Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas
This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to g...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10909520/ |
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| author | Peng Shu Rana Waqar Aslam Iram Naz Bushra Ghaffar Dmitry E. Kucher Abdul Quddoos Danish Raza M. Abdullah-Al-Wadud Rana Muhammad Zulqarnain |
| author_facet | Peng Shu Rana Waqar Aslam Iram Naz Bushra Ghaffar Dmitry E. Kucher Abdul Quddoos Danish Raza M. Abdullah-Al-Wadud Rana Muhammad Zulqarnain |
| author_sort | Peng Shu |
| collection | DOAJ |
| description | This study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to generate high-resolution (0.5 m) outputs, achieving a peak signal-to-noise ratio of 32.4 dB and structural similarity index of 0.91. The enhanced imagery enabled precise delineation of urban features and environmental parameters affecting groundwater quality. Using the super-resolved images alongside traditional water quality parameters (pH, hardness, TDS) analyzed through fuzzy analytic hierarchy process, we calculated the groundwater quality index (GWQI) for 33 areas across four years (2008–2020). Results showed most areas achieved “Better water” quality status by 2020, though two regions (Old City and Anarkali) were classified as “Poor water” quality. We observed a moderate negative correlation (<italic>r</italic> = -0.62, <italic>p</italic> < 0.001) between GWQI and static water level depth, with significant depth increases in areas such as Dholanwal (37.1 m), Ichhra (47.06 m), and Township (49.35 m) by 2020. The integration of super-resolution remote sensing with conventional water quality assessment demonstrates promising applications for urban environmental monitoring and groundwater resource management. |
| format | Article |
| id | doaj-art-a98655783dec4494bf9947e9c754a9fe |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a98655783dec4494bf9947e9c754a9fe2025-08-20T03:40:52ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01187933794910.1109/JSTARS.2025.354801010909520Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban AreasPeng Shu0Rana Waqar Aslam1https://orcid.org/0000-0002-8711-8700Iram Naz2https://orcid.org/0009-0002-9509-9914Bushra Ghaffar3https://orcid.org/0000-0003-1630-5119Dmitry E. Kucher4https://orcid.org/0000-0002-7919-3487Abdul Quddoos5https://orcid.org/0009-0007-3493-9920Danish Raza6https://orcid.org/0000-0002-7623-3666M. Abdullah-Al-Wadud7https://orcid.org/0000-0001-6767-3574Rana Muhammad Zulqarnain8https://orcid.org/0000-0002-2656-8679School of Network & Communication Engineering, Chengdu Technological University, Chengdu, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Environmental Science, Faculty of Sciences, International Islamic University, Islamabad, PakistanDepartment of Environmental Management, Institute of Environmental Engineering, RUDN University, Moscow, RussiaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Mathematics, Saveetha School of Engineering, SIMATS Thandalam, Chennai, IndiaThis study presents a novel deep learning-based super-resolution framework for enhancing remote sensing imagery to assess groundwater quality and environmental conditions in Lahore, Pakistan. We developed a convolutional neural network architecture that upscales low-resolution satellite imagery to generate high-resolution (0.5 m) outputs, achieving a peak signal-to-noise ratio of 32.4 dB and structural similarity index of 0.91. The enhanced imagery enabled precise delineation of urban features and environmental parameters affecting groundwater quality. Using the super-resolved images alongside traditional water quality parameters (pH, hardness, TDS) analyzed through fuzzy analytic hierarchy process, we calculated the groundwater quality index (GWQI) for 33 areas across four years (2008–2020). Results showed most areas achieved “Better water” quality status by 2020, though two regions (Old City and Anarkali) were classified as “Poor water” quality. We observed a moderate negative correlation (<italic>r</italic> = -0.62, <italic>p</italic> < 0.001) between GWQI and static water level depth, with significant depth increases in areas such as Dholanwal (37.1 m), Ichhra (47.06 m), and Township (49.35 m) by 2020. The integration of super-resolution remote sensing with conventional water quality assessment demonstrates promising applications for urban environmental monitoring and groundwater resource management.https://ieeexplore.ieee.org/document/10909520/Deep learningenvironmental monitoringfuzzy analytic hierarchy process (AHP)groundwater quality index (GWQI)super-resolutionurban water management |
| spellingShingle | Peng Shu Rana Waqar Aslam Iram Naz Bushra Ghaffar Dmitry E. Kucher Abdul Quddoos Danish Raza M. Abdullah-Al-Wadud Rana Muhammad Zulqarnain Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning environmental monitoring fuzzy analytic hierarchy process (AHP) groundwater quality index (GWQI) super-resolution urban water management |
| title | Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas |
| title_full | Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas |
| title_fullStr | Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas |
| title_full_unstemmed | Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas |
| title_short | Deep Learning-Based Super-Resolution of Remote Sensing Images for Enhanced Groundwater Quality Assessment and Environmental Monitoring in Urban Areas |
| title_sort | deep learning based super resolution of remote sensing images for enhanced groundwater quality assessment and environmental monitoring in urban areas |
| topic | Deep learning environmental monitoring fuzzy analytic hierarchy process (AHP) groundwater quality index (GWQI) super-resolution urban water management |
| url | https://ieeexplore.ieee.org/document/10909520/ |
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