1. Introduction
Considering global climate change and the COVID-19 pandemic, crops affected by droughts, floods, pests, diseases, and lack of nutrients such as Nitrogen (N) have garnered more attention [
1,
2,
3,
4]. Nitrogen is essential for crop growth and development [
5]. Using remote sensing technology to estimate nitrogen content, existing relevant studies mainly focus on nodes in a certain growth period, which makes it difficult to support relevant business requirements. 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. Therefore, the fixed-point production and management of nitrogen that considers the entire life cycle of summer maize is of great significance. Site-specific crop-production management (SCM) is an information-based technology for agricultural production management [
6]. Nitrogen is a vital nutrient for summer maize production in the North China plain; however, excessive fertilization has adverse effects on the environment [
7,
8,
9]. The nitrogen demand of summer maize varies spatially across fields, which could lead to localized differences in plant growth [
10,
11]. Hence, nitrogen management is a primary consideration in SCM [
12,
13]. An accurate and rapid monitoring of the nitrogen concentration in crops during the plant life cycle is key to assessing crop nitrogen nutrition and for the SCM of nitrogen fertilizer [
14]. It is also a key method for reducing the risk of environmental pollution caused by improper fertilization management [
15,
16].
Field variability can be evidenced by maps describing crop status. Maps could be obtained as outputs of proximal (unmanned aerial vehicle (UAV)-mounted) and remote sensing techniques using optical sensors [
17], which could then be used to interpret the dynamics of plant nitrogen demand during the crop’s life cycle [
18]. Maps help to undertake this rapidly and accurately, instead of using destructive and time-consuming ground-plant sampling and analytical measurements [
19,
20,
21].
Various satellite-mounted sensors are suitable for monitoring a crop’s nitrogen nutrient status, providing information at different levels of spatial (pixels from 300 to 0.3 m) and temporal (every 1–44 days) resolution [
22]. Numerous studies have shown that nitrogen-sensitive bands are concentrated in the visible and near-infrared regions [
18]. Furthermore, spectral indices are mainly calculated from the reflectance of visible and near-infrared bands according to ratios, normalization, or certain formulas [
23,
24,
25,
26]. However, satellite remote sensing data are greatly affected by weather—it is rainy and cloudy during the growing period of summer maize. It is challenging to obtain remote sensing data in time under cloudy conditions [
18,
27,
28,
29,
30]. Furthermore, some authors stress that this technique has limited operational flexibility for real-time field monitoring or management due to the low spatial resolution of the images acquired and the long intervals between satellite revisits.
In recent years, the limitations of remote sensing satellites have opened up new opportunities for the innovative use of unmanned aerial vehicles (UAVs) in crop monitoring. Equipped with multispectral digital cameras, these devices can be used to periodically fly over a field to obtain spectral information on crops in the visible and near-infrared regions and calculate the vegetation index with very high spatial resolution (usually <20 mm).
UAVs are easy to carry and operate and can help realize the real-time monitoring of nutrients and water management [
31], weed control, disease, and pest detection and can give an estimation of the grain yield [
22,
32] of different crops (such as rice [
33,
34], wheat [
35], maize [
36], etc.), thus providing a guarantee for the SCM. With the advent of robust and reliable unmanned aircraft and low-payload spectral scanners [
23,
37,
38,
39], an opportunity now exists for using high-resolution hyperspectral imaging techniques to predict the nitrogen nutrition status of summer maize [
23,
24,
25,
28,
40]. However, due to the high correlation of adjacent bands in a hyperspectral sensor, the redundant information is relatively increased. In comparison, the number of multispectral sensor bands are less than the hyperspectral ones, with 3 to 10 bands (generally divided into the red, green, blue, visible, thermal infrared (two bands), near-infrared, and panchromatic bands), while redundant information is relatively reduced [
41,
42]. Additionally, the hyperspectral imaging technology of UAVs requires specific flight conditions and expensive equipment, contributing to high data acquisition costs and limited promotion and application of low-altitude accurate observations [
37]. The UAV multispectral technology has garnered considerable attention due to its flexibility, convenience, and low cost. In particular, in comparison to near-surface hyperspectral point measurements, it is also attractive with regard to its scale capabilities for nitrogen management [
15,
20,
38,
43].
Numerous scholars have proposed many spectral indices for various research areas, crops, and growth stages [
23,
24,
25,
26]. Numerous diagnostic studies on the spectral nitrogen nutrition status of summer maize were also undertaken; however, they primarily focused on one or several key growth stages [
44]. Studies that consider the nitrogen nutrition status during the entire life cycle of summer maize are lacking. Most of them consider multiple linear support vector machines, machine learning language, and other methods to study and analyze crop nitrogen spectral diagnostic models. Research on a simple and practical universal model is lacking.
