Remote Sensing in Water Quality Parameters Monitoring: History
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Contributor:
Godson Ebenezer Adjovu
,
Haroon Stephen
,
David James
,
Sajjad Ahmad

Remote sensing (RS) applications offer the opportunity for decisionmakers to quantify and monitor water quality parameters (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.

  • remote sensing
  • resolution
  • spectral signatures
  • spaceborne sensors
  • water quality parameters

1. Introduction

Water quality parameters (WQPs) have traditionally been analyzed and monitored through in situ sampling and laboratory testing; however, these approaches are expensive, labor-intensive, time-consuming, and not suitable for large-scale analysis. Sample collections are therefore limited over spatial and temporal scales [1][2][3][4][5]. The use of these conventional methods in water quality monitoring has been taxing due to limited resources in terms of capital and labor available particularly in developing countries. Conventional methods have limited capability to capture the horizontal heterogeneity in the water ecosystem on a large scale and extent. Monitoring, management, and forecasting of the entire water bodies may be inaccessible due to the topographic terrain of the water body. Furthermore, the accuracy and precision of sampled in situ data can be questionable owing to both errors that may arise from field sampling and laboratory analysis [6][7][8][9]. These limitations have put constraints on efforts to monitor the ecological suitability of water bodies over the past decades. Remote sensing (RS) application alternatives have been used in water quality monitoring at local, regional, and global scales in the last six decades and can overcome the limitations associated with conventional methods [10][11][12].
RS applications for water quality monitoring offer synoptic spatial and temporal coverage of the water body that is unattainable by conventional methods, making it ideal and suited for the cost-effective assessment of water quality [8][13]. Most of the satellite images are made available at no cost to users [3]. RS applications, unlike in situ field measurement, allow for analyses of WQPs on a large scale, and hence, are not limited to a single location in space and time. These applications aid to fill the global gap in the spatiotemporal data. They also provide present-time and comparable accurate results at a very fast pace [2][14][15][16][17][18]. Water resources researchers, decision, and policymakers can rely on remotely sensed data to reinforce their abilities in the effective monitoring of the fast-depleting water resources [19]. RS coupled with strategic in situ measurement and monitoring is a useful tool in aiding to predict, mitigate, and possibly prevent future water quality issues [20].
RS sensors capture spectral signatures of water constituents, which makes them an effective tool for the estimation of different WQPs including TSS and chlorophyll-a concentration [1]. Several studies have been carried out that have utilized RS applications in the qualitative and quantitative determinations of WQPs. Qualitative determinations include the identification of the presence, absence, or approximation of WQPs. When RS is combined with calibration curves from ground truth observations, quantitative estimates can be obtained, which are of much greater value to water scientists, engineers, planners, regulators, and policymakers.

2. Strengths of RS Applications

RS techniques present the opportunity to manage and protect water resources. RS applications offer researchers and policymakers the opportunity to carry out synoptic, consistent, repetitive, detailed, and cost-effective assessments of water quality on a spatiotemporal scale compared with point-based in situ measurements [16][18]. Measurement of WQPs with RS satellite sensors can be performed at a great distance, i.e., several hundred to thousands of miles, which helps in the coverage of large areas on the ground and can observe water surfaces repeatedly [21].
The use of satellite images saves users a huge sum of money that would have been spent on field or laboratory work. The use of satellite images and improved statistical and mechanistic modeling of WQPs has been performed at local, watershed, and regional scales with a high degree of accuracy. These models also can effectively enhance the real-time detection of hydrological variability, which is key in providing early warning and rapid response to harmful algal bloom events [18].
Most satellite images are freely available, open-source data that can be accessed at no cost to the users. Different sensors offer different advantages compared to others in terms of their spatial and temporal resolutions. Comparing Landsat sensor data to sensors of higher resolution such as those aboard the SPOT, ASTER, or IKONOS satellite program still places the Landsat programs at an advantage owing to their more extensive temporal record dating back as far as the 1970s. Landsat satellites have been extensively used for water quality assessment due to their relatively low cost, and spatial and temporal resolutions. The utilization of Landsat image data has saved users a sum of about USD 3.45 billion as of 2017, with users in the United States accounting for about 59.7% [22]. The Landsat program serves as a central reference comparison focus for many different moderate and high-resolution optical satellite sensors operated by commercial and government institutions due to their associated benefits including the length of a record, availability of data, calibration standards, and global coverage. Landsat programs have influenced the development of other earth RS satellites by government agencies such as the ESA’s Copernicus Program Sentinel-2 satellite constellation and private agencies’ systems [22].

