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ARS Home » Plains Area » Lincoln, Nebraska » Agroecosystem Management Research » Research » Publications at this Location » Publication #207052
Title: Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress

Author
item HONG, S - KOREA
item SCHEPERS, JAMES
item FRANCIS, DENNIS
item SCHLEMMER, MICHAEL

Submitted to: Communications in Soil Science and Plant Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/1/2007
Publication Date: 9/5/2007
Citation: Hong, S., Schepers, J.S., Francis, D.D., Schlemmer, M.R. 2007. Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Communications in Soil Science and Plant Analysis 38:2209-2226.

Interpretive Summary: Monitoring crop vigor and growth is difficult and subjective unless great care and time is taken in making measurements. Recent advances in sensors can provide an estimate of plant growth parameters like chlorophyll content, leaf area index, biomass, and nitrogen status. A greenhouse study was conducted using corn at various growth stages that was grown under various levels of nitrogen deficiency. A combination of research grade spectroradiometers, hand-held chlorophyll meters, and field-grade reflectance sensors were compared. Sensor results were compared to leaf chlorophyll concentration and dry matter production (biomass). Background reflectance from soil can be a problem for sensors that measure reflectance. Likewise, sensors that do not have a foot-print that is compatible with the canopy structure (i.e., field-of-view vs. target) can have problems. Vegetation indices derived from the ground-based remote sensors provided a reliable, non-destructive, real-time assessment of plant N status and biomass up to about the 8-leaf growth stage of corn. After that time, the sensors could not penetrate deep enough into the canopy to detect how much biomass was present. Sensors should to be a useful tool for in-season crop N management providing both spatial and temporal information to users.

Technical Abstract: The non-destructive determination of plant biomass is not possible; however crop canopy sensors that determine the normalized difference vegetation index (NDVI) have the potential to estimate living biomass. Pot experiments using sand culture were conducted in 2002 and 2003 under greenhouse conditions to evaluate the effect of nitrogen (N) deficiency on corn biomass and reflectance. Nitrogen stress was imposed by implementing 8 levels (50% to 120%) of N in Hoagland’s nutrient solution in 2002 and 6 levels (40% to 140%) in 2003. Canopy reflectance measurements were made with hand held spectral sensors including an active red GreenSeekerTM (Ntech Industries) and passive sensors including Crop CircleTM (Holland Scientific), and Field ScoutTM Chlorophyll meter (CM1000, Spectrum Technologies, Inc.), and spectroradiometers (ASD Inc. and Ocean Optics models S2000 and USB2000) as well as Minolta SPAD-502 chlorophyll meter. Canopy reflectance and dry weight of corn were measured at three growth stages from V6 to flowering for the 2002 and 2003 experiments, respectively. Dry weight of corn affected by nitrogen stress showed large differences between maximum and minimum values at the flowering stage, ranging from 24.1 to 95.5 g plant-1 for 2002 and from 8.3 to 101.7 g plant-1 for 2003. Several reflectance indexes obtained from GreenSeeker, Crop Circle, and spectroradiometers including chlorophyll readings were compared for evaluation of corn biomass. The NDVI and GNDVI reflectance indexes by the Crop Circle sensor were the best tool for assessing differences in dry weight of corn at every growth stage evaluated. Especially, the NDVI and GNDVI at the critical V6 and V7~8 growth stages were closely correlated with dry weight of corn at flowering, meaning that these reflectance indexes could be used for in-season N management decisions when using real-time crop sensors. Ground-based remote sensing provided a non-destructive real-time assessment of plant N status and should to be a useful tool for in-season crop N management providing both spatial and temporal information.