such as "Introduction", "Conclusion"..etc
Hyperspectral field reflectance measurements to estimate wheat grain yield and plant height
IUniversidade Federal do Espírito Santo - UFES, C.P. 16 - 29500-000 - Alegre, ES - Brasil IIInstituto Nacional de Pesquisas Espaciais - INPE, C.P. 515 - 12201-970 - São José dos Campos, SP - Brasil IIIInstituto Agronômico Campinas - IAC, C.P. 28 - 13020-902 - Campinas, SP - Brasil
Hyperspectral crop reflectance data are useful for several remote sensing applications in agriculture, but there is still a need for studies to define optimal wavebands to estimate crop biophysical parameters. The objective of this work is to analyze the use of narrow and broad band vegetation indices (VI) derived from hyperspectral field reflectance measurements to estimate wheat (Triticum aestivum L.) grain yield and plant height. A field study was conducted during the winter growing season of 2003 in Campinas, São Paulo State, Brazil. Field canopy reflectance measurements were acquired at six wheat growth stages over 80 plots with four wheat cultivars (IAC-362, IAC-364, IAC-370, and IAC-373), five levels of nitrogen fertilizer (0, 30, 60, 90, and 120 kg of N ha-1) and four replicates. The following VI were analyzed: a) hyperspectral or narrow-band VI (1. optimum multiple narrow-band reflectance, OMNBR; 2. narrow-band normalized difference vegetation index, NB_NDVI; 3. first- and second-order derivative of reflectance; and 4. four derivative green vegetation index); and b) broad band VI (simple ratio, SR; normalized difference vegetation index, NDVI; and soil-adjusted vegetation index, SAVI). Hyperspectral indices provided an overall better estimate of biophysical variables when compared to broad band VI. The OMNBR with four bands presented the highest R2 values to estimate both grain yield (R2 = 0.74; Booting and Heading stages) and plant height (R2 = 0.68; Heading stage). Best results to estimate biophysical variables were observed for spectral measurements acquired between Tillering II and Heading stages.
Key words: remote sensing, agriculture, vegetation indices
Sci. agric. (Piracicaba, Braz.) vol.63 no.2 Piracicaba Mar./Apr. 2006.
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