Utilizing neural networks for image downscaling and water quality monitoring
Article Main
Abstract
Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods.
Article Details
Article Details
Keywords
Image downscale, Lake turbidity, MODIS, Neural network, Sentinel-2
References
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Ackerman, S., Strabala, K. I., Menzel, W. P., Frey, R., Moeller, C. C. and Gumley, L. E. (1998). Discriminating clear-sky from cloud with MODIS. J. Geophys. Res., 103, 141-157.
Atkinson, P. M. (2013). Downscaling in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 22, 106-114. https://doi.org/10.1016/j.jag.2012.04.012.
Avdan, Z. Y., Kaplan, G., Goncu, S. &Avdan, U. (2019). Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS International Journal of Geo-Information, 8(12),553-564. https://doi.org/10.3390/ijgi8120553
Blix, K.; Pálffy, K.; Tóth, V. R. & Eltoft, T. (2018). Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI.Water, 10(10), 1428-1447. https://doi.org/10.3390/w10101428.
Bresciani, M., Giardino, C., Stroppiana, D., Dessena, M., Buscarinu, P., Cabras, L., Schenk, K., Heege, T., Bernet, H., Bazdanis G. &Tzimas, A. (2019). Monitoring water Quality in two dammed reservoirs from multispectral satellite sata. European Journal of Remote Sensing, 52(4),113-122.https://doi.org/10.1080/22797254.2019.16 86956
Canziani, G., Ferrati, R., Marinelli, C.&Dukatz, F. (2008). Artificial neural networks and remote sensing in the analysis of the highly variable Pampean Shallow Lakes. Mathematical Biosciences and Engineering, 5(4), 691-711. https://doi.org/10.3934/mbe.2008.5.691
Central Unit for Water Quality Monitoring (2009). A Report on Water Quality Survey of Lake Nasser at its Identified Cross- sectional Areas, Central Unit for Water Quality Monitoring, Ministry of Water Resources and Irrigation, Egypt.
Cox, R. M. Jr., Forsythe, R. D., Vaughan, G. E., & Olmsted, L. L. (1998) Assessing water quality in catawba river reservoirs using Landsat thematic mapper satellite data. Lake and Reservoir Management, 14(4), 405-416. https://doi.org/10.1080/07438149809354347.
Cui, J., Zhang, X. & Luo, M. (2018). Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery. Remote Sensing, 10(7),1047-1065.https://doi.org/10.33 90/rs10071047.
Erzin, Y., Rao, B.H., Patel, A., Gumaste, S.D. & Singh, D.N. (2010). Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity. International Journal of Thermal Sciences, 49(1),118-130.https://doi.org/10.1016/j.ijthermalsci.20 09.06.008.
Flores-Anderson, A. I., Griffin R., Dix M., Romero-Oliva C. S., Ochaeta G., Skinner-Alvarado J., Ramirez M. M. V., Hernandez B., Cherrington E., Page B., Barreno F. (2020). Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala. Frontiers in Environmental Science, 8(Article 7), 1-8. https://doi.org/10.3389/fenvs.2020.00007.
Fu, Y.,Xu, S., Zhang, C. &Sun, Y. (2018). Spatial Downscaling of MODIS Chlorophyll-a using Landsat 8 Images for Complex Coastal Water Monitoring.Estuarine, Coastal and Shelf Science, 209,149-159.https://doi.org/10.1016/j.ecss.2018.05.031.
Guan, X. (2009). Monitoring Lake submitted to Simcoe Water Quality Using Landsat TM Images.MScsubmitted to the University of Waterloo, Waterloo, Ontario, Canada.
Gummadi, J. A. (2013). Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio. MSc submitted to the University of Toledo,TheUnited States of America.
Hamre, K.D. Gerling, A.B., Munger, Z.W., Doubek, J.P., McClure, R.P., Cottingham, K.L. & Carey, C.C. (2017). Spatial Variation in Dinoflagellate Recruitment along a Reservoir Ecosystem Continuum. Journal of Plankton Research, 39(4), 715–728. https://doi.org/10.1093/plankt/fbx004.
