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United States Department of Agriculture

Agricultural Research Service

Research Project: Pesticide Application Technologies for Spray-drift Management, Maximizing In-field Deposition, and Targeted Spraying

Location: Crop Production Systems Research Unit

Title: Multispectral Imaging Systems for Airborne Remote Sensing to Support Site-Specific Agricultural Management

Authors
item Huang, Yanbo
item Thomson, Steven
item Lan, Yubin
item Maas, Stephan

Submitted to: Asian Conference on Precision Agriculture
Publication Type: Proceedings
Publication Acceptance Date: August 15, 2009
Publication Date: October 14, 2009
Citation: Huang, Y., Thomson, S.J., Lan, Y., Maas, S. 2009. Multispectral Imaging Systems for Airborne Remote Sensing to Support Site-Specific Agricultural Management. Proceedings of 3rd Asian Conference on Precision Agriculture.

Interpretive Summary: Remote sensing imaging systems on agricultural aircraft are a powerful tool for site-specific crop management. Agricultural aircraft carrying remote sensing imaging systems is flexible in setting up for obtaining high quality, high resolution imagery at desired spectral bands, target field location, specific altitude, and time. Multispectral imaging systems, which may cover the wave bands from visible (blue, green, red), near infrared, to thermal infrared, are cost-effective, not data-intensive, and a good source providing data and information of crops, soil, and ground cover for agricultural application and production. For practical applications of aerial multispectral remote sensing imaging system, this work investigates three representative multispectral imaging systems in supporting site-specific crop management. The three systems are from low-cost to relatively high-cost. They can be operated manually or automated. They are either integrated sensor systems or separated camera systems (each for a band). In the investigation, practical issues are described and discussed. The advantages and disadvantages of each system are summarized and compared. The information provided from this study will benefit for developing practical aerial remote sensing systems on fixed-wing aircraft or unmanned autonomous helicopters.

Technical Abstract: Remote sensing has shown promise as a tool for site-specific management in agricultural application and production. Earth-observing satellite systems have an advantage for large-scale analysis at regional levels but are limited in spatial resolution. High-resolution satellite systems have been available in recent years, but scheduling these systems for appropriate bands, location of flight, proper altitude, and time of acquisition is difficult. Airborne remote sensing systems offer a flexible, do-it-yourself platform to configure for high quality, high spatial resolution imagery at any desired spectral combination, location, altitude, and time. Use of airborne hyperspectral remote sensing in agriculture has been steadily increasing during the past decade. Compared to hyperspectral systems, multispectral systems are much lower in cost and are less data-intensive. Airborne multispectral techniques are cost-effective and still a good source of crop, soil, or ground cover information for agricultural application and production. This paper investigates three different types of multispectral imaging systems for airborne remote sensing to support site-specific management in agricultural application and production. The three systems have been used in agricultural studies. They range from low-cost to relatively high-cost, manually operated to automated, multispectral composite imaging with a single camera and individual imaging with assembly of separate cameras. With the investigation, practical issues are described and discussed. The advantages and disadvantages of each system are summarized and compared in different configurations. The information is provided for developing practical aerial remote sensing systems. System applications are suitable for fixed-wing aircraft and unmanned autonomous helicopter platforms.

Last Modified: 12/19/2014
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