Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts

Abstract

Abstract. We tested the capacity of several published multispectral indices to estimate the nitrogen nutrition of wheat canopies grown under different levels of water supply and plant density and derived a simple canopy reflectance index that is greatly independent of those factors. Planar domain geometry was used to account for mixed signals from the canopy and soil when the ground cover was low. A nitrogen stress index was developed, which adjusts shoot %N for plant biomass and area, thereby accounting for environmental conditions that affect growth, such as crop water status. The canopy chlorophyll content index (CCCi) and the modified spectral ratio planar index (mSR Pi ) could explain 68 and 69% of the observed variability in the nitrogen nutrition of the crop as early as Zadoks 33, irrespective of water status or ground cover. The CCCi was derived from the combination of 3 wavebands 670, 720 and 790 nm, and the mSR Pi from 445, 705 and 750 nm, together with broader bands in the NIR and RED. The potential for their spatial application over large fields/paddocks is discussed.Additional keywords: water stress, plant density, remote sensing, nitrogen stress index.

Airborne Hyperspectral Images and Ground-Level Optical Sensors As Assessment Tools for Maize Nitrogen Fertilization

“…In the absence of other limiting factors, most literature has shown that ratios between red-edge and near-infrared reflectance provide the best correlation with leaf N concentration [35,37]. To detect N deficiency in wheat under different water status, a two-dimensional reflectance-based index combining an indicator of plant cover (NDVI), and an indicator of Chl content has been proposed [38]. However, in our study, a combination of two narrow-band indices did not improve yield prediction.…”

Testing the spectral resolutions of the new multispectral sensors for detecting <i>Phaeosphaeria</i> leaf spot (PLS) infestations in maize crop

“…The Yellow band's ability to determine early PLS infestation can be attributed to its ability to identify "yellowness'', a common characteristic in diseased plants (Digital Globe, 2010;Oliveira et al, 2010 ). The Red-Edge band has the ability to assess plant nutrition, health and discriminate vegetation species (Filella and Peñuelas, 1994;Pinar and Curran, 1996;Daughtry et al, 2000;Rodriguez et al, 2006). According to Eitel et al (2011), the Red-Edge band can determine both the amount of chlorophyll and Nitrogen, the main determinants of the crop health.…”

Nitrogen concentration estimation with hyperspectral LiDAR

“…NDRE was presented by Barnes et al (2000) to be used as a vegetation stress indicator with Canopy Chlorophyll Content Index (CCCI). NDRE is designed to respond to changes in chlorophyll concentration and nitrogen content (Rodriguez et al, 2006;Tilling et al, 2007). The equation of NDRE is:…”