Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery

The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m<sup>2</sup> surfaces at 172 points inside 18 al...

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Autores principales:	Andrés Echeverría, Alejandro Urmeneta, María González-Audícana, Esther M González
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	satellite vegetation indices semiarid environment Bardenas Reales legumes forage crops Science Q
Acceso en línea:	https://doaj.org/article/941b4e75161a4c2faac8ed4791bbdb07
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id	oai:doaj.org-article:941b4e75161a4c2faac8ed4791bbdb07
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spelling	oai:doaj.org-article:941b4e75161a4c2faac8ed4791bbdb072021-11-25T18:55:43ZMonitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery10.3390/rs132247192072-4292https://doaj.org/article/941b4e75161a4c2faac8ed4791bbdb072021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4719https://doaj.org/toc/2072-4292The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m<sup>2</sup> surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (<i>R</i><sup>2</sup> = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.Andrés EcheverríaAlejandro UrmenetaMaría González-AudícanaEsther M GonzálezMDPI AGarticlesatellitevegetation indicessemiarid environmentBardenas Realeslegumesforage cropsScienceQENRemote Sensing, Vol 13, Iss 4719, p 4719 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	satellite vegetation indices semiarid environment Bardenas Reales legumes forage crops Science Q
spellingShingle	satellite vegetation indices semiarid environment Bardenas Reales legumes forage crops Science Q Andrés Echeverría Alejandro Urmeneta María González-Audícana Esther M González Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
description	The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m<sup>2</sup> surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (<i>R</i><sup>2</sup> = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.
format	article
author	Andrés Echeverría Alejandro Urmeneta María González-Audícana Esther M González
author_facet	Andrés Echeverría Alejandro Urmeneta María González-Audícana Esther M González
author_sort	Andrés Echeverría
title	Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
title_short	Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
title_full	Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
title_fullStr	Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
title_full_unstemmed	Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
title_sort	monitoring rainfed alfalfa growth in semiarid agrosystems using sentinel-2 imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/941b4e75161a4c2faac8ed4791bbdb07
work_keys_str_mv	AT andresecheverria monitoringrainfedalfalfagrowthinsemiaridagrosystemsusingsentinel2imagery AT alejandrourmeneta monitoringrainfedalfalfagrowthinsemiaridagrosystemsusingsentinel2imagery AT mariagonzalezaudicana monitoringrainfedalfalfagrowthinsemiaridagrosystemsusingsentinel2imagery AT esthermgonzalez monitoringrainfedalfalfagrowthinsemiaridagrosystemsusingsentinel2imagery
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Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery

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