Synergetic use of Sentinel-1 and Sentinel-2 data for wheat-crop height monitoring using machine learning
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Date
Authors
Nduku, Lwandile
Munghemezulu, Cilence
Mashaba-Munghemezulu, Zinhle
Ratshiedana, Phathutshedzo Eugene
Sibanda, Sipho
Chirima, Johannes George
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
Abstract
Please read abstract in article.
Description
This 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.
DATA AVAILABILITY STATEMENT : Data used in this study will be made available upon request.
Keywords
Crop height, Sentinel-1, Sentinel-2, Random forest regression, Support vector machine regression, Wheat, Synthetic aperture radar (SAR), Optimized random forest regression (RFR), Support vector machine regression (SVMR), Decision tree regression (DTR), Neural network regression (NNR), Machine-learning algorithms, SDG-02: Zero hunger, SDG-09: Industry, innovation and infrastructure, SDG-15: Life on land
Sustainable Development Goals
SDG-02:Zero Hunger
SDG-09: Industry, innovation and infrastructure
SDG-15:Life on land
SDG-09: Industry, innovation and infrastructure
SDG-15:Life on land
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.