Abstract
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 m2 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 (R2 = 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.
Highlights
The normalized difference vegetation index (NDVI) has been widely used to identify and monitor areas covered with vegetation [24,25]
0.345 between fractional vegetation cover (FVC) and NDVI data derived from the Sentinel-2 satellite multispectral images
We explored the suitability of Sentinel-2 imagery for the monitoring of rainfed alfalfa crop establishment in the semiarid Bardenas Reales area
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As a temperate legume that usually grows in arid and semiarid regions, alfalfa can reach deep soils to obtain water through its well-developed root system [5,6] In this context, Medicago species, such as alfalfa, have been promoted in countries, such as the United States, Canada, Australia, and New. Zealand, in order to improve and reduce the feeding of herds in rainfed areas [7]. The normalized difference vegetation index (NDVI) has been widely used to identify and monitor areas covered with vegetation [24,25] This index exploits the fact that green, healthy vegetation displays contrast-reflecting behavior between the red and near-infrared spectral bands, providing a good indication of the vegetation “greenness” [26]. The aim of this study was to assess Sentinel-2 images for their suitability in the monitoring of the FVC of rainfed alfalfa in semiarid areas. Were used to produce time series over the cultivation period in twenty-eight experimental rainfed alfalfa throughout the study area
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