Krenker, A., Bester, J. & Kos, A. (2011). Introduction to the Artificial Neural Networks, Artificial Neural Networks – Methodological Advances and Biomedical Applications, Kenji Suzuki (Ed.), InTech, 1-18. ISBN: 978-
953-307-243-2,.http://www.intechopen.com/books/artificial-neural-networks-methodological-advances-and-biomedical-applications/introduction-to-the-artificial-neural-networks
Kumar, N., Yamaç, S. Murugan, A. (2015). Applications of Remote Sensing and GIS in Natural Resource Management. The Andaman Science Association, 20, 1-6.
Li, W., Ni, L., Li, Z., Duan, S. & Wu, H. (2019). Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 7, 2299-2307.
Liu, W., Liu, Y., Mannaerts, C.M. &Wu, G. (2007). Monitoring variation of water turbidity and related environmental factors in Poyang lake National nature reserve, China. Proc. Society of Photo-Optical Instrumentation Engineers (SPIE) 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 67541H. https://doi.org/10.1117/12.764879.
McCullough, I.M., Loftin, C.S., & Sader, A.S. (2012). High-Frequency Remote Monitoring of Large Lakes with MODIS 500 m Imagery. Remote Sensing of Environment, 124,234-241. https://doi.org/10.1016/j.rse.2012.05.018.
Mohebzadeh, H. & Lee, T. (2020). Spatial downscaling of MODIS chlorophyll-a with machine learning techniques over the west coast of the Yellow Sea in South Korea. Journal of Oceanography, 77,103-122. https://doi.org/10.1007/s10872-020-00562-6.
Mohsen, A., Elshemy, M. &Zeidan, B. (2021). Water quality monitoring of lake Burullus (Egypt) using landsat satellite imageries. Environmental Science and Pollution Research, 28, 15687–15700. https://doi.org/10.1007/s11356-020-11765-1.
Moreno-Madrinan, M.J.; Al-Hamdan, M.Z.; Rickman, D.L.; Muller-Karger, F.E. (2010). Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida. Remote Sensing,2(12), 2713-2728. https://doi.org/10.3390/rs2122713.
Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva, J., & Ribeiro, S. (2017). Evaluation of Various Interpolation Techniques for Estimation of Selected Soil Properties. International Journal of GEOMATE, 13 (38), 23-30.Nile Research Institute (NRI) (2014). A Report on A Scientific Trip of Lake Nasser (from 19th December 2013 to 2nd January 2014), Nile Research Institute of the National Water Research center in collaboration with High Aswan Dam Authority of the Ministry of Water Resources and Irrigation,Egypt.
Nomura, R. & Oki, K. (2021). Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data. Remote Sensing, 13(4), 732-751. http://dx.doi.org/10.3390/rs13040732.
Salem, T. A. (2011). Variation of Water Quality and Phytoplankton along Different Zones of Aswan High Dam Reservoir. Egypt J. Aquat. Biol. & Fish., 15(2), 87 - 104. http://dx.doi.org/10.21608/ejabf.2011.2091.
Santi, E. (2010). An Application of the SFIM Technique to Enhance the Spatial Resolution of Spaceborne Microwave Radiometers. International Journal of Remote Sensing, 31(9), 2419-2428. https://doi.org/10.1080/014311609 03005725.
Segarra, J.; Buchaillot, M.L.; Araus, J. L. & Kefauver, S. C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10(5), 641-658. https://doi.org/10.3390/agronomy10050641.
Senanayakea, I.P. ,Yeoa I., Willgoosea, G. R., Hancockb, G. R. &Bretregera, D. (2019). Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia. 23rd International Congress on Modelling and Simulation, Canberra, ACT, Australia, 1-6 December ,2019.
Tianxiang, Z., Jinya, S., Cunjia, L., Wen-Hua, C., Hui, L. &Guohai, L. (2017). Band selection in sentinel-2 satellite for agriculture applications. Proceedings of the 23rd International Conference on Automation & Computing, University of Huddersfield, Huddersfield, UK, 7-8 September, 2017.
Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B. & Nõges, T (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8(8),640-653. https://doi.org/10.3390/rs8080640.
Vajsová, B.& Aastrand, P. J. (2015). New Sensors Benchmark Report on Sentinel -2A. JRC Technical Reports, Publications Office of the European Union. EUR 27674 EN.
