Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Authors

Jie Jiang, Nanjing Agricultural University
Zeyu Zhang, Nanjing Agricultural University
Qiang Cao, Nanjing Agricultural University
Yan Liang, Nanjing Agricultural University
Brian Krienke, University of Nebraska-LincolnFollow
Yongchao Tian, Nanjing Agricultural University
Yan Zhu, Nanjing Agricultural University
Weixing Cao, Nanjing Agricultural University
Xiaojun Liu, Nanjing Agricultural University

ORCID IDs

Qiang Cao

Yan Zhu

Xiaojun Liu

Date of this Version

11-10-2020

Citation

Remote Sens. 2020, 12, 3684; doi:10.3390/rs12223684

Comments

CC-BY

Abstract

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha⁻¹ ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R² = 0.78), LDM (R² = 0.61), PDM (R² = 0.63), LNA (R² = 0.65) and PNA (R² = 0.73), whereas the relationships between RERVI (R² = 0.62), CIRE (R² = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.