Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat

A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. Field experiments were conducted on a typic Argiaquoll at the Purdue Agronomy...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Remote sensing of environment 1986-02, Vol.19 (1), p.47-61
Hauptverfasser: Hinzman, L.D., Bauer, M.E., Daughtry, C.S.T.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A valuable input to crop growth and yield models would be estimates of current crop condition. If multispectral reflectance indicates crop condition, then remote sensing may provide an additional tool for crop assessment. Field experiments were conducted on a typic Argiaquoll at the Purdue Agronomy Farm, West Lafayette, IN to determine the effects of nitrogen fertilization on the spectral reflectance and agronomic characteristics of winter wheat ( Triticum aestivum L). The fertilization treatments consisted of 0, 60, and 120 kg N/ha, applied as urea in the spring. Spectral reflectance was measured 11 times during the 1979 growing season and 10 times during the 1980 growing season with a spectroradiometer (Exotech 20C) in the 400–2400 nm wavelength region. Agronomic data included total leaf N concentration, leaf chlorophyll concentration, stage of development, leaf area index, plant moisture, and fresh and dry phytomass. Relationships between spectral and agronomic variables were developed using data from 1979 and tested with data from 1980. N fertilization of wheat reduced visible, increased near infrared, and deceased middle infrared reflectance. These changes were related to lower levels of chlorophyll and reduced leaf area in the nonfertilized plots. Green LAI, an important descriptor of wheat canopies, could be reliably estimated with multispectral data. This study demonstrated that N-stressed wheat could be distinguished from healthy wheat spectrally and, therefore, that multispectral imagery may be useful for monitoring crop condition.
ISSN:0034-4257
1879-0704
DOI:10.1016/0034-4257(86)90040-4