Vermote, E. (2015a). MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09A1.006.
Vermote, E. (2015b). MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09Q1.006
Wang, Q., Shi, W., Li, Z. & Atkinson, P. (2016). Fusion of Sentinel-2 images. Remote Sensing of Environment, 187, 241-252. https://doi.org/10.1016/j.rse.2016.10.030.
Wu, M., Zhang, W., Wang, X. &Luo, D. (2009). Application of MODIS Satellite data in monitoring water quality parameters of Chaohu Lake in China. Environmental Monitoring and Assessment, 148, 255-264. https://doi.org/10.1007/s10661-008-0156-2.
Yoo, C., Im, J., Park, S., & Cho, D. (2020). Spatial Downscaling of MODIS Land surface temperature: Recent research trends, challenges, and future directions. Korean Journal of Remote Sensing, 36(4), 609-626. https://doi.org/10.7780/kjrs.2020.36.4.9
Zhu, L., Wang, S., Zhou, Y., Yan, F. & Zhou, W. (2004). Water quality monitoring in Taihu Lake using MODIS image data. IEEE International Geoscience and Remote Sensing Symposium, 2004 (IGARSS 2004), 4, 2314-2317.https://doi.org/10.1109/IGARSS.2004.1369749.
AbdEllah, R.G. & El-Geziry, T., (2016). Bathymetric study of some khors in Lake Nasser, Egypt. Lakes, Reservoir and ponds, 10(2), 139-158.
Abdullah, H. S. (2010). Water quality assessment for Dokan lake using landsat 8 OLI Satellite images. MSc Thesis submitted to the Faculty of Engineering Irrigation, University of Sulaimani, The Republic of Iraq.
Ackerman, S., Strabala, K. I., Menzel, W. P., Frey, R., Moeller, C. C. and Gumley, L. E. (1998). Discriminating clear-sky from cloud with MODIS. J. Geophys. Res., 103, 141-157.
Atkinson, P. M. (2013). Downscaling in remote sensing. International Journal of Applied Earth Observation and Geoinformation, 22, 106-114. https://doi.org/10.1016/j.jag.2012.04.012.
Avdan, Z. Y., Kaplan, G., Goncu, S. &Avdan, U. (2019). Monitoring the water quality of small water bodies using high-resolution remote sensing data. ISPRS International Journal of Geo-Information, 8(12),553-564. https://doi.org/10.3390/ijgi8120553
Blix, K.; Pálffy, K.; Tóth, V. R. & Eltoft, T. (2018). Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI.Water, 10(10), 1428-1447. https://doi.org/10.3390/w10101428.
Bresciani, M., Giardino, C., Stroppiana, D., Dessena, M., Buscarinu, P., Cabras, L., Schenk, K., Heege, T., Bernet, H., Bazdanis G. &Tzimas, A. (2019). Monitoring water Quality in two dammed reservoirs from multispectral satellite sata. European Journal of Remote Sensing, 52(4),113-122.https://doi.org/10.1080/22797254.2019.16 86956
Canziani, G., Ferrati, R., Marinelli, C.&Dukatz, F. (2008). Artificial neural networks and remote sensing in the analysis of the highly variable Pampean Shallow Lakes. Mathematical Biosciences and Engineering, 5(4), 691-711. https://doi.org/10.3934/mbe.2008.5.691
Central Unit for Water Quality Monitoring (2009). A Report on Water Quality Survey of Lake Nasser at its Identified Cross- sectional Areas, Central Unit for Water Quality Monitoring, Ministry of Water Resources and Irrigation, Egypt.
Cox, R. M. Jr., Forsythe, R. D., Vaughan, G. E., & Olmsted, L. L. (1998) Assessing water quality in catawba river reservoirs using Landsat thematic mapper satellite data. Lake and Reservoir Management, 14(4), 405-416. https://doi.org/10.1080/07438149809354347.
Cui, J., Zhang, X. & Luo, M. (2018). Combining Linear Pixel Unmixing and STARFM for Spatiotemporal Fusion of Gaofen-1 Wide Field of View Imagery and MODIS Imagery. Remote Sensing, 10(7),1047-1065.https://doi.org/10.33 90/rs10071047.
