{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:34:38Z","timestamp":1778758478895,"version":"3.51.4"},"reference-count":70,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T00:00:00Z","timestamp":1634083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004424","name":"Water Research Commission","doi-asserted-by":"publisher","award":["WRC K5\/2971\/\/4"],"award-info":[{"award-number":["WRC K5\/2971\/\/4"]}],"id":[{"id":"10.13039\/501100004424","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["84157"],"award-info":[{"award-number":["84157"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","award":["119409"],"award-info":[{"award-number":["119409"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Determining maize water content variability is necessary for crop monitoring and in developing early warning systems to optimise agricultural production in smallholder farms. However, spatially explicit information on maize water content, particularly in Southern Africa, remains elementary due to the shortage of efficient and affordable primary sources of suitable spatial data at a local scale. Unmanned Aerial Vehicles (UAVs), equipped with light-weight multispectral sensors, provide spatially explicit, near-real-time information for determining the maize crop water status at farm scale. Therefore, this study evaluated the utility of UAV-derived multispectral imagery and machine learning techniques in estimating maize leaf water indicators: equivalent water thickness (EWT), fuel moisture content (FMC), and specific leaf area (SLA). The results illustrated that both NIR and red-edge derived spectral variables were critical in characterising the maize water indicators on smallholder farms. Furthermore, the best models for estimating EWT, FMC, and SLA were derived from the random forest regression (RFR) algorithm with an rRMSE of 3.13%, 1%, and 3.48%, respectively. Additionally, EWT and FMC yielded the highest predictive performance and were the most optimal indicators of maize leaf water content. The findings are critical towards developing a robust and spatially explicit monitoring framework of maize water status and serve as a proxy of crop health and the overall productivity of smallholder maize farms.<\/jats:p>","DOI":"10.3390\/rs13204091","type":"journal-article","created":{"date-parts":[[2021,10,13]],"date-time":"2021-10-13T21:48:39Z","timestamp":1634161719000},"page":"4091","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Helen S.","family":"Ndlovu","sequence":"first","affiliation":[{"name":"Discipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Odindi","sequence":"additional","affiliation":[{"name":"Discipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4589-7099","authenticated-orcid":false,"given":"Mbulisi","family":"Sibanda","sequence":"additional","affiliation":[{"name":"Department of Geography, Environmental Studies & Tourism, Faculty of Arts, University of the Western Cape, Bellville, Cape Town 7535, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7358-8111","authenticated-orcid":false,"given":"Onisimo","family":"Mutanga","sequence":"additional","affiliation":[{"name":"Discipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alistair","family":"Clulow","sequence":"additional","affiliation":[{"name":"Discipline of Agrometeorology, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Scottsville, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9912-9848","authenticated-orcid":false,"given":"Vimbayi G. P.","family":"Chimonyo","sequence":"additional","affiliation":[{"name":"Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa"},{"name":"International Maize and Wheat Improvement Center (CIMMYT)-Zimbabwe, Mt Pleasant, Harare P.O. Box MP 163, Zimbabwe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9323-8127","authenticated-orcid":false,"given":"Tafadzwanashe","family":"Mabhaudhi","sequence":"additional","affiliation":[{"name":"Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal (UKZN), Scottsville, Pietermaritzburg 3209, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,13]]},"reference":[{"key":"ref_1","unstructured":"Avetisyan, D., and Cvetanova, G. (2019). Water Status Assessment in Maize and Sunflower Crops Using Sentinel-2 Multispectral Data. Space Ecol. Saf., 152\u2013157."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ngoune Tandzi, L., and Mutengwa, C.S. (2020). Estimation of maize (Zea mays L.) yield per harvest area: Appropriate methods. 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