Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery

Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and he...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mahlatse Kganyago, Paidamwoyo Mhangara, Clement Adjorlolo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	leaf area index leaf chlorophyll content canopy chlorophyll content Sentinel-2 sparse partial least squares random forest Science Q
Acceso en línea:	https://doaj.org/article/1d0f43eba98e4faeaf3197843d31814e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1d0f43eba98e4faeaf3197843d31814e
record_format	dspace
spelling	oai:doaj.org-article:1d0f43eba98e4faeaf3197843d31814e2021-11-11T18:53:44ZEstimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery10.3390/rs132143142072-4292https://doaj.org/article/1d0f43eba98e4faeaf3197843d31814e2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4314https://doaj.org/toc/2072-4292Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LC<i><sub>ab</sub></i>), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LC<i><sub>ab</sub></i>, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LC<i><sub>ab</sub></i>, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities.Mahlatse KganyagoPaidamwoyo MhangaraClement AdjorloloMDPI AGarticleleaf area indexleaf chlorophyll contentcanopy chlorophyll contentSentinel-2sparse partial least squaresrandom forestScienceQENRemote Sensing, Vol 13, Iss 4314, p 4314 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	leaf area index leaf chlorophyll content canopy chlorophyll content Sentinel-2 sparse partial least squares random forest Science Q
spellingShingle	leaf area index leaf chlorophyll content canopy chlorophyll content Sentinel-2 sparse partial least squares random forest Science Q Mahlatse Kganyago Paidamwoyo Mhangara Clement Adjorlolo Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
description	Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LC<i><sub>ab</sub></i>), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LC<i><sub>ab</sub></i>, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LC<i><sub>ab</sub></i>, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities.
format	article
author	Mahlatse Kganyago Paidamwoyo Mhangara Clement Adjorlolo
author_facet	Mahlatse Kganyago Paidamwoyo Mhangara Clement Adjorlolo
author_sort	Mahlatse Kganyago
title	Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
title_short	Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
title_full	Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
title_fullStr	Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
title_full_unstemmed	Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery
title_sort	estimating crop biophysical parameters using machine learning algorithms and sentinel-2 imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1d0f43eba98e4faeaf3197843d31814e
work_keys_str_mv	AT mahlatsekganyago estimatingcropbiophysicalparametersusingmachinelearningalgorithmsandsentinel2imagery AT paidamwoyomhangara estimatingcropbiophysicalparametersusingmachinelearningalgorithmsandsentinel2imagery AT clementadjorlolo estimatingcropbiophysicalparametersusingmachinelearningalgorithmsandsentinel2imagery
_version_	1718431690578198528

Estimating Crop Biophysical Parameters Using Machine Learning Algorithms and Sentinel-2 Imagery

Ejemplares similares