Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10 nm bandwidth and 150 mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0. 03 (visible region) and below 0. 05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0. 08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0. 09 for the scaling-up approach, and below 0. 10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes. © 2012 Springer Science+Business Media, LLC.Financial support from the Spanish Ministry of Science and Innovation (MCI) for the projects AGL2009-13105, CONSOLIDER CSD2006-67, and AGL2003-01468 is gratefully acknowledged, as well as the Junta de Andalucía-Excelencia AGR-595 and FEDER. M.L. Guillén-Climent was supported by a grant JAE of CSIC, co-funded by the European Social Fund.Peer Reviewe

Beyond NDVI: Extraction of biophysical variables from remote sensing imagery

This chapter provides an overview of methods used for the extraction of biophysical vegetation variables from remote sensing imagery. It starts with the description of the main spectral regions in the optical window of the electromagnetic spectrum based on typical spectral signatures of land surfaces. Subsequently, the merit and problems of using radiative transfer models to describe the relationship between spectral measurements and biophysical and chemical variables of vegetation are described. Next, the use of statistical methods by means of vegetation indices for the same purpose gets attention. An overview of different types of indices is given without having the ambition in being exhaustive. Subsequently, an overview is provided of the biogeophysical vegetation variables that can directly be estimated from optical remote sensing observations, with emphasis on using vegetation indices. These vegetation variables are: (1) chlorophyll and nitrogen, (2) vegetation cover fraction and fAPAR, (3) leaf area index, and (4) canopy water. Finally, an outlook for a major research direction in the near future in this context is provided