UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review
Monitoring chlorophyll-a content is crucial for irrigation water quality, as excessive levels can harm water bodies and reduce their volumetric capacity due to algal growth. While satellite data enhances monitoring, its coarse resolution limits application in small water bodies. Unmanned Aerial Vehi...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2452246 |
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| author | Nobubelo Ngwenya Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Tafadzwanashe Mabhaudhi |
| author_facet | Nobubelo Ngwenya Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Tafadzwanashe Mabhaudhi |
| author_sort | Nobubelo Ngwenya |
| collection | DOAJ |
| description | Monitoring chlorophyll-a content is crucial for irrigation water quality, as excessive levels can harm water bodies and reduce their volumetric capacity due to algal growth. While satellite data enhances monitoring, its coarse resolution limits application in small water bodies. Unmanned Aerial Vehicles (UAVs) offer high-resolution, near-real-time data, bridging this gap. This review explores global progress, gaps, and recommendations on UAV-based chlorophyll-a monitoring in small inland water bodies, focusing on sensor characteristics, platforms, validation data and retrieval algorithms, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach. Multispectral sensors onboard DJI UAVs are the most widely used and, machine learning methods like random forest dominate chlorophyll-a inversion models. However, gaps remain in Africa due to high UAV costs, limited expertise and stringent regulations. Additionally, a universal chlorophyll-a retrieval method is also lacking. This review serves as a reference for future studies, highlighting UAVs' potential in water quality monitoring. |
| format | Article |
| id | doaj-art-ebd81f4b9303490b91b05b370ed5ca16 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-ebd81f4b9303490b91b05b370ed5ca162025-08-20T01:56:42ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2452246UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic reviewNobubelo Ngwenya0Tsitsi Bangira1Mbulisi Sibanda2Seifu Kebede Gurmessa3Tafadzwanashe Mabhaudhi4Centre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg, South AfricaCentre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg, South AfricaCentre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg, South AfricaCentre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg, South AfricaCentre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg, South AfricaMonitoring chlorophyll-a content is crucial for irrigation water quality, as excessive levels can harm water bodies and reduce their volumetric capacity due to algal growth. While satellite data enhances monitoring, its coarse resolution limits application in small water bodies. Unmanned Aerial Vehicles (UAVs) offer high-resolution, near-real-time data, bridging this gap. This review explores global progress, gaps, and recommendations on UAV-based chlorophyll-a monitoring in small inland water bodies, focusing on sensor characteristics, platforms, validation data and retrieval algorithms, using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach. Multispectral sensors onboard DJI UAVs are the most widely used and, machine learning methods like random forest dominate chlorophyll-a inversion models. However, gaps remain in Africa due to high UAV costs, limited expertise and stringent regulations. Additionally, a universal chlorophyll-a retrieval method is also lacking. This review serves as a reference for future studies, highlighting UAVs' potential in water quality monitoring.https://www.tandfonline.com/doi/10.1080/10106049.2025.2452246Chlorophyll-ainland watersremote sensingsystematic reviewunmanned aerial vehicle |
| spellingShingle | Nobubelo Ngwenya Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Tafadzwanashe Mabhaudhi UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review Geocarto International Chlorophyll-a inland waters remote sensing systematic review unmanned aerial vehicle |
| title | UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review |
| title_full | UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review |
| title_fullStr | UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review |
| title_full_unstemmed | UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review |
| title_short | UAV-based remote sensing of chlorophyll-a concentrations in inland water bodies: a systematic review |
| title_sort | uav based remote sensing of chlorophyll a concentrations in inland water bodies a systematic review |
| topic | Chlorophyll-a inland waters remote sensing systematic review unmanned aerial vehicle |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2452246 |
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