Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities
Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water q...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/8/12/733 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850042300474851328 |
|---|---|
| author | Shannyn Jade Pillay Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Alistair Clulow Tafadzwanashe Mabhaudhi |
| author_facet | Shannyn Jade Pillay Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Alistair Clulow Tafadzwanashe Mabhaudhi |
| author_sort | Shannyn Jade Pillay |
| collection | DOAJ |
| description | Monitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies. |
| format | Article |
| id | doaj-art-1e6d7dfdd8dd4534b763801ae7b2cf43 |
| institution | DOAJ |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-1e6d7dfdd8dd4534b763801ae7b2cf432025-08-20T02:55:36ZengMDPI AGDrones2504-446X2024-12-0181273310.3390/drones8120733Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and OpportunitiesShannyn Jade Pillay0Tsitsi Bangira1Mbulisi Sibanda2Seifu Kebede Gurmessa3Alistair Clulow4Tafadzwanashe Mabhaudhi5Centre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaCentre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South AfricaDepartment of Geography, Environmental Studies and Tourism, University of the Western Cape, Private Bag X17, Bellville 7535, South AfricaCentre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaDiscipline of Agro-Meteorology, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South AfricaCentre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South AfricaMonitoring water quality is crucial for understanding aquatic ecosystem health and changes in physical, chemical, and microbial water quality standards. Water quality critically influences industrial, agricultural, and domestic uses of water. Remote sensing techniques can monitor and measure water quality parameters accurately and quantitatively. Earth observation satellites equipped with optical and thermal sensors have proven effective in providing the temporal and spatial data required for monitoring the water quality of inland water bodies. However, using satellite-derived data are associated with coarse spatial resolution and thus are unsuitable for monitoring the water quality of small inland water bodies. With the development of unmanned aerial vehicles (UAVs) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval of small water bodies, which provides water for crop irrigation. This article presents the application of remotely sensed data from UAVs to retrieve key water quality parameters such as surface water temperature, total suspended solids (TSS), and Chromophoric dissolved organic matter (CDOM) in inland water bodies. In particular, the review comprehensively analyses the potential advancements in utilising drone technology along with machine learning algorithms, platform type, sensor characteristics, statistical metrics, and validation techniques for monitoring these water quality parameters. The study discusses the strengths, challenges, and limitations of using UAVs in estimating water temperature, TSS, and CDOM in small water bodies. Finally, possible solutions and remarks for retrieving water quality parameters using UAVs are provided. The review is important for future development and research in water quality for agricultural production in small water bodies.https://www.mdpi.com/2504-446X/8/12/733unmanned aerial vehicleswater quality monitoringTSSCDOMmachine learning algorithmremote sensing |
| spellingShingle | Shannyn Jade Pillay Tsitsi Bangira Mbulisi Sibanda Seifu Kebede Gurmessa Alistair Clulow Tafadzwanashe Mabhaudhi Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities Drones unmanned aerial vehicles water quality monitoring TSS CDOM machine learning algorithm remote sensing |
| title | Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities |
| title_full | Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities |
| title_fullStr | Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities |
| title_full_unstemmed | Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities |
| title_short | Assessing Drone-Based Remote Sensing for Monitoring Water Temperature, Suspended Solids and CDOM in Inland Waters: A Global Systematic Review of Challenges and Opportunities |
| title_sort | assessing drone based remote sensing for monitoring water temperature suspended solids and cdom in inland waters a global systematic review of challenges and opportunities |
| topic | unmanned aerial vehicles water quality monitoring TSS CDOM machine learning algorithm remote sensing |
| url | https://www.mdpi.com/2504-446X/8/12/733 |
| work_keys_str_mv | AT shannynjadepillay assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities AT tsitsibangira assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities AT mbulisisibanda assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities AT seifukebedegurmessa assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities AT alistairclulow assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities AT tafadzwanashemabhaudhi assessingdronebasedremotesensingformonitoringwatertemperaturesuspendedsolidsandcdomininlandwatersaglobalsystematicreviewofchallengesandopportunities |