Synergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learning

Nduku, LwandileMunghemezulu, CilenceMashaba-Munghemezulu, ZinhleRatshiedana, Phathutshedzo EugeneSibanda, SiphoChirima, Johannes George2024-08-012024-08-012024-06Nduku, 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/agriengineering6020063.2624-7402 (online)10.3390/agriengineering6020063http://hdl.handle.net/2263/97389This article belongs to the Special Issue titled 'Application of Remote Sensing and GIS in Agricultural Engineering'.DATA AVAILABILITY STATEMENT : Data used in this study will be made available upon request.Please read abstract in article.en© 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/).Crop heightSentinel-1Sentinel-2Random forest regressionSupport vector machine regressionWheatSynthetic aperture radar (SAR)Optimized random forest regression (RFR)Support vector machine regression (SVMR)Decision tree regression (DTR)Neural network regression (NNR)Machine-learning algorithmsSDG-02: Zero hungerSDG-09: Industry, innovation and infrastructureSDG-15: Life on landSynergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learningArticle