3. Limitations of RS Applications

Although RS satellite images offer a great tool for the monitoring of WQPs, they possess some underlying limitations that when properly addressed will improve their performance and provide many comparable results with conventional methods. The limitations of RS techniques are outlined in the following paragraphs.
Image acquisition errors may occur. The atmospheric path existing between the sensor and the water surface impacts remotely sensed reflectance, which, in turn, affects the data acquired. The interactions of the sensor’s incident energy with atmospheric conditions are significant enough to impact the incoming radiance, which has the potential to compromise the image. Cloud cover, fog, and haze will blur or obscure target water bodies. Hence, they impact images collected in the visible and near-infrared regions of the electromagnetic spectrum [5][6]. As a result, atmospheric corrections need to be carried out before analysis is performed. Atmospheric corrections are important for water surfaces where the reflected light fraction is very low (i.e., not more than 10% of the radiance measured by the sensor) [18].
Atmospheric interference: This has the potential to restrict the optical signals sensed from the surface of the water bodies. The maximum penetration of light in seawater is at a depth of around 55 m near the 475 nm wavelength. At this depth, a greater percentage of incident energy on the water surface is transmitted. With increased concentrations of sediments to about 400 ppm, light penetration reduces to about 60 cm, making only a progressively thinner layer of surface water detectable [16]. The interactions of incident energy and the atmospheric conditions are significant enough to quantitatively affect the incoming radiance and hence can comprise the results [18].
Image processing errors such as processes involved in the atmospheric corrections and pan-sharpening of satellite images may induce errors when using these images for the estimation of water quality. Satellite images with coarse spatial resolutions may possess issues related to the contamination of pixels with the land surface presenting an inaccurate spectral signature. Additionally, remote sensing image processing errors may result in an invalid detection of WQPs, bringing the accuracy of extracted WQPs into question [23]. For example, the densest areas of cyanobacteria blooms in the Baltic Sea are said to be usually masked out in chlorophyll product maps as processing errors or an atmospheric correction. This is because high reflectance in the near-infrared range of the electromagnetic spectrum is not anticipated by ocean color algorithms [24].
Studies have considered ways of improving the accuracy of their results obtained from high-resolution images through techniques such as pan-sharpening. Other studies resampled their pixel resolution such as the case of [25] where the 20 m resolution Sentinel 2 image was resampled to 10 m resolution. Pan-sharpening or downscaling, unlike conventional resampling, is a fusion technique that allows for the artificial enhancement of the spatial resolution of the image by fusing it with a higher-resolution image. Pan-sharpening introduces less spectral and spatial distortion and provides visually more coherent data compared with resampling of the pixel [26][27]. Conventional resampling of images selects the closest image pixel and preserves the input data, which has the potential to cause blockings in the pixel and location shifts [26]. The authors in the study [26] compared the results of a conventional resampling of a 30 m resolution Landsat 8 OLI to 20 m and Panchromatic-Assisted Downscaling (LPAD) or pan-sharpening of 30 m reflective wavelength bands to Sentinel-2 20 m resolution and found that the LPAD resulted in clearer object boundaries and finer spatial detail than the conventionally resampled data. Although these techniques have been applied to images, it is noted that the pan-sharpened approach alters the radiometric and spectral features of the satellite image and is only useful for image visualization and interpretation, and not for analytical purposes [27]. Researchers in [28] also noted that the sharpening procedure introduces artifacts, particularly around bright non-water pixels, and is therefore not recommended to resample high-resolution bands for noise reduction. The authors pointed out the spatial resolution of the image is its strength, and the native resolution band will produce adequate performance.
Another drawback in the use of RS techniques in the monitoring of water quality is the lack of in situ data in some areas, particularly in sub-Sahara Africa, for the calibration and validation of remotely sensed models due to associated on-site cost and high level technical expertise involved in conducting in situ water quality monitoring efforts [18]. Models developed using RS data need to be properly calibrated and validated using in situ data. The absence of in situ measurements for calibration and validation raises accuracy concerns [16][23].
Weak or inactive optical characteristics of some WQPs: Most of the WQPs detected directly by RS applications are optically active such as TSS, CDOM, and chlorophyll-a concentration. These parameters absorb light in the EM spectrum and influence water optical properties enabling these parameters to be sensed from satellite observations, unlike the optically inactive or weak optical parameters such as TDS, pH, TN, and NH3-N, which have a low signal-to-noise ratio. The estimation of optically inactive parameters such as TDS is due to their statistical associations with other colored WQPs with which they may co-vary. Additionally, retrieval of most WQPs is performed through methods that require accurate parameterization, which may vary with the optical property of the water body [16][29].
Limitations of the sensor’s spatial, spectral, and temporal properties: Other limitations that arise with the use of RS have to do with spatial, temporal, and spectral resolutions of RS sensors. RS sensors have different spatial, spectral, and temporal resolutions, and depending on the need, it may be difficult to rely on a particular sensor to detect temporal trends, especially when they have a longer repeat cycle. An example is comparing the temporal resolutions of Sentinel-2 A/B MSI, which has a 5-day revisit time, to Landsat 8 OLI, which has a 16-day revisit time.
Airborne sensors compared with spaceborne sensors have finer spatial and temporal resolutions, but the data may, however, come at a higher cost to the user and may defeat the purpose of cost savings with the use of RS. Data from most sensors such as MODIS (250 m–1.1 km), MERIS (300 m), and SeaWiFS (1.1 km) are suitable for the effective retrieval of ocean WQPs but may not work efficiently for inland water bodies such as narrow river channels and lakes due to their coarse spatial resolutions and their low signal-to-noise ratios. Data from some sensors with high spatial resolution may not provide an effective time series of effective assessments of WQPs due to their low temporal resolutions. Those sensors with high temporal resolutions may also not support comprehensive and effective investigations owing to their coarse spatial resolutions [5].