So far, the application of spectral technology in diagnosing summer maize nitrogen has primarily focused on single spectral technology, such as by only considering ground hyperspectral research, UAV research, or remote sensing research. Some researchers have combined ground hyperspectral technology with satellite remote sensing technology; however, few have combined ground hyperspectral technology with UAV multispectral technology. Previous studies have noted that spectral indices are developed for the entire growing season of crops; hence, they could overestimate or underestimate plant parameters at specific growth stages. For precision farming, exponents based on growth stages could be required to prevent the smoothing that could occur when an entire seasonal equation is applied to a particular stage [
45]. Additionally, spectral tests generally occur in cycles of 1–2 years; moreover, inter-annual changes also need to be studied and discussed.
Therefore, ground hyperspectral technology and UAV multispectral technology were combined here to diagnose the nitrogen nutrition status over the entire life cycle of summer maize. This study aimed to: (i) construct and evaluate a diagnostic model of nitrogen considering the entire life cycle of summer maize using systematic near-surface hyperspectral data from 2017 to 2019; (ii) optimize the summer maize life cycle nitrogen diagnostic model using UAV multispectral data from 2019; and (iii) determine the spectral index thresholds of sufficient and inadequate plant nitrogen nutrition nutrient levels at different stages of the summer maize life cycle based on the optimized nitrogen diagnostic model and the critical nitrogen concentration model.
2. Materials and Methods
2.1. Field Experiments and Environmental Conditions
Field experiments were conducted in the China National Research Center of Water-saving Irrigation Engineering Technology, Daxing District, Beijing, China (39°39′ N, 116°15′ E, 34 m above sea level) in three consecutive growing seasons (2017–2019). Daxing is located within semiarid-temperate, continental monsoon climate zones. The soil at the experimental site was classified as a tidal soil comprising a silty loam in the 0–100 cm soil layer. The average soil bulk density in the 0–60 cm soil layer is 1.42 g cm
−3. The average nitrate nitrogen content, ammonia nitrogen content, and PH value are 20.44 mg kg
−1, 5.20 mg kg
−1, and 8.2, respectively, before the summer maize sowing. The mean annual temperature is 13.4 °C and the mean annual precipitation is 532 mm. The three experimental seasons were characterized by varying environmental conditions, with major differences in rainfall and temperature, especially for July (beginning of the growing period in summer) and September (end of the growing period in autumn). In
Figure 1, the cumulative rainfall (mm) and mean temperatures (°C) of the three experimental seasons are presented with values of the long-term mean (1990–2019).
A random block design with three replications was used. The area of each plot was 60 m2, with a planting density of 55,000 plants per ha−1. The distribution obtained was maintained during the three years, preventing interference of the residual mineral nitrogen. The soil surrounding each plot was insulated to ensure that the fertilizer did not penetrate the other plots. The field experiment area was surrounded by a 5 m protected area. Cultivation and sowing were carried out simultaneously in accordance with the local uniform farming model. The terrain is flat. The soil is uniform in texture and fertility. The maize plant spacing was 30 cm and the row spacing was 60 cm. In all three seasons, a widely grown Chinese summer maize cultivar (Jiyuan 168) was sown on 11 June in 2017, and on 15 June in 2018 and 2019 at five levels of N fertilization (0 (N0), 140 (N1), 209 (N2), 279 (N3), and 419 (N4) kg·N·ha−1) with three replications. The N fertilizer was split into two doses in a ratio of 1:3 (before sowing and the tasseling (VT) stage of summer maize) and applied on 8 June and 9 August in 2017, on 12 June and 13 August in 2018, and on 12 June and 5 August in 2020. Prior to sowing, “keba” compound fertilizer (15% N) was applied. In the V12 stage, urea fertilizer (47% N) was applied. Due to a severe water shortage, 60 mm irrigation was undertaken prior to sowing in all three years of the field trials. Weeds, pests, and pathogens were frequently monitored and controlled as needed. The summer maize was harvested on 24 September in 2017–2019.
The “Jiyuan” 168 cultivar was characterized by a short ripening period, lodging resistance, good quality, good resistance, and an outstanding yield. In China, the variety is widely grown in summer maize areas.
Field test data are the basis for the model-adjustment verification, and previous studies have shown that the most effective model-adjustment parameter data are the observation data under different environments (including climate and field treatment, etc.) [
46]. Therefore, the nitrogen gradient set in this study was relatively large.
