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.
Highlights
Overapplication of nitrogen (N) fertilizers is a common problem in crop production
The plant N accumulation (PNA) was most variable (coefficient of variation (CV) = 71.70%) across all growth stages, followed by leaf N accumulation (LNA) (CV = 70.11%), leaf area index (LAI) (CV = 60.08%), plant dry matter (PDM) (CV = 57.14%) and leaf dry matter (LDM) (CV = 51.00%), and nutrition index (NNI) had the lowest coefficient of variation (43.28%)
The analysis showed that LDM, PDM, LNA and PNA were more variable during Feekes stages 6.0–10.0 (CV = 53.46%, 56.31%, 72.79% and 70.04%, respectively) than Feekes stages 10.3–11.1 (CV = 49.49%, 35.46%, 66.61% and 62.09%, respectively), and LAI, NNI showed more variable during Feekes stages 10.3–11.1 (CV = 62.21% and 44.44%, respectively) than stages 6.0–10.0 (CV = 52.24% and 40.63%, respectively)
Summary
Overapplication of nitrogen (N) fertilizers is a common problem in crop production. Precision N management is developed to improve N use efficiency by matching fertilizer N input with spatial and temporal crop N demand [1,2]. The red band can be used to calculate different red-based vegetation indices, such as common difference vegetation index (DVI) and NDVI, which have been used for successfully monitoring rice LAI [24], maize N status [18] and wheat yield [25]. Erdle et al [26] compared three different active and passive spectral sensors to discriminate biomass parameters and nitrogen status in wheat and found the red edge ratio vegetation index (RERVI, R760/R730) was the most powerful and temporally stable spectral index for monitoring wheat biomass and N status among all indices tested. It is necessary to evaluate the performance of different vegetation indices derived from the RapidSCAN CS-45 sensor for winter wheat growth and N status estimation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