This entry is adapted from the peer-reviewed paper 10.3390/rs15071938

References

  1. Chen, X.; Liu, L.; Zhang, X.; Li, J.; Wang, S.; Liu, D.; Duan, H.; Song, K. An Assessment of Water Color for Inland Water in China Using a Landsat 8-Derived Forel-Ule Index and the Google Earth Engine Platform. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 5773–5785.
  2. Hajigholizadeh, M.; Moncada, A.; Kent, S.; Melesse, A.M. Land–lake linkage and remote sensing application in water quality monitoring in lake okeechobee, florida, usa. Land 2021, 10, 147.
  3. Schaeffer, B.A.; Schaeffer, K.G.; Keith, D.; Lunetta, R.S.; Conmy, R.; Gould, R.W. Barriers to adopting satellite remote sensing for water quality management. Int. J. Remote Sens. 2013, 34, 7534–7544.
  4. Uudeberg, K.; Aavaste, A.; Kõks, K.-L.; Ansper, A.; Uusõue, M.; Kangro, K.; Ansko, I.; Ligi, M.; Toming, K.; Reinart, A. Optical Water Type Guided Approach to Estimate Optical Water Quality Parameters. Remote Sens. 2020, 12, 931.
  5. Yang, H.; Kong, J.; Hu, H.; Du, Y.; Gao, M.; Chen, F. A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges. Remote Sens. 2022, 14, 1770.
  6. McCarthy, M.J.; Colna, K.E.; El-Mezayen, M.M.; Laureano-Rosario, A.E.; Méndez-Lázaro, P.; Otis, D.B.; Toro-Farmer, G.; Vega-Rodriguez, M.; Muller-Karger, F.E. Satellite Remote Sensing for Coastal Management: A Review of Successful Applications. Environ. Manag. 2017, 60, 323–339.
  7. Allan, M.G.; Hamilton, D.P.; Hicks, B.J.; Brabyn, L. Landsat remote sensing of chlorophyll a concentrations in central North Island lakes of New Zealand. Int. J. Remote Sens. 2011, 32, 2037–2055.
  8. Gholizadeh, M.H.; Melesse, A.M.; Reddi, L. Spaceborne and airborne sensors in water quality assessment. Int. J. Remote Sens. 2016, 37, 3143–3180.
  9. Adjovu, G.E.; Stephen, H.; Ahmad, S. Monitoring of Total Dissolved Solids Using Remote Sensing Band Reflectance and Salinity Indices: A Case Study of the Imperial County Section, AZ-CA, of the Colorado River. World Environ. Water Resour. Congr. 2022, 2022, 1132.
  10. Pizani, F.M.C.; Maillard, P.; Ferreira, A.F.F.; De Amorim, C.C. Estimation of Water Quality in a Reservoir from Sentinel-2 MSI amd Landsat-8 OLI Sensors. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, V-3-2020, 401–408.
  11. Zhou, Y.; Dong, J.; Xiao, X.; Xiao, T.; Yang, Z.; Zhao, G.; Zou, Z.; Qin, Y. Open surface water mapping algorithms: A comparison of water-related spectral indices and sensors. Water 2017, 9, 256.
  12. Zhou, B.; Shang, M.; Wang, G.; Zhang, S.; Feng, L.; Liu, X.; Wu, L.; Shan, K. Distinguishing two phenotypes of blooms using the normalised difference peak-valley index (NDPI) and Cyano-Chlorophyta index (CCI). Sci. Total Environ. 2018, 628–629, 848–857.
  13. Gallagher, L.C. Hyperspectral Remote Sensing of Suspended Minerals, Chlorophyll and Coloured Dissolved Organic Matter in Coastal and Inland Waters, British Columbia, Canada. Mater’s Thesis, University of Victoria, Victoria, BC, Canada, 2004.
