Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China

Abstract

Remote sensing of rice traits has advanced significantly with regard to the capacity to retrieve useful plant biochemical, physiological and structural quantities across spatial scales. The rice leaf NDVI (normalized difference vegetation index) has been developed and applied in monitoring rice growth, yield prediction and disease status to guide agricultural management practices. This study combined rice canopy NDVI and environmental data to estimate rice leaf NDVI. The test site was a japonica rice experiment located in the eastern city of Shenyang, Liaoning Province, China. This paper describes (1) the use of multiple linear regression to establish four periods of rice leaf NDVI models with good accuracy (R2=0.782–0.903), and (2) how the key point of the rice growth period based on these models was determined. The techniques for modeling leaf NDVI at the point of remote canopy sensing were also presented. The results indicate that the rice leaf NDVI has a high correlation with the canopy NDVI and multisource environmental data. This research can provide an efficient method to detect rice leaf growth at the canopy scale in the future. Keywords: japonica rice, NDVI, leaf models, canopy scale, environmental data DOI: 10.3965/j.ijabe.20160905.2266 Citation: Yu F H, Xu T Y, Cao Y L, Yang G J, Du W, Wang S. Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. Int J Agric & Biol Eng, 2016; 9(5): 132－142.