Erzin, Y., Rao, B.H., Patel, A., Gumaste, S.D. & Singh, D.N. (2010). Artificial neural network models for predicting electrical resistivity of soils from their thermal resistivity. International Journal of Thermal Sciences, 49(1),118-130.https://doi.org/10.1016/j.ijthermalsci.20 09.06.008.
Flores-Anderson, A. I., Griffin R., Dix M., Romero-Oliva C. S., Ochaeta G., Skinner-Alvarado J., Ramirez M. M. V., Hernandez B., Cherrington E., Page B., Barreno F. (2020). Hyperspectral Satellite Remote Sensing of Water Quality in Lake Atitlán, Guatemala. Frontiers in Environmental Science, 8(Article 7), 1-8. https://doi.org/10.3389/fenvs.2020.00007.
Fu, Y.,Xu, S., Zhang, C. &Sun, Y. (2018). Spatial Downscaling of MODIS Chlorophyll-a using Landsat 8 Images for Complex Coastal Water Monitoring.Estuarine, Coastal and Shelf Science, 209,149-159.https://doi.org/10.1016/j.ecss.2018.05.031.
Guan, X. (2009). Monitoring Lake submitted to Simcoe Water Quality Using Landsat TM Images.MScsubmitted to the University of Waterloo, Waterloo, Ontario, Canada.
Gummadi, J. A. (2013). Comparison of Various Interpolation Techniques for Modeling and Estimation of Radon Concentrations in Ohio. MSc submitted to the University of Toledo,TheUnited States of America.
Hamre, K.D. Gerling, A.B., Munger, Z.W., Doubek, J.P., McClure, R.P., Cottingham, K.L. & Carey, C.C. (2017). Spatial Variation in Dinoflagellate Recruitment along a Reservoir Ecosystem Continuum. Journal of Plankton Research, 39(4), 715–728. https://doi.org/10.1093/plankt/fbx004.
Krenker, A., Bester, J. & Kos, A. (2011). Introduction to the Artificial Neural Networks, Artificial Neural Networks – Methodological Advances and Biomedical Applications, Kenji Suzuki (Ed.), InTech, 1-18. ISBN: 978-
953-307-243-2,.http://www.intechopen.com/books/artificial-neural-networks-methodological-advances-and-biomedical-applications/introduction-to-the-artificial-neural-networks
Kumar, N., Yamaç, S. Murugan, A. (2015). Applications of Remote Sensing and GIS in Natural Resource Management. The Andaman Science Association, 20, 1-6.
Li, W., Ni, L., Li, Z., Duan, S. & Wu, H. (2019). Evaluation of machine learning algorithms in spatial downscaling of MODIS land surface temperature. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 7, 2299-2307.
Liu, W., Liu, Y., Mannaerts, C.M. &Wu, G. (2007). Monitoring variation of water turbidity and related environmental factors in Poyang lake National nature reserve, China. Proc. Society of Photo-Optical Instrumentation Engineers (SPIE) 6754, Geoinformatics 2007: Geospatial Information Technology and Applications, 67541H. https://doi.org/10.1117/12.764879.
McCullough, I.M., Loftin, C.S., & Sader, A.S. (2012). High-Frequency Remote Monitoring of Large Lakes with MODIS 500 m Imagery. Remote Sensing of Environment, 124,234-241. https://doi.org/10.1016/j.rse.2012.05.018.
Mohebzadeh, H. & Lee, T. (2020). Spatial downscaling of MODIS chlorophyll-a with machine learning techniques over the west coast of the Yellow Sea in South Korea. Journal of Oceanography, 77,103-122. https://doi.org/10.1007/s10872-020-00562-6.
Mohsen, A., Elshemy, M. &Zeidan, B. (2021). Water quality monitoring of lake Burullus (Egypt) using landsat satellite imageries. Environmental Science and Pollution Research, 28, 15687–15700. https://doi.org/10.1007/s11356-020-11765-1.
Moreno-Madrinan, M.J.; Al-Hamdan, M.Z.; Rickman, D.L.; Muller-Karger, F.E. (2010). Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida. Remote Sensing,2(12), 2713-2728. https://doi.org/10.3390/rs2122713.