  14. Abbas, A.; Khan, S. Using remote sensing techniques for appraisal of irrigated soil salinity. In Land, Water Environment Management Integrated Systems for Sustainability, Proceedings of the MODSIM 2007 International Congress on Modelling and Simulation, Canberra, Australia, 10–13 December 2007; Modelling and Simulation Society of Australia and New Zealand: Christchurch, New Zealand, 2007; pp. 2632–2638.
  15. Deutsch, E.S.; Alameddine, I.; El-Fadel, M. Monitoring water quality in a hypereutrophic reservoir using Landsat ETM+ and OLI sensors: How transferable are the water quality algorithms? Environ. Monit. Assess. 2018, 190, 141.
  16. Gholizadeh, M.H.; Melesse, A.M.; Reddi, L. A comprehensive review on water quality parameters estimation using remote sensing techniques. Sensors 2016, 16, 1298.
  17. Usali, N.; Ismail, M.H. Use of Remote Sensing and GIS in Monitoring Water Quality. J. Sustain. Dev. 2010, 3, 228–238.
  18. Dube, T.; Mutanga, O.; Seutloali, K.; Adelabu, S.; Shoko, C. Water quality monitoring in sub-Saharan African lakes: A review of remote sensing applications. Afr. J. Aquat. Sci. 2015, 40, 1–7.
  19. Avdan, Z.Y.; Kaplan, G.; Goncu, S.; Avdan, U. Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS Int. J. Geo-Inf. 2019, 8, 553.
  20. Olmanson, L.G.; Brezonik, P.L.; Bauer, M.E. Evaluation of medium to low resolution satellite imagery for regional lake water quality assessments. Water Resour. Res. 2011, 47, W09515.
  21. Nath, R.K.; Deb, S.K. Water-Body Area Extraction From High Resolution Satellite Images-An Introduction, Review, and Comparison. Int. J. Image Process. 2010, 3, 353–372.
  22. Normand, A.E. Landsat 9 and the Future of the Sustainable Land Imaging Program. 2020; p. 31. Available online: https://crsreports.congress.gov/product/pdf/R/R46560 (accessed on 10 February 2023).
  23. Dey, J.; Vijay, R. A critical and intensive review on assessment of water quality parameters through geospatial techniques. Environ. Sci. Pollut. Res. 2021, 28, 41612–41626.
  24. Kutser, T. Passive optical remote sensing of cyanobacteria and other intense phytoplankton blooms in coastal and inland waters. Int. J. Remote Sens. 2009, 30, 4401–4425.
  25. Ávila, D.M.; Torres-Bejarano, F.; Lara, Z.M. Spectral indices for estimating total dissolved solids in freshwater wetlands using semi-empirical models. A case study of Guartinaja and Momil wetlands. Int. J. Remote Sens. 2022, 43, 2156–2184.
  26. Li, Z.; Zhang, H.K.; Roy, D.P.; Yan, L.; Huang, H.; Li, J. Landsat 15-m Panchromatic-Assisted Downscaling (LPAD) of the 30-m reflective wavelength bands to Sentinel-2 20-m resolution. Remote Sens. 2017, 9, 755.
  27. USDA. Resampling and Pansharping Using Raster Functions in ArcPro; Forest Service, USDA: Washington, DC, USA, 2021; pp. 1–8.
  28. Vanhellemont, Q.; Ruddick, K. Acolite for Sentinel-2: Aquatic applications of MSI imagery. Eur. Symp. Agency 2016, SP-740, 9–13.
  29. Chawla, I.; Karthikeyan, L.; Mishra, A.K. A review of remote sensing applications for water security: Quantity, quality, and extremes. J. Hydrol. 2020, 585, 124826.
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