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An assessment of semi-analytical models based on the absorption coefficient in retrieving the chlorophyll-a concentration from a reservoir
摘要： Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tietê River cascading system, S?o Paulo State, Brazil. It was verified that only QAAV5 based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAAV5 outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed.
关键词： water quality monitoring,satellite data.,Trophic status,inland water
更新于2025-09-23 15:23:52
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Proximal VIS-NIR spectrometry to retrieve substance concentrations in surface waters using partial least squares modelling
摘要： Many water quality parameters such as concentrations of suspended matter, nutrients and algae directly or indirectly change the electromagnetic reflectance and transmission properties of surface water bodies. Optical measurement approaches have shown great potential to partially substitute water sampling and laboratory analyses, but are obstructed by limited flexibility or high maintenance demands. In order to overcome these problems and to bridge the gap between in situ and remote sensing measurements, the use of close-range, above-surface reflectance measurements in the VIS-NIR domain to measure water quality parameters in surface water bodies was investigated. Remote sensing reflectance in a 1 m3 water tank with increasing, known concentrations of suspended solids was measured. A partial least squares model was trained to predict concentrations from reflectance curves, which performed well, considering the wide range of concentrations and illumination conditions (R2cal ? 0.96, R2val ? 0.97). The approach was then transferred to the field and further parameters were tested. Using a semi-autonomous spectrometer mounted to a boom stand on a motor boat, we traced substance concentrations in close intervals along a longitudinal gradient from inflow to dam in a drinking water reservoir in Brazil. The method is suitable for parameters directly influencing the reflection properties of the water body (e.g. suspended solids (R2cal ? 0.93), chlorophyll-a (R2cal ? 0.74)), or for parameters closely related to those (e.g. total phosphorus (R2cal ? 0.97)). For chemical oxygen demand, the method is not well suited (R2cal ? 0.14, R2val ? 0.45). Once calibrated to the local conditions, the spectrometer can be used stationary or on moving platforms to map and monitor surface waters. The integration of the procedure into acoustic and imaging techniques is further investigated.
关键词： water quality,suspended solids,hyperspectral,reservoir,partial least squares,proximal sensing
更新于2025-09-23 15:22:29
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WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis
摘要： In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.
关键词： Quantum Cascade Lasers,photonics,water quality monitoring,photodetectors
更新于2025-09-23 15:22:29
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Remote sensing-based water quality assessment for urban rivers: a study in linyi development area
摘要： Nowadays, urban rivers play an important role in city development and make great contributions to urban ecology. Most urban rivers are the drinking water sources and water quality is extremely critical. The current assessment method in national standard of China has multiple limitations; therefore, this paper introduces an advanced assessment, that is, Canadian Water Quality Index (CWQI). This method can help to provide comprehensive and objective water quality assessment for the urban rivers. Moreover, CWQI can prevent waste of the water resource, since current assessment is pessimistic and tent to underestimate water samples to a lower grade. Linyi development area is selected as study region and CWQI method is applied to assess two major urban rivers within the area. The water monitoring data from 2014 to 2017 is acquired in 24 parameters. Since the CWQI calculation is still based on traditional water quality measurement in parameters, there will be a huge cost when increasing research scale and accuracy. In this paper, remote sensing technique is employed to develop models of CWQI scores from satellite data. By utilizing 23 selected monitoring instances and matching satellite data, linear regression analysis shows that red band data has highest correlation with CWQI in both two urban rivers in the study region. In addition, two testing datasets with five instances for each river are used to validate the RS-based CWQI models and the results show that testing datasets can be fitted well. With the models, CWQI distribution diagrams are generated and assist both spatial and temporal analysis. Experimental results show that the proposed approach can indicate actual water quality pattern which is validated by field visit. The proposed approach in this paper has satisfying effectiveness and robustness.
关键词： Remote sensing,Urban rivers,Water quality index,Spatial temporal analysis
更新于2025-09-23 15:22:29
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Towards Smart Selective Sensors exploiting a novel approach to connect Optical Fiber Biosensors in Internet
摘要： The selective detection of pollutants in water in a laboratory scenario has been presented by authors exploiting low-cost optical biosensors based on plastic optical fibers (POFs) and biological or bio-mimetic receptors. For instance, the detection in water of naphthalene, perfluoroalkyl and polyfluoroalkyl substances (PFAs) have been investigated with interesting detection limits when compared to those obtained by using different expensive traditional approaches (e.g. liquid chromatography-mass spectrometry with high performances). In this work, we have developed and tested a novel approach used in a smart measuring system to use POF sensors in situ for the remote measures of pollutants in water for smart cities applications. More specifically, we have used different water-glycerin solutions to test the novel sensor system based on a Raspberry PI connected to the Internet and to a spectrometer, a light source, a POF sensor, and two computers connected to Internet used as client and server.
