Remote Sensing in Water Quality and Water Resources Management
DOI:
https://doi.org/10.31033/ijrasb.9.1.19Keywords:
Remote Sensing, Earth observation, Water Resources, Water QualityAbstract
The quality of water ascertains the ‘integrity’ of water for specific purposes. Tests and quality of examination of water can provide sufficient information about the waterway health. If tests are conducted over a span of time period, the water quality changes can be realized. There are several testing parameters like pH value, temperature, salinity, turbidity, phosphates and nitrates, which can help assess the water quality. Also, aquatic macro-invertebrates can give a proper water quality indication.
Surface water contaminated can pose a high risk to the entire human population and it remains a challenging task to investigate and resolve the problem for public health authority. Intensification of agricultural activities, change in climatic conditions, coastal area quick urban development, and resultant freshwater source declining have contributed considerably to the surface water contamination risk and the augmentation of waterborne disease incidences. The quality of surface water monitoring needs frequent problem detection to reduce any negative effect on public health. The epidemiology study applies geospatial and remote sensing technologies to distinguish the temporal and spatial environmental variability determinants to assess the epidemiology of certain diseases. By providing an integrated and systematic approach to risky water management for the public health and safety, a proper epidemiology method can be used and proved to be an efficient device to evaluate the quality of surface water and any related health risks.
SWRMS- Spatial water resource monitoring system provides important and beneficial information to support water management. Requisite innovative features involve the explicit water redistribution description and use of river water and groundwater systems, to achieve more spatial details like key irrigated area features and wetlands, to improve hydrometer observation accuracy and assimilating the observations. A review of research and operational applications reveals that satellite view can enhance spatial detail and accuracy in estimating hydrological model. Every operating system uses land cover classification, dynamic forcing, and a parameterization priory of vegetation dynamics, which is partially or completely based on remote sensing, while satellite observations are utilized in varying stages for data assimilation and model evaluation. The satellite observation, utility by data assimilation varies as a dominant hydrological function. This review paper identifies the spatial and temporal precipitation products, including the application of a higher remote sensing product range, along with operational challenges while research satellite mission continuity with data services, finding computationally-efficient data assimilation techniques. The entire observations critically relies on the detailed information availability and understanding the remotely-sensed spatial and temporal scaling.
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Arnold, J. G., Moriasi, D. N., Gassman, P. W., Abbaspour, K. C., White, M. J., Srinivasan, R., Santhi, C., Harmel, R. D., van Griensven, A., Van Liew, M. W., Kannan, N., & Jha, M. K. (2012). SWAT: model use, calibration, and validation. Transactions of the ASABE, 55, 1491–1508.
Auerbach, L.W.; Goodbred, S.L.; Mondal, D.R.; Wilson, C.A.; Ahmed, K.R.; Roy, K.; Steckler, M.S.; Small, C.; Gilligan, J.M.; Ackerly, B.A. (2015). Flood Risk of Natural and Embanked Landscapes on the Ganges, Brahmaputra Tidal Delta Plain. Nat. Claim. Chang. 2015, 5, 153–157, doi:10.
Bai, Z. G., Dent, D. L., Olsson, L., & Schaepman, M. E. (2008). Proxy global assessment of land degradation. Soil Use and Management, 24, 223–234
Borrelli, P., Robinson, D.A., Fleischer, L.R., et al. (2017). An assessment of the global impact of 21st century land use change on soil erosion. Nat Commun 8, 2017. https://doi.org/10.1038/s41467-017-02142-7
Bartholomeus, H., Epema, G., & Schaepman, M. (2007). Determining iron content in Mediterranean soils in partly vegetated areas, using spectral reflectance and imaging 1658, Page 20 of 27 Water Air Soil Pollut (2013) 224:1658 spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 9, 194–203.
Bartholomeus, H. M., Schaepman, M. E., Kooistra, L., Stevens, A., Hoogmoed, W. B., & Spaargaren, O. S. P. (2008). Spectral reflectance based indices for soil organic carbon quantification. Geoderma, 145, 28–36.
Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor, E., & Tychon, B. (2011). Soil organic carbon mapping of partially vegetated agricultural fields with imaging spectroscopy. International Journal of Applied Earth Observation and Geoinformation, 13, 81-88.
Bouma, J., & McBratney, A. (2013). Framing soils as an actor when dealing with wicked environmental problems. Geoderma, 200–201, 130–139.
Dieye, A. M., Roy, D. P., Hanan, N. P., Liu, S., Hansen, M., & Toure, A. (2012). Sensitivity analysis of the GEMS soil organic carbon model to land cover land use classification uncertainties under different climate scenarios in Senegal. Biogeosciences, 9, 631–648
Dungait, J. A. J., Hopkins, D. W., Gregory, A. S., & Whitmore, A. P. (2012). Soil organic matter turnover is governed by accessibility not recalcitrance. Global Change Biology, 18, 1781–1796.
Erban, L.E.; Gorelick, S.M.; Zebker, H.A. (2014). Groundwater Extraction, Land Subsidence, and Sea-Level Rise in the Mekong Delta, Vietnam. Environ. Res. Letter. 2014, 9, doi:10.1088/1748-9326/9/8/084010
Garcia-Pineda, O.; Holmes, J.; Rissing, M.; Jones, R.; Wobus, C.; Svejkovsky, J.; Hess, M. (2017). Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR). Remote Sens. 2017, 9, doi:10.3390/rs9060567.
Gobin, A., Campling, P., Deckers, J., & Feyen, J. (2000). Quantifying soil morphology in tropical environments, methods and application in soil classification. Soil Science Society of America Journal, 64, 1423–1433
Gholizadeh, Mohammad Haji et al. (2016). “A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.” Sensors (Basel, Switzerland) 16 (2016), [PDF] A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques | Semantic Scholar
Higgins, S.A.; Overeem, I.; Steckler, M.S.; Syvitski, J.P.M.; Seeber, L.; Akhter, S.H. (2014). In SAR measurements of Compaction and Subsidence in the Ganges-Brahmaputra Delta, Bangladesh Stephanie. J. Geophys. Res. Earth Surf. 2014, 119, 1768–1781, doi:10.1002/2014JF003117.Received.
ICARDA Communication Team, (2021). How satellite observations help lessen the contribution of agriculture to climate change, how satellite observations help lessen the contribution of agriculture to climate change | ICARDA
Jana, R. B., & Mohanty, B. P. (2011). Enhancing PTFs with remotely sensed data for multi-scale soil water retention estimation. Journal of Hydrology, 399, 201–211.
John R. Jensen (2007). Remote Sensing of the Environment, Second Edition, Pearson Prentice Hall, IMP.09_rs_water (1)
Lamma, O. A. (2021). Groundwater Problems Caused By Irrigation with Sewage Effluent. International Journal for Research in Applied Sciences and Biotechnology, 8(3), 64-70.
Lamma, O. A. (2021). The impact of recycling in preserving the environment. IJAR, 7(11), 297-302.
Lamma, O. A., & Swamy, A. V. V. S. (2018). Assessment of Ground Water Quality at Selected Industrial Areas of Guntur, AP, India. Int. J. Pure App. Biosci, 6(1), 452-460.
Lamma, O., & Swamy, A. V. V. S. (2015). E-waste, and its future challenges in India. Int J Multidiscip Adv Res Trends, 2(I), 12-24
Matthew R. Fisher, (2021). Essentials of Environmental Science by Kamala Doršner, Water Cycle and Fresh Water Supply, 7.1 Water Cycle and Fresh Water Supply – Environmental Biology (pressbooks.pub)
Minasny, B., & Hartemink, A. E. (2011). Predicting soil properties in the tropics. Earth-Science Reviews, 106,52–62
Mohammad, M. J., Krishna, P. V., Lamma, O. A., & Khan, S. (2015). Analysis of water quality using limnological studies of Wyra reservoir, Khammam District, Telangana, India. Int. J. Curr. Microbiol. App. Sci, 4(2), 880-895.
