Optimizing Nitrogen Management in Summer Maize Using Spectral Models and UAV Technologies Throughout the Crop Life Cycle

Maize is one of the most widely planted crops worldwide, grown in over 170 countries. China is the second largest corn producer globally; moreover, summer corn dominates the Huang-Huai-Hai plain in China. Global climate change and the spread of COVID-19 have caused widespread concerns about food security. The development of smart agriculture could contribute to food security; moreover, the targeted and accurate management of crop nitrogen is a topic of concern in the field of smart agriculture. Unmanned aerial vehicle (UAV) spectroscopy has demonstrated versatility in the rapid and non-destructive estimation of nitrogen in summer maize. Previous studies focused on the entire growth season or early stages of summer maize; however, systematic studies on the diagnosis of nitrogen that consider the entire life cycle are few. This study aimed to (1) construct a practical diagnostic model of the nitrogen life cycle of summer maize based on ground hyperspectral data and UAV multispectral sensor data and (2) evaluate this model and express a change in the trend of nitrogen nutrient status at a spatiotemporal scale. Here, a comprehensive data set consisting of a time series of crop biomass, nitrogen concentration, hyperspectral reflectance, and UAV multispectral reflectance from field experiments conducted during the growing seasons of 2017–2019 with summer maize cultivars grown under five different nitrogen fertilization levels in Beijing, China, were considered. Field experiments were conducted in the China National Research Center of Watersaving Irrigation Engineering Technology, Daxing District, Beijing. Hyperspectral reflectance was measured from 2017 to 2019, using the passive, non-imaging spectroradiometer, FieldSpec HandHeld 2. Multispectral reflectance was measured through a UAV, equipped with a multispectral sensor in 2019. Multispectral data of the hyperspectral conversion from the first two experiments (2017 and 2018) were used as the training set, while those from the third experiment (2019) and the multispectral data set obtained by a UAV were, respectively, used for validating the new parameters (“validation data set”). Using regression statistics, quantitative monitoring models were established to determine plant nitrogen content during various growth stages using a multispectral index. The results demonstrated that the entire life cycle of summer maize was divided into four stages, viz., V6 (mean leaf area index (LAI) = 0.67), V10 (mean LAI = 1.94), V12 (mean LAI = 3.61), and VT-R6 (mean LAI = 3.94), respectively; moreover, the multi-index synergy model demonstrated high accuracy (mean R2=0.681) and good stability (mean RE=6.82%). The best spectral indexes of these four stages were GBNDVI, TCARI, NRI, and MSAVI2, respectively. The thresholds of the spectral index of nitrogen sufficiency in the V6, V10, V12, VT, R1, R2, and R3-R6 stages were 0.83-0.44, -0.22 to -5.23, 0.42-0.35, 0.69-0.87, 0.60-0.75, 0.49-0.61, and 0.42-0.53, respectively. The simulated nitrogen concentration at the various growth stages of summer maize was consistent with the actual spatial distribution.