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(2) 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 (R-2 = 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.

Automated summary

This study assessed the utility of Sentinel-2 images in monitoring rainfed alfalfa vegetation cover in semiarid areas like Bardenas Reales in Spain. Results showed a high correlation between NDVI and FVC at the parcel level, while the correlation at the pixel level remained moderate. The findings suggest that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, potentially increasing pasture management success.