2.2. Plant Sampling and Spectral Measurements
Tissue samples were collected to measure the tissue nitrogen concentration in the above-ground biomass the day after the spectral data were collected. The plants were sampled by cutting the base of the stem just above the soil surface. Additional details regarding sample acquisition (i.e., date, stage, sample number, etc.) are listed in
supplementary Table S1. Before the annual first test, three representative plants were selected in each plot and marked as reference plants for each sampling. Three plants were then selected for each sampling with reference to the plant height, stem diameter, and panicle size of the reference plants. The plant samples were oven-dried at 105 °C for 0.5 h and then at 75 °C until a constant weight was achieved. Thereafter, the samples were weighed and ground to pass through a 1 mm sieve. The nitrogen concentration of the plants was measured using Kjeldahl digestion (
Table S2).
Here, spectral reflectance includes hyperspectral reflectance and multispectral reflectance. Hyperspectral reflectance was measured from 2017 to 2019, using the passive, non-imaging spectroradiometer, FieldSpec HandHeld 2 (ASD Inc., Boulder, CO, USA). This device provides hyperspectral data within a wavelength range of 325–1075 nm at a sampling interval of 1.5 nm. The spectral sampling dates were synchronized with the plant sampling dates; moreover, the spectral measurements were undertaken on sunny days around midday (10:00–14:00) Beijing time under clear sky conditions. The sensor had a field of view of 25°. After optimization of the ASD instrument, the reflectance of the device was set to 100% by measuring the reflectance of a spectral on a reference panel (white reference panel 25.4 × 25.4 cm, calibrated as 99% reflective). The white reference was measured approximately every 5–10 min to check the instrument’s stability for 100% reflectance. The white reference measurements were conducted by bringing the sensor close (~0.4 m) to a spectral to ensure that the reflectance of only the reference panel was measured. Three representative points were selected from each test plot; furthermore, the sensor probe was kept vertically downward during measurement. To ensure that maximum information on the plants was obtained within the measured range, and to minimize the influence of the soil background, the sensor was placed ~20 cm away from the plant canopy. The data were converted to five multispectral wavebands centered at 475 nm (blue, bandwidth: 20 nm), 560 nm (green, bandwidth: 20 nm), 668 nm (red, bandwidth: 10 nm), 840 nm (near infrared, bandwidth: 40 nm), and 717 nm (red edge, bandwidth: 10 nm) by calculating the average reflectance of the three readings per waveband.
Multispectral reflectance was measured through a UAV, equipped with a multispectral sensor in 2019. The platform used here comprised the Jingweim-M600 hex rotor UAV system controlled by the open-source flight control Pixhawk (DJI, Shenzhen, China). At present, there are many kinds of multispectral sensors (such as the Red Edge sensor, the DJI Phantom 4 Multispectral Camera, the Parrot Sequoia
+, the Sentera 6X, etc.). Through a literature review and a cost-performance analysis [
46,
47,
48], the Red Edge sensor (Mica Sense, Seattle, WA, USA) was selected. The focal length of the camera was 5.5 mm, with an image resolution of 1280 × 960 pixels. The camera was equipped with five spectral channels, with central wavelengths of 475 nm (blue), 560 nm (green), 668 nm (red), 840 nm (near infrared), and 717 nm (red edge); it was also equipped with a 30 cm × 30 cm gray plate and a light intensity sensor. The light intensity sensor corrected the image of the external light during the shooting process. The gray plate had a fixed reflectance; moreover, this plate was corrected before and after the UAV measurements. The UAV image acquisition was conducted on seven occasions between July 2019 and September 2019 and was synchronized with the time of the ground data acquisition. The UAV images were acquired from 10 am to 12 am, while the flight altitude was set to 60 m. The course was fixed during flight (
Figures S1 and S2). The overlap between the course and the side was set to 80%, the ground resolution was 4.09 cm, and the flight speed was 3 m/s. A total of 335 images were collected after the flight and imported into the Pix4Dmapper software for Mosaic. Thereafter, the reflectivity and orthophoto images of the study area were obtained after gray plate reflectivity correction.
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”).
2.3. Data Processing and Analysis
2.3.1. Calculation of Simulated Multispectral Reflectance
The equation below was used to convert the near-ground hyperspectral reflectance into multispectral reflectance. Thus, a multispectral model to diagnose nitrogen content in the plant canopy was constructed:
where
R represents the reflectivity of the wide band of the simulated multispectral;
λmin and
λmax represent the starting and ending wavelengths of the UAV sensor, respectively;
Sλ represents the value of the spectral response function of the sensor at the lambda wavelength; and
Rλ represents the hyperspectral reflectance of the summer maize plant canopy spectrum at the lambda wavelength [
49].
