Synergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learning
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| dc.contributor.author | Nduku, Lwandile
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| dc.contributor.author | Munghemezulu, Cilence
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| dc.contributor.author | Mashaba-Munghemezulu, Zinhle
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| dc.contributor.author | Ratshiedana, Phathutshedzo Eugene
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| dc.contributor.author | Sibanda, Sipho
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| dc.contributor.author | Chirima, Johannes George
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| dc.date.accessioned | 2024-08-01T09:08:22Z | |
| dc.date.available | 2024-08-01T09:08:22Z | |
| dc.date.issued | 2024-06 | |
| dc.description | This article belongs to the Special Issue titled 'Application of Remote Sensing and GIS in Agricultural Engineering'. | en_US |
| dc.description | DATA AVAILABILITY STATEMENT : Data used in this study will be made available upon request. | en_US |
| dc.description.abstract | Please read abstract in article. | en_US |
| dc.description.department | Geography, Geoinformatics and Meteorology | en_US |
| dc.description.sdg | SDG-02:Zero Hunger | en_US |
| dc.description.sdg | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.description.sdg | SDG-15:Life on land | en_US |
| dc.description.sponsorship | The Council for Scientific and Industrial Research (CSIR), the Department of Science and Innovation (DSI), the Agricultural Research Council-Natural Resources and Engineering (ARC-NRE), and National Research Foundation (NRF). | en_US |
| dc.description.uri | http://www.mdpi.com/journal/agriengineering | en_US |
| dc.identifier.citation | Nduku, L.; Munghemezulu, C.; Mashaba-Munghemezulu, Z.; Ratshiedana, P.E.; Sibanda, S.; Chirima, J.G. Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning. AgriEngineering 2024, 6, 1093–1116. https://doi.org/10.3390/agriengineering6020063. | en_US |
| dc.identifier.issn | 2624-7402 (online) | |
| dc.identifier.other | 10.3390/agriengineering6020063 | |
| dc.identifier.uri | http://hdl.handle.net/2263/97389 | |
| dc.language.iso | en | en_US |
| dc.publisher | MDPI | en_US |
| dc.rights | © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). | en_US |
| dc.subject | Crop height | en_US |
| dc.subject | Sentinel-1 | en_US |
| dc.subject | Sentinel-2 | en_US |
| dc.subject | Random forest regression | en_US |
| dc.subject | Support vector machine regression | en_US |
| dc.subject | Wheat | en_US |
| dc.subject | Synthetic aperture radar (SAR) | en_US |
| dc.subject | Optimized random forest regression (RFR) | en_US |
| dc.subject | Support vector machine regression (SVMR) | en_US |
| dc.subject | Decision tree regression (DTR) | en_US |
| dc.subject | Neural network regression (NNR) | en_US |
| dc.subject | Machine-learning algorithms | en_US |
| dc.subject | SDG-02: Zero hunger | en_US |
| dc.subject | SDG-09: Industry, innovation and infrastructure | en_US |
| dc.subject | SDG-15: Life on land | en_US |
| dc.title | Synergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learning | en_US |
| dc.type | Article | en_US |
