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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/22/4719 |
| _version_ | 1797508525220429824 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:05:09Z |
| format | Article |
| id | doaj.art-941b4e75161a4c2faac8ed4791bbdb07 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:05:09Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-941b4e75161a4c2faac8ed4791bbdb072023-11-23T01:22:14ZengMDPI AGRemote Sensing2072-42922021-11-011322471910.3390/rs13224719Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 ImageryAndrés Echeverría0Alejandro Urmeneta1María González-Audícana2Esther M González3Department of Science, Institute for Multidisciplinary Research in Applied Biology—IMAB, Public University of Navarra, 31006 Pamplona, SpainInformation Center of Natural Park and Biosphere Reserve of Bardenas Reales, San Marcial 19, 31500 Tudela, SpainDepartment of Engineering, Institute on Innovation and Sustainable Development in Food Chain—ISFOOD, Public University of Navarra, 31006 Pamplona, SpainDepartment of Science, Institute for Multidisciplinary Research in Applied Biology—IMAB, Public University of Navarra, 31006 Pamplona, SpainThe 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.https://www.mdpi.com/2072-4292/13/22/4719satellitevegetation indicessemiarid environmentBardenas Realeslegumesforage crops |
| spellingShingle | Andrés Echeverría Alejandro Urmeneta María González-Audícana Esther M González Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery Remote Sensing satellite vegetation indices semiarid environment Bardenas Reales legumes forage crops |
| title | 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_short | Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery |
| title_sort | monitoring rainfed alfalfa growth in semiarid agrosystems using sentinel 2 imagery |
| topic | satellite vegetation indices semiarid environment Bardenas Reales legumes forage crops |
| url | https://www.mdpi.com/2072-4292/13/22/4719 |
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