Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions
This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term d...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2347935 |
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| author | Maria Sigopi Cletah Shoko Timothy Dube |
| author_facet | Maria Sigopi Cletah Shoko Timothy Dube |
| author_sort | Maria Sigopi |
| collection | DOAJ |
| description | This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities. |
| format | Article |
| id | doaj-art-c72441cde96643feaeedd5a6af47ceaa |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-c72441cde96643feaeedd5a6af47ceaa2024-12-10T08:23:09ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2347935Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directionsMaria Sigopi0Cletah Shoko1Timothy Dube2Institute for Water Studies, University of the Western Cape, South Africa;Department of Geography School of Geography, University of Witwatersrand, Johannesburg, South AfricaInstitute for Water Studies, University of the Western Cape, South Africa;This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.https://www.tandfonline.com/doi/10.1080/10106049.2024.2347935Ariditybig dataclimate changedata extraction and integrationmulti-date satellite datasurface water dynamics |
| spellingShingle | Maria Sigopi Cletah Shoko Timothy Dube Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions Geocarto International Aridity big data climate change data extraction and integration multi-date satellite data surface water dynamics |
| title | Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions |
| title_full | Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions |
| title_fullStr | Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions |
| title_full_unstemmed | Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions |
| title_short | Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions |
| title_sort | advancements in remote sensing technologies for accurate monitoring and management of surface water resources in africa an overview limitations and future directions |
| topic | Aridity big data climate change data extraction and integration multi-date satellite data surface water dynamics |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2347935 |
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