A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques

Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water’s surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD)

Development of bio-optical algorithms to estimate chlorophyll in the Great Salt Lake and New England lakes using in situ hyperspectral measurements

Chlorophyll is widely used to evaluate lake water quality, effectively integrating the chemical, physical and biological state of a lake. Assessment of chlorophyll conditions in lakes can be greatly enhanced by the use of remote sensing, allowing information to be gathered at spatial and temporal scales not possible with traditional limnological sampling methods. In order for remote sensing methods to provide accurate estimates of chlorophyll concentration, algorithms need to be developed with high-quality spectral data paired with water quality measurements and optimized for regional lake differences. In this study, in situ hyperspectral optical measurements were used to develop algorithms to estimate chlorophyll for the Great Salt Lake and New England lakes. The spectral data were used to mimic bands utilized by the MODIS, MERIS, and SeaWiFS sensors, as well as for a theoretical hyperspectral sensor with 3-nm wide bands, providing the capability to evaluate algorithm performance in all of these sensors. In addition to the traditional bands used in these algorithms, alternate band combinations were examined for both ocean color chlorophyll (OC) and maximum chlorophyll index (MCI) algorithms. A simulated 709 nm band was created for MODIS using the 754 nm band, providing a method for testing MODIS with algorithms relying on the key 705 nm to 715 nm wavelength range. In New England lakes, the most effective algorithm for hyperspectral bands (RMS = 0.206, in log decades) and MERIS (RMS = 0.218) was a version of MCI. For MODIS and SeaWiFS, the most effective algorithm used an OC approach with 489 nm as the blue band, yielding an RMS of 0.242 and 0.231, respectively. In the Great Salt Lake, the most effective algorithms for hyperspectral bands and MERIS were based on a single ratio of 709 nm / 675 nm, providing an RMS of 0.236 and 0.249, respectively. For MODIS and SeaWiFS, the most effective algorithm was the OC method using 489 nm as the blue band, which resulted in an RMS of 0.246 and 0.255, respectively

NIR-red reflectance-based algorithms for chlorophyll-a estimation in mesotrophic inland and coastal waters: Lake Kinneret case study

A variety of models have been developed for estimating chlorophyll-a (Chl-a) concentration in turbid and productive waters. All are based on optical information in a few spectral bands in the red and near-infra-red regions of the electromagnetic spectrum. The wavelength locations in the models used were meticulously tuned to provide the highest sensitivity to the presence of Chl-a and minimal sensitivity to other constituents in water. But the caveat in these models is the need for recurrent parameterization and calibration due to changes in the biophysical characteristics of water based on the location and/or time of the year. In this study we tested the performance of NIR-red models in estimating Chl-a concentrations in an environment with a range of Chl-a concentrations that is typical for coastal and mesotrophic inland waters. The models with the same spectral bands as MERIS, calibrated for small lakes in the Midwest U.S., were used to estimate Chla concentration in the subtropical Lake Kinneret (Israel), where Chl-a concentrations ranged from 4 to 21 mgm-3 during four field campaigns. A two-band model without reparameterization was able to estimate Chl-a concentration with a root mean square error less than 1.5 mgm-3. Our work thus indicates the potential of the model to be reliably applied without further need of parameterization and calibration based on geographical and/or seasonal regimes

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Developments in Earth observation for the assessment and monitoring of inland, transitional, coastal and shelf-sea waters

The Earth's surface waters are a fundamental resource and encompass a broad range of ecosystems that are core to global biogeochemical cycling and food and energy production. Despite this, the Earth's surface waters are impacted by multiple natural and anthropogenic pressures and drivers of environmental change. The complex interaction between physical, chemical and biological processes in surface waters poses significant challenges for in situ monitoring and assessment and often limits our ability to adequately capture the dynamics of aquatic systems and our understanding of their status, functioning and response to pressures. Here we explore the opportunities that Earth observation (EO) has to offer to basin-scale monitoring of water quality over the surface water continuum comprising inland, transition and coastal water bodies, with a particular focus on the Danube and Black Sea region. This review summarises the technological advances in EO and the opportunities that the next generation satellites offer for water quality monitoring. We provide an overview of algorithms for the retrieval of water quality parameters and demonstrate how such models have been used for the assessment and monitoring of inland, transitional, coastal and shelf-sea systems. Further, we argue that very few studies have investigated the connectivity between these systems especially in large river-sea systems such as the Danube-Black Sea. Subsequently, we describe current capability in operational processing of archive and near real-time satellite data. We conclude that while the operational use of satellites for the assessment and monitoring of surface waters is still developing for inland and coastal waters and more work is required on the development and validation of remote sensing algorithms for these optically complex waters, the potential that these data streams offer for developing an improved, potentially paradigm-shifting understanding of physical and biogeochemical processes across large scale river-sea continuum including the Danube-Black Sea is considerable

Relationship between Land Use and Water Quality and its Assessment Using Hyperspectral Remote Sensing in Mid- Atlantic Estuaries

