Expression of Variability in Corn as Influenced by Growth Stage Using Optical Sensor Measurements

Improving crop management inputs with remote sensing devices is an emerging technology. This study documented the progression of the normalized difference vegetative index (NDVI) during the life cycle of corn (Zea mays L.), evaluated the spatial variability of corn growth in terms of the CV (calculated from NDVI readings), and documented the relationships between NDVI, CV (calculated fromNDVI), and grain and biomass yields and plant density. Four rows, 30 m in length, from two locations during 2 yr were randomly selected for this study. An optical sensor was used to collect NDVI readings at multiple growth stages during the life cycle of corn. The NDVI increased with progression of vegetative growth stages until V10, where a plateau was encountered, followed by a decline in NDVI after the VT growth stage. Coefficient of variation data from the NDVI readings revealed two dominant peaks during the life cycle of corn, one between the V6 and V8 growth stages and the second during the late reproductive growth stages. The CV data illustrated that the greatest variation expressed by corn during the vegetative growth stages was between the V6 and V8 growth stages. The highest correlation of NDVI with corn grain yield was found at the V7 to V9 growth stages; likewise, CV and plant density were also more highly correlated from V7 to V9. The CV from NDVI readings was highly correlated with grain and biomass yields at all growth stages. REMOTE SENSING TECHNOLOGY is increasing in popularity in agricultural settings. Today, there is a wide variety of uses for remote sensors and nearly all applications continue to undergo updates and revisions to improve their effectiveness.Alongwith themany sensors, there are also many different indices produced by these various sensors. For this study, our focus was on better understanding the NDVI in corn production. Vegetation indices such asNDVI have a direct relationship with crop canopy attributes such as biomass and grain yield (Ashcroft et al., 1990; Anderson and Hanson, 1992). Singlewavelengths in the510to760-nmrangeor spectral vegetation indices such as the green simple ratio index (GSRI) and green normalized difference vegetative index (GNDVI) were proposed as predictors of corn grain yield (Elwadie et al., 2005). The normalized difference vegetative index is an index developed from two important wave bands: the red and near infrared. According to Knipling (1970) these two bands have unique features. Reflected red radiation is known to negatively correlate with green leaf area, whereas reflected nearinfrared radiation is known to positively correlate with leaf area (Knipling, 1970). The index is given by NDVI 5 rNIR 2 rRed rNIR 1 rRed where rNIR is the fraction of emitted near infrared (NIR) radiation returned from the sensed area (reflectance) and rRed is the fraction of emitted red radiation returned from the sensed area (reflectance). In a study designed to compare different vegetation indices as a means of assessing canopy variation and its impact on corn yield, Shanahan et al. (2001) showed that GNDVI values derived from images acquired during midgrain filling were the most highly correlated with grain yield. According to Gilbert et al. (1996), a significant logarithmic (r 5 0.96, P , 0.01) relationship was observed between NDVI and biomass where, after canopy closure, the biomass continued to increase after NDVI reached its maximumvalue.Astudy thatcomparedNfertilizer recommendation based on NDVI and corn yield measurements showed that, at the V8 to V9 growth stage, the remotesensing-based N recommendation model was more accurate than models based on yield (Clay et al., 2006). An additional benefit that sensor technology adds to production agriculture is the ability to quantitatively identify variability within a field. When considering the spatial resolution of photographic images, Simonett (1983) stated that spatial resolution may be defined as the minimum distance between two objects that a sensor can record distinctly. Independent of resolution or amount of variability, many studies have shown that variability has a negative impact on yield (Krall et al., 1977; Nielsen, 2001; Nafziger et al., 1991; Raun et al., 1986; Raun and Johnson, 1999). TheCVis a statistical parameter that can easily be used to calculate the variability in NDVImeasurements taken across a given area. Freund and Wilson (2003) defined CV as the ratio of the standard deviation to the mean, expressed in percentage terms, or simply the standard deviation as a proportion of the mean. Washmon et al. (2002) used CV (calculated from Landsat satellite images) to evaluate variability in wheat (Triticum aestivum L.) and indicated that if within-field CVs could be predicted, yield estimations should improve. This study documented the progression of NDVI and CVwith time, which is similar to that found byRaun et al. (2005). The study conducted by Raun et al. (2005) took place in Texcoco, Mexico. The difference in climate, crop K.L. Martin, K. Girma, K.W. Freeman, R.K. Teal, B. Tubańa, D.B. Arnall, B. Chung, O. Walsh, and W.R. Raun, Dep. of Plant and Soil Sciences; and J.B. Solie and M.L. Stone, Dep. of Biosystems and Agricultural Engineering, Oklahoma State Univ., Stillwater, OK 74078. Contribution from the Oklahoma Agric. Exp. Stn. Mention of trademarked instruments does not imply endorsement. Received 22 Sept. 2005. *Corresponding author ([email protected]). Published in Agron. J. 99:384–389 (2007). Remote Sensing doi:10.2134/agronj2005.0268 a American Society of Agronomy 677 S. Segoe Rd., Madison, WI 53711 USA Abbreviations: NDVI, normalized difference vegetative index; NIR, near infrared. R e p ro d u c e d fr o m A g ro n o m y J o u rn a l. P u b lis h e d b y A m e ri c a n S o c ie ty o f A g ro n o m y . A ll c o p y ri g h ts re s e rv e d . 384 Published online February 6, 2007