Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures

The increasing availability of high-quality sensors and computational power on low-cost mobile devices like smartphones and tablets is opening new possibilities for adopting this kind of technology for monitoring biophysical processes of interest for agronomic and environmental studies. A method for leaf area index (LAI) estimates based on gap fraction, derived from the segmentation of images acquired at 57 below the canopy, was recently proposed and implemented in the smartphone app PocketLAl (R), and successfully tested against commercial devices for paddy rice. In this study, PocketLAI was tested against the AccuPAR ceptometer on canopy structures (maize, row-seeded giant reed and natural grassland) that strongly deviate from the ideal assumption behind the simplified model for light transmittance into the canopy used in the app (i.e., random distribution of infinitely small leaves). The comparison between PocketLAI and AccuPAR showed overall good performances for the app, with root mean square error of 0.41, 0.49 and 0.96 m(2) m(-2) for grassland, maize and giant reed respectively, and R-2 of 0.86, 0.92 and 0.88. A saturation effect was observed for PocketLAI for LAI values higher than 5 m2 m-2 especially for giant reed, with the LAI values obtained with the app markedly underestimating those provided by AccuPAR. Although further studies are needed to better investigate the need for calibrating the app in case of low-quality devices, these results confirm the possible role of PocketLAI in providing a suitable alternative to the commercial tools available for indirect LAI estimates in contexts characterized by few economic resources or when a high portability is needed. (C) 2013 Elsevier B.V. All rights reserved.