Nicolas Tremblay

TL;DR: In this paper, a combined modeling and indices-based approach is presented to predict the crop chlorophyll content from remote sensing data while minimizing LAI (vegetation parameter) influence and underlying soil background effects.

TL;DR: In this article, the authors proposed a new spectral index to estimate plant nitrogen (N) concentration, which is a critical component of NNI calculation, using hyperspectral reflectance data collected using a ground-based spectroradiometer on corn and wheat crops at different growth stages from 2005 to 2008.

TL;DR: In this paper, a review of near-term operational sensors, handheld and tractor-mounted, for proximal remote measurements for N status determination is presented, where the authors focus on the relationship between sensor readings and optimal N rates, such as critical NSI (nitrogen sufficiency index), INSEY (in-season estimated yield), and sensor-determined CI (chlorophyll index).

TL;DR: In this article, the authors present the application of fluorescence induced by le rayonnement ultraviolet peuvent etre consideres comme des signatures complexes which renseignent sur l'etat physiologique de la plante, including photosynthese, reactions primaires photochimiques et la teneur en chlorophylle.

TL;DR: Field spectral measurements collected over corn and wheat canopies in different intensive field campaigns organized during the growing seasons of 2004 and 2005 were used to test and evaluate several combined indices for chlorophyll determination using hyperspectral imagery (Compact Airborne Spectrographic Imager).