Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 5; p. 824
• Main Authors: Naser, Mohammed, Khosla, Raj, Longchamps, Louis, Dahal, Subash
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.03.2020
• Subjects: dryland; genotypes; irrigated; k-means clustering; ndvi; proximal sensor
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12050824

Crop breeders are looking for tools to facilitate the screening of genotypes in field trials. Remote sensing-based indices such as normalized difference vegetative index (NDVI) are sensitive to biomass and nitrogen (N) variability in crop canopies. The objectives of this study were (i) to determine if proximal sensor-based NDVI readings can differentiate the yield of winter wheat (Triticum aestivum L.) genotypes and (ii) to determine if NDVI readings can be used to classify wheat genotypes into grain yield productivity classes. This study was conducted in northeastern Colorado in 2010 and 2011. The NDVI readings were acquired weekly from March to June, during 2010 and 2011. The correlation between NDVI and grain yield was determined using Pearson’s product-moment correlation coefficient (r). The k-means clustering method was used to classify mean NDVI and mean grain yield into three classes. The overall accuracy between NDVI and yield classes was reported. The findings of this study show that, under dryland conditions, there is a reliable correlation between grain yield and NDVI at the early growing season, at the anthesis growth stage, and the mid-grain filling growth stage, as well as a poor association under irrigated conditions. Our results suggest that when the sensor is not saturated, i.e., NDVI < 0.9, NDVI could assess grain yield with fair accuracy. This study demonstrated the potential of using NDVI readings as a tool to differentiate and identify superior wheat genotypes.