Author's personal copy Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance P.J. Zarco-Tejada a, ⁎, A. Morales a, b , L. Testi a , F.J. Villalobos a, c a Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain b Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands c Departamento de Agronomía, Universidad de Córdoba, Campus Universitario de Rabanales, 14014 Córdoba, Spain abstract article info Article history: Received 4 November 2012 Received in revised form 4 February 2013 Accepted 7 February 2013 Available online xxxx Keywords: Hyperspectral Physiological indices Eddy covariance Chlorophyll Narrow-band indices Gross primary production GPP Unmanned aerial vehicle UAV This study provides insight into the assessment of the spatio-temporal trends of chlorophyll fluorescence, narrow-band physiological indices, and structural indices acquired with a hyperspectral imager flown over a flux tower in a canopy characterized by small seasonal structural changes and a heterogeneous architecture. A total of seven flights between summer and autumn were conducted with a hyperspectral camera that captured 30 cm resolution imagery and 260 spectral bands in the 400-900 nm region. This enabled the identification of pure-vegetation tree crown pixels around an eddy covariance flux tower without shadow components or back- ground effects. The hyperspectral imagery was used to study the temporal patterns of canopy fluorescence and reflectance indices related to physiology and canopy structure. The seasonal trends observed in the airborne in- dices and fluorescence and their relationship with gross primary production (GPP) demonstrated that vegetation indices mostly related to structure such as the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) yielded non-significant relationships (r 2 =0.17; p>0.05) due to the small structural changes in the canopy through the season. By contrast, physiological indices related to chlorophyll content (TCARI/OSAVI), light use efficiency (PRI 570 ), and canopy chlorophyll fluorescence calculated through the Fraun- hofer Line Depth principle (FLD3) showed a similar seasonal trend to that of GPP measured at the same time of the flights (r 2 in the range 0.75–0.84; p b 0.01). These results are consistent with the physiological trend observed during summer and autumn, which showed that chlorophyll content increased by 17.9% while the NDVI and the estimated tree crown projected LAI (LAI p ) remained almost constant during the experiment (3% variation). The time-series hyperspectral dataset demonstrated that the seasonal trajectories of the NDVI and EVI were weakly related (p>0.05) to the physiological indicators such as TCARI/OSAVI, PRI 570 and fluorescence. The spatial vari- ability of the hyperspectral indices investigated through the coefficient of variation (CV) showed that fluores- cence around the tower varied up to 17% at the time of the maximum stress (summer), while LAI p showed little variation during that time (CV = 1.8%). After the summer stress period, the CV for fluorescence and chloro- phyll content decreased in autumn down to 9%. This study demonstrates that small physiological changes occur- ring in an evergreen canopy were still captured by remote sensing physiological indices and high-resolution airborne fluorescence. These indicators are required for GPP monitoring when the vegetation dynamics are not captured by remote sensing structural indices. © 2013 Elsevier Inc. All rights reserved. 1. Introduction Remote sensing research methods proposed for global monitoring of vegetation dynamics rely on vegetation indices related to canopy structure. Such indices include the normalized difference vegetation index (NDVI) (Rouse et al., 1974) and other robust indices such as the enhanced vegetation index (EVI) (Huete et al., 2002), developed to avoid saturation at larger leaf area index (LAI) values and to be re- sistant to atmospheric effects. The NDVI, the EVI, and other vegetation indices mostly related to structure have been proposed in several studies as a proxy for LAI, canopy structure, green biomass, percent green cover, and fraction of absorbed photosynthetically active radia- tion (fAPAR) (Asrar et al., 1984; Baret & Guyot, 1991; Goward & Huemmrich, 1992; Huete et al., 2006; Jiang et al., 2008; Running & Nemani, 1988; Sellers, 1985). Certain models have identified these canopy parameters as being critical to scale up estimates of Remote Sensing of Environment 133 (2013) 102–115 ⁎ Corresponding author at: Instituto de Agricultura Sostenible (IAS), Consejo Superior de Investigaciones Científicas (CSIC), Alameda del Obispo, s/n, 14004 — Córdoba, Spain. Tel.: +34 957 499 280, +34 676 954 937; fax: +34 957 499 252. E-mail address: pablo.zarco@csic.es (P.J. Zarco-Tejada). URL: http://quantalab.ias.csic.es (P.J. Zarco-Tejada). 0034-4257/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2013.02.003 Contents lists available at SciVerse ScienceDirect Remote Sensing of Environment journal homepage: www.elsevier.com/locate/rse