2.3.2. Multispectral Index
This index is a combination of two or more sensitive bands of vegetation; it helps highlight the vegetation characteristics. Here, over twenty spectral indices that had a good correlation with the nitrogen concentrations of the crops were selected from the published literature for modeling and for verification of the analysis.
Table 1 shows the formulas for calculating these multispectral indices.
2.3.3. Critical Nitrogen Content Curve
The critical nitrogen content (
Nc) was calculated by Equation (2):
where
a represents the critical nitrogen content of the plants’ aboveground unit biomass;
b represents the dilution coefficient of the critical nitrogen content; and
Wmax represents the maximum aboveground biomass of the plants (10
3 kg·ha
−1). The values of
a and
b were determined based on the field test data.
2.3.4. Leaf Area Index (LAI)
Three summer maize plants which had been tested by hyperspectral measurement in the plots were sampled. The total leaf area was measured using CanoScan LiDE 300. The LAI was calculated using Equation (3):
2.4. Model Evaluation
Using regression statistics, quantitative monitoring models were established to determine plant nitrogen content during various growth stages using a multispectral index. The simulated values of the quantitative monitoring model were calculated by the determination coefficient (R
2), the root mean square error (RMSE), and the relative error (RE). The sensitivities of the different spectral vegetation indices for detecting changes in plant nitrogen concentration in the growth stages were tested by utilizing the noise equivalent (NE) method, as reported by Viña et al. [
68]. The modeling and analysis were undertaken in Microsoft Excel 2010. Nitrogen prediction maps from the UAV data were drawn using ENVI and ArcGIS.
2.5. Diagnosis Flow of Nitrogen Nutrition Status
The diagnosis flow of the nitrogen nutrition status in this study is illustrated in
Figure 2. First, three years of near-surface hyperspectral data were simulated as multi-spectral data.
Furthermore, the whole growth stages of summer maize were divided into different stages, and models were established. The suitable multi-spectral indices for the nitrogen nutrition estimation were screened by comparative analysis by using the simulated multi-spectral experiment. In this step, the multi-spectral indices were calculated by using simulated spectra, and the relationship between the various multi-spectral indices and the nitrogen nutrition was analyzed to identify the spectral indices that enabled an accurate estimation of the nitrogen nutrition.
Finally, the multi-spectral models of nitrogen concentration were validated by the UAV multi-spectral field observation data. It was concluded that the whole growth period of summer maize could be divided into several stages for better diagnosis of the nitrogen nutrition. Then, the multi-index synergy diagnosis model of the nitrogen nutrition of summer maize over its life cycle was determined. The sensitivity of these indices to environmental interference factors was evaluated. In this step, we calculated the LAI and the critical nitrogen concentration curve and gave the spectral diagnostic threshold.
5. Conclusions
Here, ground hyperspectral technology was combined with UAV multispectral technology; thereafter, a multi-index synergy model of nitrogen concentration during the life cycle of summer maize was proposed based on the data of a 3-year systematic field experiment. Based on the critical nitrogen concentration model, the spectral index thresholds of sufficient nitrogen levels were determined at different stages.
The results of this study showed that the optimized four-stage, multi-index synergy model could better diagnose the nitrogen nutrition status considering the entire life cycle of summer maize. GBNDVI was suitable for stage V6 with low coverage (mean LAI = 0.67), the evaluation accuracy R2 was 0.833, while the spectral index threshold of nitrogen adequacy ranged from 0.83 to 0.44. TCARI was suitable for stage V10 with medium and low coverage (mean LAI = 1.94), with an R2 of 0.547. The spectral index threshold of nitrogen adequacy ranged from −0.22 to −5.23. NRI was suitable for stage V12 with medium to high coverage (mean LAI = 3.61), and with an R2 of 0.612. The spectral index threshold of nitrogen adequacy was 0.42–0.35. MSAVI2 was suitable for stage VT–R6 with high coverage (mean LAI = 3.94), and with an R2 of 0.735. The spectral index thresholds of nitrogen adequacy in stages VT, R1, R2, and R3-R6 were 0.69–0.87, 0.60–0.75, 0.49–0.61, and 0.42–0.53, respectively.
The nitrogen concentration of summer maize plants simulated by the optimal multispectral index synergy model constructed at the different growth stages was similar to the actual spatial distribution. This study provides technical support for the timely, rapid, convenient, and accurate diagnosis of summer maize nitrogen nutrition and provides a basis for the SCM of nitrogen.