Row orientation and viewing geometry effects on row-structured vine crops for chlorophyll content estimation

Abstract

Methods for chlorophyll a + b (Cab) estimation in row-structured crops that account for row orientation and sun geometry are presented in this research. Airborne campaigns provided imagery over a total of 72 study sites from 14 Vitis vinifera L. fields with the Compact Airborne Spectrographic Imager (CASI) hyperspectral sensor in different sun geometries and a wide range of row orientations. Two different CASI acquisition modes were used, comprising 1 and 4 m spatial resolutions with 8 and 72 bands, respectively, in the visible and near-infrared spectral regions. Airborne campaigns were acquired over the same sites in the morning and in the afternoon to assess the bidirectional reflectance distribution function (BRDF) effects on the imagery owing to the different fractions of shadow as a function of the sun viewing geometries and the row orientation. Narrow-band indices sensitive to chlorophyll content (TCARI/OSAVI (transformed chlorophyll absorption in reflectance index / optimized soil-adjusted vegetation index)) and canopy structure (normalized difference vegetation index (NDVI)) were calculated from the CASI imagery. The effects on the canopy reflectance of different sun viewing geometries and row orientation were studied through a modelling approach. The validity of narrow-band indices for Cab content estimation at the canopy level was assessed using an upscaling approach with the Markov-chain canopy reflectance model (MCRM), with additions to simulate the row crop structure (rowMCRM) to account for the effects of vineyard structure, vine dimensions, row orientation, and soil and shadow effects on the canopy reflectance. Several predictive algorithms were tested in this study to explore the importance of row orientation and viewing geometry of row-structured crops. New predictive relationships were developed with the rowMCRM model between Cab and TCARI/OSAVI as a function of structural properties of the canopy, taking into account diurnal variations in the viewing geometry and row orientation. One of these new predictive algorithms for Cab content estimation, valid for a typical range of viewing geometries and row orientations, was successfully applied to the 72 study areas, yielding an RMSE in leaf chlorophyll content estimation of 10.2 and 10.6 µg·cm−2 for morning and afternoon sun geometries, respectively.