support of the SDG 6 are also discussed. According to the initial investigation and characteristics of various satellites, we have proposed several schemes for Sentinel virtual constellation toward different missions covering water quantity measurement and water quality monitoring, which can maximize the observation capability of the satellite. The optimal Sentinel virtual constellation constructing scheme theoretically enables a coverage of 10 m spatial resolution and less than 2 days temporal resolution for all-weather inland water monitoring. These solutions will significantly enhance the observational capacity to obtain high-quality, long-term water security parameters in supporting SDG 6. Nevertheless, there remains a scarcity of freely available Sentinel-derived products, widely applicable data processing algorithms, and unified platform, capable of supporting water security monitoring on a broad scale. 

Monitoring inland water via Sentinel satellite constellation: A review and perspective

The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

/pdf/water-quality-chl-a-inversion-based-on-spatio-temporal-eoqfg4f6.pdf

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

: The combination of remote sensing technology and traditional ﬁeld sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R 2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were veriﬁed with measured data (R 2 = 0.803), and then the results were improved (R 2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R 2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and ﬁne resolution in space, which provide a stronger scientiﬁc basis for rapid diagnosis of eutrophication in inland lakes.

remote Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

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A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

remote Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network