Assessing canopy PRI from airborne imagery to map water stress in maize M. Rossini a,⇑ , F. Fava a,b , S. Cogliati a , M. Meroni a,c , A. Marchesi a,d , C. Panigada a , C. Giardino e , L. Busetto a , M. Migliavacca a,f , S. Amaducci g , R. Colombo a a Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, P.zza della Scienza 1, 20126 Milano, Italy b Nucleo Ricerca Desertificazione, Centro Interdipartimentale di Ateneo – NRD, University of Sassari, Sassari, Italy c European Commission, DG-JRC, Institute for Environment and Sustainability, Monitoring Agricultural Resources Unit, Ispra, VA, Italy d Building Environment Sciences and Technology Department (BEST), Politecnico di Milano University, Milano, Italy e Optical Remote Sensing Group, CNR-IREA, Milano, Italy f Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany g Istituto di Agronomia Generale e Coltivazioni Erbacee, Università Cattolica del Sacro Cuore, Piacenza, Italy article info Article history: Received 15 October 2012 Received in revised form 20 September 2013 Accepted 10 October 2013 Keywords: Hyperspectral Vegetation Monitoring Aerial Crop abstract This paper presents a method for mapping water stress in a maize field using hyperspectral remote sens- ing imagery. An airborne survey using AISA (Specim, Finland) was performed in July 2008 over an exper- imental farm in Italy. Hyperspectral data were acquired over a maize field with three different irrigation regimes. An intensive field campaign was also conducted concurrently with imagery acquisition to mea- sure relative leaf water content (RWC), active chlorophyll fluorescence (DF/F 0 m ), leaf temperature (T l ) and Leaf Area Index (LAI). The analysis of the field data showed that at the time of the airborne overpass the maize plots with irrigation deficits were experiencing a moderate water stress, affecting the plant phys- iological status (DF/F 0 m , difference between T l and air temperature (T air ), and RWC) but not the canopy structure (LAI). Among the different Vegetation Indices (VIs) computed from the airborne imagery the Photochemical Reflectance Index computed using the reflectance at 570 nm as the reference band (PRI 570 ) showed the strongest relationships with DF/F 0 m (r 2 = 0.76), T l  T air (r 2 = 0.82) and RWC (r 2 = 0.64) and the red-edge Chlorophyll Index (CI red-edge ) with LAI (r 2 = 0.64). Thus PRI has been proven to be related to water stress at early stages, before structural changes occurred. A method based on an ordinal logit regression model was proposed to map water stress classes based on airborne hyperspectral imagery. PRI 570 showed the highest performances when fitted against water stress classes, identified by the irrigation amounts applied in the field, and was therefore used to map water stress in the maize field. This study proves the feasibility of mapping stress classes using hyper- spectral indices and demonstrates the potential applicability of remote sensing data in precision agricul- ture for optimizing irrigation management. Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. 1. Introduction In modern management of agricultural resources, there is an increasing interest in the development of systems to balance crop production with energy supply and sustainability of the farming system in order to increase the productivity, optimize the profit- ability, and protect the environment. The assessment of water sta- tus in crops is particularly important for precision irrigation practices, since water deficit stress is considered one of the main factors limiting photosynthesis and plant primary productivity (Boyer, 1982; Chaves et al., 2003). Maize (Zea mays, L.) is one of the major crops throughout the world (Leff et al., 2004), and it is the main crop in the Po Plain (Northern Italy), the largest and most intensive agricultural area in Italy. Maize has high irrigation requirements (Musick and Dusek, 1980). The decrease in summer rainfall predicted in the Mediterranean region (IPCC, 2007) may enhance water stress for this crop and limit its productivity. In this context, the development of methods for accurate irrigation sched- uling and control aimed to achieve an optimum water supply for productivity and to maximize the water-use efficiency has an increasing importance (Li et al., 2009). Remote sensing has high potential in providing timely informa- tion on crop conditions during the growing season over large areas, 0924-2716/$ - see front matter Ó 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.isprsjprs.2013.10.002 ⇑ Corresponding author. Tel.: +39 0264482848; fax: +39 0264482895. E-mail address: micol.rossini@unimib.it (M. Rossini). ISPRS Journal of Photogrammetry and Remote Sensing 86 (2013) 168–177 Contents lists available at ScienceDirect ISPRS Journal of Photogrammetry and Remote Sensing journal homepage: www.elsevier.com/locate/isprsjprs