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Journal of Sensors
Volume 2016, Article ID 4612809, 16 pages
http://dx.doi.org/10.1155/2016/4612809
Research Article

Remote Sensing of Grassland Biophysical Parameters in the Context of the Sentinel-2 Satellite Mission

1Sustainable Agro-Ecosystems and Bioresources Department, Research and Innovation Centre, Edmund Mach Foundation, Via E. Mach 1, 38010 San Michele all’Adige, Italy
2Meteorology Department, Poznan University of Life Sciences, Piatkowska Street 94, 60-649 Poznań, Poland
3Foxlab Joint CNR-FEM Initiative, Via E. Mach 1, 38010 San Michele all’Adige, Italy

Received 27 November 2015; Accepted 22 March 2016

Academic Editor: Hairong Qi

Copyright © 2016 Karolina Sakowska et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This study investigates the potential of the Sentinel-2 satellite for monitoring the seasonal changes in grassland total canopy chlorophyll content (CCC), fraction of photosynthetically active radiation absorbed by the vegetation canopy (FAPAR), and fraction of photosynthetically active radiation absorbed only by its photosynthesizing components (GFAPAR). Reflectance observations were collected on a continuous basis during growing seasons by means of a newly developed ASD-WhiteRef system. Two models using Sentinel-2 simulated data (linear regression-vegetation indices (VIs) approach and multiple regression (MR) reflectance approach) were tested to estimate vegetation biophysical parameters. To assess whether the use of full solar spectrum reflectance data is able to provide an added value in CCC and GFAPAR estimation accuracy, a third model based on partial least squares regression (PLSR) and the ASD-WhiteRef reflectance data was tested. The results showed that FAPAR remained quite stable during the reproduction and senescence stages, and no significant relationships between FAPAR and VIs were found. On the other hand, GFAPAR showed clearer seasonal trends. The comparison of the three models revealed no significant differences in the accuracies of CCC and GFAPAR predictions and demonstrated a strong contribution of SWIR bands to the explained variability of investigated parameters. The promising results highlight the potential of the Sentinel-2 satellite for retrieving biophysical parameters from space.