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Thermal and Narrow-band Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle

AutorBerni, José A. J.; Zarco-Tejada, Pablo J. ; Suárez Barranco, María Dolores ; Fereres Castiel, Elías
Palabras claveMultispectral
Radiative transfer modeling
Remote sensing
Stress detection
Unmanned aerial system (UAS)
Unmanned aerial vehicles (UAVs)
Fecha de publicaciónmar-2009
EditorInstitute of Electrical and Electronics Engineers
CitaciónIEEE Transactions on Geoscience and Remote Sensing 47(3): 722-738 (2009)
ResumenTwo 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.
Descripción17 pages, 18 figures, 4 tables.
Versión del editorhttp://dx.doi.org/10.1109/TGRS.2008.2010457
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