Mid-Atlantic coastal waters are under increasing pressures from anthropogenic disturbances at various temporal and spatial scales exacerbated by the climate change. According to the National Oceanic Atmospheric Association (NOAA), 10 of the 22 estuaries in the Mid-Atlantic, including the Chesapeake Bay, exhibit high levels of eutrophic conditions while seven, including Delaware Bay, exhibit low conditions. Chesapeake Bay is the largest estuarine system in the United States and undergoes frequent eutrophication and low dissolved oxygen events. Although substantially lower in nutrients compared to other Mid-Atlantic Estuaries, the biological, chemical, and ecological status of the Delaware Bay has changed in the past few decades due to high coastal tourism, increased local resident populations, and agricultural activities which have increased nutrient inputs into this shallow coastal bay. As stated by the Academy of Natural Sciences, although the nutrient load has reduced since the Clean Water Act, years of nutrient accumulation, contaminations, and sedimentation have impacted estuarine systems substantially, long-term monitoring is lacking, and ecological responses are not well quantified. Eutrophication within the Bays has degraded water quality conditions advanced by sedimentation. Understanding the quality of the water in any aquatic ecosystem is a critical first step in order to identify characteristics of that ecosystem and draw conclusions about how well adapted the system is in terms of anthropogenic activity and climate change. Determining water quality in intertidal creeks along the Chesapeake and Delaware coastlines is important because land cover is constantly changing. Many of these tidal creeks are lined with forested riparian buffers that may be intercepting nutrients from running off into the waterways. Identifying water conditions, coupled with the marsh land cover, provides a strong foundation to see if the buffer systems are providing the ecosystem services they are designed to provide. Our primary goal in this chapter is to provide research findings on the application of the hyperspectral remote sensing to monitor specific land-use activities and water quality. Along with hyperspectral remote sensing, our monitoring was coupled with the integration of remotely sensed data, global positioning system (GPS), and geographic information system (GIS) technologies that provide a valuable tool for monitoring and assessing waterways in the Mid-Atlantic Estuaries

Real time HABs mapping using NASA Glenn hyperspectral imager

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand

A Relaxed Matrix Inversion Method for Retrieving Water Constituent Concentrations in Case II Waters: The Case of Lake Kasumigaura, Japan

The matrix inversion method (MIM) is an effective algorithm for estimating water constituent concentrations in case II waters. To apply this method, appropriate and accurate specific inherent optical properties (SIOPs) for each constituent in water are essential. However, many routine observations of lake water quality do not in fact provide SIOPs, thus limiting the application of the MIM. In this paper, an alternative MIM method based on linear matrix inversion theory was proposed to relax the requirement of SIOPs measurement. For this, so-called ESIOPs (Estimated SIOPs) were first derived by an unusual application of MIM based on adequate calibration samples; then the water constituent concentrations for the whole study area were retrieved by the standard application of MIM based on the derived ESIOPs. For each calibration sample, measurement of the reflectance spectrum and corresponding water constituent concentrations, which can be obtained from periodical satellite data and routine field surveys, is required. The performance of the proposed method was evaluated using the simulation data from Hydrolight and three MEdium Resolution Imaging Spectrometer Instrument (MERIS) images. The results showed that this method yielded satisfactory estimations of the water constituent concentrations for the noise-contaminated simulation data sets. For MERIS data in our study area (Lake Kasumigaura, Japan), the average bias (mean normalized bias or MNB) and relative random uncertainty (normalized root mean square error, or NRMS) were in the range of -11.2% to 3.4% and 4.8% to 29.7% for each water constituent concentration. These findings imply that the algorithm proposed in this study is theoretically reasonable and practically applicable

Optical remote sensing of lakes: an overview on Lake Maggiore

Optical satellite remote sensing represents an opportunity to integrate traditional methods for assessing water quality of lakes: strengths of remote sensing methods are the good spatial and temporal coverage, the possibility to monitor many lakes simultaneously and the reduced costs. In this work we present an overview of optical remote sensing techniques applied to lake water monitoring. Then, examples of applications focused on lake Maggiore, the second largest lake in Italy are discussed by presenting the temporal trend of chlorophyll-a (chl-a), suspended particulate matter (SPM), coloured dissolved organic matter (CDOM) and the z90 signal depth (the latter indicating the water depth from which 90% of the reflected light comes from) as estimated from the images acquired by the Medium Resolution Imaging Spectrometer (MERIS) in the pelagic area of the lake from 2003 to 2011. Concerning the chl-a trend, the results are in agreement with the concentration values measured during field surveys, confirming the good status of lake Maggiore, although occasional events of water deterioration were observed (e.g., an average increase of chl-a concentration, with a decrease of transparency, as a consequence of an anomalous phytoplankton occurred in summer 2011). A series of MERIS-derived maps (summer period 2011) of the z90 signal are also analysed in order to show the spatial variability of lake waters, which on average were clearer in the central pelagic zones. We expect that the recently launched (e.g., Landsat-8) and the future satellite missions (e.g., Sentinel-3) carrying sensors with improved spectral and spatial resolution are going to lead to a larger use of remote sensing for the assessment and monitoring of water quality parameters, by also allowing further applications (e.g., classification of phytoplankton functional types) to be developed

Modeling Chlorophyll Concentrations on the Ohio River using Remotely Sensed Data

Traditional direct water quality methodologies limit the ability to spatially and temporally predict algal blooms in lotic systems due to the size and characteristics of large river systems. Algal blooms potentially can be predicted by knowing the spatial and temporal patterns of change in cyanobacteria concentrations at large scales. Remote sensing studies investigating freshwater algal blooms, some known to secrete harmful toxins, are primarily conducted on lentic systems while large lotic systems are greatly ignored. In this study I developed a chlorophyll concentration estimation model for the Ohio River using a satellite remote sensing approach. Ground-truth water quality measures, including temperature, dissolved oxygen, turbidity, as well as chlorophyll concentrations, were obtained through hand-samples on days the satellite flew over the study area. Concentrations of chlorophyll were correlated with spectral signatures from Landsat-8 OLI satellite imagery. Then a predictive model was developed using two bands of Landsat 8 to predict chlorophyll a and the generated model has an R2 = 0.879 (Adj. R2 = 0.819) and a p-value = 0.015. Two other models were generated for estimating both chlorophyll a & b and total chlorophyll; however, the models were not as robust, R2 = 0.801 (Adj. R2 = 0.603), p-value = 0.141 and R2 = 0.764 (Adj. R2 = 0.528), p-value = 0.18, respectively