English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/10236
Compartir / Impacto:
Estadísticas
Add this article to your Mendeley library MendeleyBASE
Citado 102 veces en Web of Knowledge®  |  Ver citas en Google académico
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar otros formatos: Exportar EndNote (RIS)Exportar bibText (RIS)Exportar csv (RIS)
Título

Temporal and Spatial Relationships between within-field Yield variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery

AutorZarco-Tejada, Pablo J. ; Whiting, M.; Ustin, S. L.
Fecha de publicación2005
EditorAmerican Society of Agronomy
CitaciónAgronomy Journal, Vol. 97, No. 3, 641-653
ResumenTraditional remote sensing methods for yield estimation rely on broadband vegetation indices, such as the Normalized Difference Vegetation Index, NDVI. Despite demonstrated relationships between such traditional indices and yield, NDVI saturates at larger leaf area index (LAI) values, and it is affected by soil background. We present results obtained with several new narrow-band hyperspectral indices calculated from the Airborne Visible and Near Infrared (AVNIR) hyperspectral sensor flown over a cotton (Gossypium hirsutum L.) field in California (USA) collected over an entire growing season at 1-m spatial resolution. Within-field variability of yield monitor spatial data collected during harvest was correlated with hyperspectral indices related to crop growth and canopy structure, chlorophyll concentration, and water content. The time-series of indices calculated from the imagery were assessed to understand within-field yield variability in cotton at different growth stages. A K means clustering method was used to perform field segmentation on hyperspectral indices in classes of low, medium, and high yield, and confusion matrices were used to calculate the kappa ({kappa}) coefficient and overall accuracy. Structural indices related to LAI [Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI)] obtained the best relationships with yield and field segmentation at early growth stages. Hyperspectral indices related to crop physiological status [Modified Chlorophyll Absorption Index (MCARI) and Transformed Chlorophyll Absorption Index (TCARI)] were superior at later growth stages, close to harvest. From confusion matrices and class analyses, the overall accuracy (and kappa) of RDVI at early stages was 61% ({kappa} = 0.39), dropping to 39% ({kappa} = 0.08) before harvest. The MCARI chlorophyll index remained sensitive to within-field yield variability at late preharvest stage, obtaining overall accuracy of 51% ({kappa} = 0.22).
Versión del editorhttp://dx.doi.org/10.2134/agronj2003.0257
URIhttp://hdl.handle.net/10261/10236
DOI10.2134/agronj2003.0257
ISSN1539-1582
Aparece en las colecciones: (IAS) Artículos
Ficheros en este ítem:
No hay ficheros asociados a este ítem.
Mostrar el registro completo
 



NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.