Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #344986

Research Project: Improving Agroecosystem Services by Measuring, Modeling, and Assessing Conservation Practices

Location: Hydrology and Remote Sensing Laboratory

Title: Assessing UAS mounted imaging sensors for the evaluation of Zea mays nitrogen status.

item Russ, Andrew - Andy
item Daughtry, Craig
item Hunt Jr, Earle

Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 8/31/2017
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Improved efficiency of Nitrogen (N) fertilizer applications is an important environmental and economic issue for the agricultural community. Considerable research for improving Nitrogen Use Efficiency (NUE) has focused on optimal timing and rate N applications. Remote sensing techniques can detect the onset and spatial variability of N stress, but traditional sources of remotely sensed imagery (satellites and manned-aircraft) are often not available at appropriate times or spatial resolutions. A new generation of compact multispectral imaging sensors suited for unmanned aircraft systems (UAS) has opened new possibilities for timely acquisitions of crop imagery at high spatial resolutions. An experiment was conducted at the Beltsville Agricultural Research Service in Maryland with a range of N application rates, irrigated vs rain fed treatments, and plant populations that induced biophysical variability in the corn canopies. Biophysical and remote sensing measurements were conducted at multiple growth stages. The spatial resolutions of the multispectral imagery ranged from a few centimeters to a few decimeters depending on altitude of the flights. Pixels with background, glare and shadow pixels were identified. Removing these sources of “noise” could provide more reliable assessments of spatial variability of crop N requirements than is possible with conventional remote sensed images.