Minasny, B., & Hartemink, A. E. (2011). Predicting soil properties in the tropics. Earth-Science Reviews, 10
Munyati, C., & Ratshibvumo, T. (2011). Characterizing vegetation cover in relation to land use in the Inkomati catchment, South Africa, using Landsat imagery. Area, 43(2), 189–201. doi:10.1111/j.1475-4762.2010.00979.x
National Environmental Research Institute, (2021). Mapping challenges of the submerged coastal area vegetation while using remote airborne sensing, Challenges of remote sensing (dmu.dk)
Nelson, D. W., & Sommers, L. E. (1996). Total carbon, organic carbon, and organic matter. In: D. L. Sparks & et al. (Eds.), Society of America and American Society of Agronomy (pp. 961–1010). Madison, WI: Soil Science Society of America.
Obade Vincent de Paul, Rattan Lal &Jiquan Chen, (2013). Remote Sensing of Soil and Water Quality in Agroecosystems, Water Air Soil Pollut (2013) 224:1658, (PDF) Remote Sensing of Soil and Water Quality in Agroecosystems (researchgate.net)
Outhman, A. M., & Lamma, O. A. (2020). Investigate the contamination of tissue paper with heavy metals in the local market. IJCS, 8(1), 1264-1268.
Proud, S. R., Rasmussen, M. O., Fensholt, R., Sandholt, I., Shisanya, C., Mutero, W., Mbow, C., & Anyamba, A. (2010). Improving the SMAC atmospheric correction code analysis of Meteosat Second Generation NDVI and surface reflectance data. Remote Sensing of Environment, 114, 1687–1698.
Peltoniemi, J., Hakala, T., Suomalainen, J., & Puttonen, E. (2009). Polarized bidirectional reflectance factor measurements of soil, stones, and snow. Journal of Quantitative Spectroscopy and Radiative Transfer, 110, 1940–1953
Roy, D. P., Ju, J., Mbow, C., Frost, P., & Loveland, T. (2010). Accessing free Landsat data via the Internet: Africa’s challenge. Remote Sensing Letters, 1, 111–117
Stockmann, U., Adams, M. A., Crawford, J. W., Field, D. J., Henakaarchchi, N., Jenkins, M., Minasny, B., McBratney, A. B., de Courcelles, V. D., Singh, K., Wheeler, I., Abbott, L., Angers, D. A., Baldock, J., Bird, M., Brookes, P. C., Chenu, C., Jastrowh, J. D., Lal, R., Lehmann, J., O’Donnell, A. G., Parton, W. J., Whitehead, D., & Zimmermann, M. (2013). The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agriculture, Ecosystems & Environment, 164, 80–99.
Tuomisto, H. L., Hodge, I. D., Riordan, P., & Macdonald, D. W. (2012). Does organic farming reduce environmental impacts? - A meta-analysis of European research. Journal of Environmental Management, 112, 309–320.
Volker Wulfmeyer, R. Michael Hardesty, David D. Turner, Andreas Behrendt, Maria P. Cadeddu, Paolo Di Girolamo, Peter Schlüssel, Joël Van Baelen, Florian Zus, (2015). Review of Geophysics, A review of the remote sensing of lower Tropospheric thermodynamic profiles and its indispensable role of the understanding and the simulation of water and energy cycles. Significant Paper: A review of the remote sensing of lower Tropospheric thermodynamic profiles and its indispensable role of the understanding and the simulation of water and energy cycles. – NSSL News (noaa.gov)
Vertucci, F. A., & Likens, G. E. (1989). Spectral reflectance and water quality of Adirondack mountain region lakes. Limnology and Oceanography, 34, 1656–1672.
Walkley, A., & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37, 29–38.
Wielopolski, L., Chatterjee, A., Mitra, S., & Lal, R. (2011). In situ determination of soil carbon pool by inelastic neutron scattering: comparison with dry combustion. Geoderma, 160, 394–399
Zhang, S., Zhao, T. B., Wang, J., Qu, X. L., Chen, W., & Han, Y. (2013). Determination of fluorine, chlorine and bromine in household products by means of oxygen bomb combustion and ion chromatography. Journal of Chromatographic Science, 51, 65–69.
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