Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/10730
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Title: Thermal and Narrow-band Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle
Authors: Berni, José A. J., Zarco-Tejada, Pablo J., Suárez, L., Fereres Castiel, Elías
Keywords: Multispectral
Narrowband
Radiative transfer modeling
Remote sensing
Stress detection
Thermal
Unmanned aerial system (UAS)
Unmanned aerial vehicles (UAVs)
Issue Date: Mar-2009
Publisher: Institute of Electrical and Electronics Engineers
Abstract: Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5–13- $muhbox{m}$ region (40-cm resolution) and narrowband multispectral imagery in the 400–800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content $(C_{rm ab})$ , and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by traditional mann- - ed airborne sensors.
Description: 17 pages, 18 figures, 4 tables.
Publisher version (URL): http://dx.doi.org/10.1109/TGRS.2008.2010457
URI: http://hdl.handle.net/10261/10730
ISSN: 0196-2892
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Citation: IEEE Transactions on Geoscience and Remote Sensing 47(3): 722-738 (2009)
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