320-4 Effects of Water Status on the Detection of Maize Nitrogen Status with Remote Sensing.
Poster Number 1231
See more from this Division: SSSA Division: Soil Fertility & Plant NutritionSee more from this Session: Fertilizer Decision Support Tools/Systems for Sustainable Agriculture and Environment
Tuesday, November 4, 2014
Long Beach Convention Center, Exhibit Hall ABC
Improving the efficiency of Nitrogen (N) fertilizer applications is an issue of importance for the agricultural community. The environmental and
economic considerations of optimizing N input to yield ratios warrant research in topics including optimal timing of fertilization, effectiveness of variable rate
applications for improving Nitrogen Use Efficiency (NUE), and capabilities of remote sensing techniques to detect the onset and spatial variability of N stress.
A multi-year assessment of the efficacy of spectral remote sensing derived vegetation indices (VIs) to discern N stress in corn has been conducted in Maryland
from 2004 to 2014. Satellite multispectral imagery, aerial hyperspectral imagery, ground based passive sensor reflectance data, and ground based active sensor
reflectance data were collected. Biophysical parameters including plant height, leaf area index (LAI), leaf chlorophyll content, and development stage were acquired
weekly from growth stages V6 to R1. Nitrogen treatment plots were divided into rain fed and irrigation supplemented treatments to investigate the effects of water status
upon crop chlorophyll levels and the ability to discern them with remote sensing techniques.
VIs derived from spectral remote sensing are affected by the water status of the crop. Changes in water status through the growing season and even intraday can change
the apparent biomass (LAI) visible to the sensor. Therefore it is preferabe to seek out VIs which minimize responses to biomass and maximize responses to crop chlorophyll
content.
See more from this Division: SSSA Division: Soil Fertility & Plant NutritionSee more from this Session: Fertilizer Decision Support Tools/Systems for Sustainable Agriculture and Environment