关键词： optical fiber sensors,water quality,pollutants,software,biosensors,Plastic optical fibers (POFs),Internet of Things (IoT)
更新于2025-09-23 15:21:01
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Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine
摘要： Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.
关键词： suspended sediment,Landsat,water quality,extreme learning machine,machine learning
更新于2025-09-23 15:21:01
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Long-Term Agroecosystem Research in the Central Mississippi River Basin: Hyperspectral Remote Sensing of Reservoir Water Quality
摘要： In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters—chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH3, all measured water quality parameters were strongly related (R2 ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.
关键词： water quality,Mark Twain Lake,partial least squares regression,chlorophyll,hyperspectral remote sensing,nutrients,turbidity
更新于2025-09-23 15:21:01
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Plastic fiber evanescent sensor in measurement of turbidity
摘要： The construction and working principles of a plastic fiber sensor for examining the level of turbidity is studied in this paper. This work focuses on designing an inexpensive turbidity sensor that incorporates a pair of multimode fibers (MMF) that are attached side by side and their beveled tips are mounted vertically. The efficiency of different beveled angles is evaluated by simulation with Tracepro software. The reflected signal is collected by immersing the sensor head into a water mixture and analyzed for various concentration. It was found that there is a linear increment of output intensity when concentration of mixture is increased. The turbidity sensor is tested with real samples that are collected from lake, river and coastal areas to demonstrate its consistency with commercial apparatus in natural compounds. The results showed that the proposed sensor structure is able to produce reliable results in a dynamic range of detection from 0 to 1000 Nephelometric Turbidity Unit (NTU) to facilitate practical field measurements.
关键词： Optical fiber sensor,Tracepro software,Water quality monitoring,Turbidity
更新于2025-09-23 15:21:01
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Estimating the Optical Properties of Inorganic Matter-Dominated Oligo-to-Mesotrophic Inland Waters
摘要： Many studies over the years have focused on bio-optical modeling of inland waters to monitor water quality. However, those studies have been conducted mainly in eutrophic and hyper-eutrophic environments dominated by phytoplankton. With the launch of the Ocean and Land Colour Instrument (OLCI)/Sentinel-3A in 2016, a range of bands became available including the 709 nm band recommended for scaling up these bio-optical models for productive inland waters. It was found that one category of existing bio-optical models, the quasi-analytical algorithms (QAAs), when applied to colored dissolved organic matter (CDOM) and detritus-dominated waters, produce large errors. Even after shifting the reference wavelength to 709 nm, the recently re-parameterized QAA versions could not accurately retrieve the inherent optical properties (IOPs) in waterbodies dominated by inorganic matter. In this study, three existing versions of QAA were implemented and proved inefficient for the study site. Therefore, several changes were incorporated into the QAA, starting with the re-parameterization of the empirical steps related to the total absorption coefficient retrieval. The re-parameterized QAA, QAAOMW showed a significant improvement in the mean absolute percentage error (MAPE). MAPE decreased from 58.05% for existing open ocean QAA (QAALv5) to 16.35% for QAAOMW. Considerable improvement was also observed in the estimation of the absorption coefficient of CDOM and detritus from a MAPE of 91.05% for QAALv5 to 18.87% for QAAOMW. The retrieval of the absorption coefficient of phytoplankton (aφ) using the native form of QAA proved to be inaccurate for the oligo-to-mesotrophic waterbody due to the low aφ returning negative predictions. Therefore, a novel approach based on the normalized aφ was adopted to maintain the spectral shape and retrieve positive values, resulting in an improvement of 119% in QAAOMW. Further tuning and scale-up of QAAOMW to OLCI bands will aid in monitoring water resources and associated watershed processes.
关键词： water quality,bio-optics,quasi-analytical algorithm,Brazilian reservoir,inland waters,inherent optical properties
更新于2025-09-19 17:15:36
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Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay
摘要： Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.
关键词： water quality,total suspended solids,ocean color,satellite remote sensing,statistical analysis,Random Forest,Chesapeake Bay,multispectral,machine learning
更新于2025-09-19 17:15:36