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

Nduku, Lwandile; Munghemezulu, Cilence; Mashaba-Munghemezulu, Zinhle; Ratshiedana, Phathutshedzo Eugene; Sibanda, Sipho; Chirima, Johannes George

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

2024

This article belongs to the Special Issue titled 'Application of Remote Sensing and GIS in Agricultural Engineering'.

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DATA AVAILABILITY STATEMENT : Data used in this study will be made available upon request.

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Please read abstract in article.

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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).

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http://www.mdpi.com/journal/agriengineering

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Geography, Geoinformatics and Meteorology

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SDG-02:Zero Hunger

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SDG-09: Industry, innovation and infrastructure

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SDG-15:Life on land

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AGROVOC关键词

synthetic aperture radar (sar) wheat

书目信息

出版者

MDPI

其它主题

Sentinel-1; Support vector machine regression (svmr); Sdg-09: industry; Support vector machine regression; Optimized random forest regression (rfr); Crop height; Neural network regression (nnr); Sdg-15: life on land; Decision tree regression (dtr); Sdg-02: zero hunger; Innovation and infrastructure; Machine-learning algorithms; Random forest regression; Sentinel-2

语言

英语

格式

application/pdf

许可

© 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/).

ISSN

2624-7402

类型

Journal Article

自何时收录于AGRIS: 2024-10-08

修改日期: 2024-10-08

格式: Dublin Core

数据提供者

这条记录提供自 University of Pretoria

发现该数据提供方在AGRIS的更多集合

链接

DOI DOI http://hdl.handle.net/2263/97389