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

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.

Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on wavelet neural network approach using spectral indices

“…Trilateral parameters, especially red edge parameters, effectively reflect the characteristics of crop N status [ 49 , 50 ]. Studies on wheat, rice, and other crops developed N content estimation models based on red edge parameters, which achieved satisfactory results [ 51 , 52 ]. In this study, we established that the correlation between blue edge parameters and petiole NO 3 − –N contents was strong, and that the traditional regression model of blue edge parameters and petiole NO 3 − –N content was superior to the red edge and yellow edge parameters.…”

A Bi-Temporal-Feature-Difference- and Object-Based Method for Mapping Rice-Crayfish Fields in Sihong, China

“…Sihong County (117 • 56 -118 • 46 E, 33 • 08 -33 • 44 N) [25], the study area, is located in the northwestern part of Jiangsu Province, China. The terrain is dominated by plains, with scattered granite and hills distributed in the west and southwest [26]. The location of the study area and its terrain are shown in Figure 1a,b (ground elevation data from http://www.gscloud.cn, accessed on 1 March 2022).…”

Inversion of Winter Wheat Growth Parameters and Yield Under Different Water Treatments Based on UAV Multispectral Remote Sensing

“…At the winter wheat key stages (DAS210-DAS240) (Du et al, 2017;Yue et al, 2017;Ji et al, 2020;Jiang et al, 2020;Tao et al, 2020a), crop parameters (LAI and SPAD) were measured on DAS210, DAS229, and DAS240. Sixty sets of samples (LAI [30 sets] and SPAD [30 sets]) were obtained every 7 days.…”