Submitted to: Crop Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 12/1/2011
Publication Date: 3/1/2012
Citation: Gutierrez, M., Norton, R., Thorp, K.R., Wang, G. 2012. Association of spectral reflectance indices with plant growth and lint yield in upland cotton (Gossypium hirsutum L.). Crop Science. 52:849-857. Interpretive Summary: Remote sensing technology can be implemented as a diagnostic tool to monitor growth and development of crops and to predict yield. Modern remote sensing devices are 'hyperspectral' in nature, offering hundreds or thousands of discrete crop canopy reflectance measurements in narrow wavebands. A remaining problem is to understand how this information can be utilized to better predict crop yield or nitrogen status and to optimize management of water and nutrients accordingly. In this study, a variety of reflectance indices were compared for their ability to estimate cotton biomass, leaf area index, and lint yield. Results demonstrated that three reflectance indices were better able to predict cotton canopy characteristics than the widely-used normalized difference vegetation index (NDVI). The study advances our understanding of how to use reflectance measurements in discrete, narrow waveband to monitor crop growth and predict yield. Results are useful to researchers who are developing methodologies for using remote sensing data to optimize agricultural production while reducing environmental impacts of agricultural practices.
Technical Abstract: Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices and cotton biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices [normalized difference vegetation index (NDVI); simple ratio (SR); near infrared index (NIR); and ratio vegetation index (RVI)] were determined at different cotton growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87% variation in cotton biomass (all R2>0.87) and LAI (R2>0.93). SR, NIR, and RVI all had higher coefficients of determination (R2) compared to NDVI because the former three indices were not saturated at late growth stages. There was not a significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom stage showed significant correlations with lint yield. SR, NIR, and RVI explained 56%, 60%, and 58% of variations in cotton lint yield, respectively, while NDVI only explained 47% of variations in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.