Morgan, R.S., Abd El-Hady, M., Rahim, I.S., Silva, J., & Ribeiro, S. (2017). Evaluation of Various Interpolation Techniques for Estimation of Selected Soil Properties. International Journal of GEOMATE, 13 (38), 23-30.Nile Research Institute (NRI) (2014). A Report on A Scientific Trip of Lake Nasser (from 19th December 2013 to 2nd January 2014), Nile Research Institute of the National Water Research center in collaboration with High Aswan Dam Authority of the Ministry of Water Resources and Irrigation,Egypt.
Nomura, R. & Oki, K. (2021). Downscaling of MODIS NDVI by Using a Convolutional Neural Network-Based Model with Higher Resolution SAR Data. Remote Sensing, 13(4), 732-751. http://dx.doi.org/10.3390/rs13040732.
Salem, T. A. (2011). Variation of Water Quality and Phytoplankton along Different Zones of Aswan High Dam Reservoir. Egypt J. Aquat. Biol. & Fish., 15(2), 87 - 104. http://dx.doi.org/10.21608/ejabf.2011.2091.
Santi, E. (2010). An Application of the SFIM Technique to Enhance the Spatial Resolution of Spaceborne Microwave Radiometers. International Journal of Remote Sensing, 31(9), 2419-2428. https://doi.org/10.1080/014311609 03005725.
Segarra, J.; Buchaillot, M.L.; Araus, J. L. & Kefauver, S. C. (2020). Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy, 10(5), 641-658. https://doi.org/10.3390/agronomy10050641.
Senanayakea, I.P. ,Yeoa I., Willgoosea, G. R., Hancockb, G. R. &Bretregera, D. (2019). Using an artificial neural network to enhance the spatial resolution of satellite soil moisture products based on soil thermal inertia. 23rd International Congress on Modelling and Simulation, Canberra, ACT, Australia, 1-6 December ,2019.
Tianxiang, Z., Jinya, S., Cunjia, L., Wen-Hua, C., Hui, L. &Guohai, L. (2017). Band selection in sentinel-2 satellite for agriculture applications. Proceedings of the 23rd International Conference on Automation & Computing, University of Huddersfield, Huddersfield, UK, 7-8 September, 2017.
Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B. & Nõges, T (2016). First experiences in mapping lake water quality parameters with Sentinel-2 MSI imagery. Remote Sensing, 8(8),640-653. https://doi.org/10.3390/rs8080640.
Vajsová, B.& Aastrand, P. J. (2015). New Sensors Benchmark Report on Sentinel -2A. JRC Technical Reports, Publications Office of the European Union. EUR 27674 EN.
Vermote, E. (2015a). MOD09A1 MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09A1.006.
Vermote, E. (2015b). MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD09Q1.006
Wang, Q., Shi, W., Li, Z. & Atkinson, P. (2016). Fusion of Sentinel-2 images. Remote Sensing of Environment, 187, 241-252. https://doi.org/10.1016/j.rse.2016.10.030.
Wu, M., Zhang, W., Wang, X. &Luo, D. (2009). Application of MODIS Satellite data in monitoring water quality parameters of Chaohu Lake in China. Environmental Monitoring and Assessment, 148, 255-264. https://doi.org/10.1007/s10661-008-0156-2.
Yoo, C., Im, J., Park, S., & Cho, D. (2020). Spatial Downscaling of MODIS Land surface temperature: Recent research trends, challenges, and future directions. Korean Journal of Remote Sensing, 36(4), 609-626. https://doi.org/10.7780/kjrs.2020.36.4.9
Zhu, L., Wang, S., Zhou, Y., Yan, F. & Zhou, W. (2004). Water quality monitoring in Taihu Lake using MODIS image data. IEEE International Geoscience and Remote Sensing Symposium, 2004 (IGARSS 2004), 4, 2314-2317.https://doi.org/10.1109/IGARSS.2004.1369749.
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Utilizing neural networks for image downscaling and water quality monitoring. (2021). Journal of Applied and Natural Science, 13(4), 1452-1461. https://doi.org/10.31018/jans.v13i4.3146
