Enhancing the Nitrogen Signals of Rice Canopies across Critical Growth Stages through the Integration of Textural and Spectral Information from Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Hengbiao Zheng,Yan Zhu,Jifeng Ma,Weixing Cao,Tao Cheng,Meng Zhou,Xia Yao,Dong Li

doi:10.3390/rs12060957

Abstract

This paper evaluates the potential of integrating textural and spectral information from unmanned aerial vehicle (UAV)-based multispectral imagery for improving the quantification of nitrogen (N) status in rice crops. Vegetation indices (VIs), normalized difference texture indices (NDTIs), and their combination were used to estimate four N nutrition parameters leaf nitrogen concentration (LNC), leaf nitrogen accumulation (LNA), plant nitrogen concentration (PNC), and plant nitrogen accumulation (PNA). Results demonstrated that the normalized difference red-edge index (NDRE) performed best in estimating the N nutrition parameters among all the VI candidates. The optimal texture indices had comparable performance in N nutrition parameters estimation as compared to NDRE. Significant improvement for all N nutrition parameters could be obtained by integrating VIs with NDTIs using multiple linear regression. While tested across years and growth stages, the multivariate models also exhibited satisfactory estimation accuracy. For texture analysis, texture metrics calculated in the direction D3 (perpendicular to the row orientation) are recommended for monitoring row-planted crops. These findings indicate that the addition of textural information derived from UAV multispectral imagery could reduce the effects of background materials and saturation and enhance the N signals of rice canopies for the entire season.

Highlights

As one of the most important staple crops around the world, rice (Oryza sativa L.) crop feeds more than 50% of the world’s population
Nitrogen signals from rice leaves and plants captured by canopy spectra at early growth stages are weak, because the background materials occupy a large proportion of the field of view and the biomass of rice crops increases as the N concentration decreases due to the N dilution effect [5,6]
We investigated the potential of texture analysis from unmanned aerial vehicle (UAV)-based multispectral imagery for N status monitoring in terms of texture metrics, texture directions, and texture indices

Summary

Introduction

As one of the most important staple crops around the world, rice (Oryza sativa L.) crop feeds more than 50% of the world’s population. Nitrogen signals from rice leaves and plants captured by canopy spectra at early growth stages are weak, because the background materials (e.g., soil, water) occupy a large proportion of the field of view and the biomass of rice crops increases as the N concentration decreases due to the N dilution effect [5,6]. Since those negative influences could be reduced at reproductive stages with the stabilization of leaf biomass [6], the N status is difficult to detect at early stages and could be estimated more accurately for late stages. Determining how to enhance N signals and build suitable models for N status monitoring over the whole season remains to be addressed

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Conclusion