Maize Canopy Research Articles

Historical increases of maize leaf area index in the US Corn Belt due primarily to plant density increases

ContextLeaf area index (LAI) and leaf area distribution within the maize plant are important traits used to explain and predict light interception and thus crop productivity. ObjectivesHere we investigate breeding and plant density effects of leaf area traits. Our objectives are to 1) quantify maize breeding impacts on leaf area distribution and determine bell-shape coefficients used in crop modeling, 2) dissect the contribution of breeding from plant density, and 3) explore the relationship between LAI and crop yields. MethodsWe studied 18 hybrids released between 1983 and 2017 at two density treatments: current (8.5 pl m-2) and historical increasing density (from 4.6 to 8.5 pl m-2) in Iowa, USA. ResultsResults indicated that concurrent changes in hybrids and increases in plant density have increased LAI from 3.4 (in 1983) to 5.9 m2 m-2 (in 2017), with the highest LAI increases (>50%) to be realized in the middle canopy. At historical increasing in plant density treatment, the LAI increased by 1.6% year-1, but the individual plant leaf area decreased by 0.33% year-1 from 1983 to 2017. This trade-off indicates that new hybrids are more tolerant to higher plant populations than old hybrids. At current plant density treatment, the year of hybrid release did not affect LAI or individual plant leaf area. New hybrids had 5% narrower leaf area distributions, 23% higher optimum LAI values (5.2 vs 4.2 m2 m-2) and 19% higher grain yields compared to old hybrids. ConclusionsThe main reason for the increase in maize LAI in the US Corn Belt is plant density. However, an increase in LAI does not necessarily translate to higher grain yields as new hybrids had significantly higher grain yields than older hybrids at similar LAI values. Present results contribute to our understanding of maize canopy architecture and allow us to better calibrate crop models to accurately estimate LAI and grain yield.

Correction: Extraction of maize canopy temperature and variation factor analysis based on multi-source unmanned aerial vehicle remote sensing data

Correction: Extraction of maize canopy temperature and variation factor analysis based on multi-source unmanned aerial vehicle remote sensing data

Canopy height uniformity: a new 3D phenotypic indicator linking individual plant to canopy

Canopy height uniformity (CHU) is a key indicator linking individual plants to populations. Determining CHU by the manual measurement of the height of individual plants is inefficient and subjective, making meeting the demand for a high-throughput assessment of CHU in high-yield crop management and variety selection challenging. Therefore, a high-throughput CHU estimation approach using unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) data is proposed. First, individual plant point clouds were segmented by incorporating planting density, and the CHU was estimated based on the extracted plant heights (PHs). The CHU was then applied to quantify the effects of different cropping practices on maize canopy structure. In addition, 18 canopy structural parameters (SPs) were extracted from the canopy point cloud, and aboveground biomass (AGB) was estimated by combining these SPs with Pelican Optimization Algorithm (POA) machine learning models (POA-MLs). Finally, as an indicator of individual variability within the canopy, CHU was integrated into the training process to evaluate the accuracy of AGB estimation for different models and datasets. The results showed that the plant height extracted by the canopy population-individual plant segmentation was accurate, with R2 ranging from 0.85 to 0.93. The CHU was able to accurately quantify the effects of different cropping practices on canopy structure. An increase in applied nitrogen fertilizer and irrigation could significantly contribute to an increase in CHU and the formation of a clean and homogeneous canopy structure. Meanwhile, marginal effects can be accurately quantified through PHs estimation to further quantify the intra-canopy differences. In addition, the accuracy of the AGB estimation can be effectively improved by merging SPs, PH, and CHU. In this study, we demonstrated the efficacy of CHU as a phenotypic indicator for representing differences in canopy structure, thus providing practical phenotypic identification information for breeders and field managers.

Quantification of spatial-temporal light interception of crops in different configurations of soybean-maize strip intercropping.

Intercropping can improve light interception and crop yield on limited farmlands. The light interception rate in intercropping is determined by row configuration. Quantifying the spatio-temporal light interception of intercrops is very important for improving crop yields by optimizing the row configuration. A two-year field experiment was conducted at two sites to quantify the responses of the light interception rate of intercrops to five treatments: two rows of maize alternated with three rows of soybean (2M3S), two rows of maize alternated four rows of soybean (2M4S), two rows of maize alternated five rows of soybean (2M5S), sole soybean (SS), and sole maize (SM). We developed a multiple regression model based on the sine of the solar elevation angle (sin(h)) and crop leaf area density (LAD) to quantify the spatio-temporal light interception of intercrops. The predicted light interception rate was positively correlated with the measured values of photosynthetically active radiation (R2 > 0.814) and dry matter (R2 > 0.830). Increasing soybean rows led to an increase in light interception of both soybean and the lower layer of maize. However, this also resulted in a decrease in light interception in the upper layer of maize. At the two sites, compared to 2M3S, the annual average cumulative light interception of soybean in 2M5S increased by 44.73% and 47.18%, that of the lower layer of maize in 2M5S increased by 9.25% and 8.04%, and that of whole canopy of maize decreased by 13.77% and 17.74% respectively. The changes in dry matter and yield of intercrops were consistent with the change in light interception, which further verified the high accuracy of the light interception model. The annual average maize yield of 2M5S was 6.03% and 6.16% lower but the soybean yield was 23.69% and 28.52% higher than that of 2M3S. On the basis of system yield, the best performance was recorded in 2M4S at the two sites. In summary, the newly created light interception model performs well in the quantification of the temporal and spatial changes in crop light interception in strip intercropping and has potential applications in other configurations. Optimizing row configurations across climatic regions to enhance light interception and yield at the system level will become a future target.

Design and testing of an intelligent variable-rate spraying system based on LiDAR-guided application

Traditional plant protection machinery cannot apply pesticides according to the actual growth demand of crops, which causes a lot of pesticide waste and serious environmental pollution. Based on this, an intelligent variable-rate application system based on 3D LiDAR (light detection and ranging) scanning and PWM (pulse-width modulation) control for maize canopy was developed in this paper. After completing the performance testing, the system was mounted on a lightweight high-clearance self-propelled sprayer by remote controlling with an operating width of 6.0 m, and a ground gap of 2.0 m. And the actual operational performance of the sprayer showed that the prediction error of LiDAR for plant height was within 10%, which illustrated the accuracy of the detection system and the reliability of the variable application based on the plant height change. And the variable-rate system could adjust the spray volume in real-time according to the plant height change. The penetration rate of the upper, middle, and bottom layers maintained a relatively stable state (upper > middle > bottom) at variable-rate application and could reach the normal application level. This study can provide new ideas for efficient pests and diseases control of high stalk crops, especially suitable for small fields, and gives a reference for the structural design of new precise variable-rate application machinery and performance optimization of efficient application technology.

Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion

Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion

Effect of Maize Canopy Structure on Light Interception and Radiation Use Efficiency at Different Canopy Layers

Effect of Maize Canopy Structure on Light Interception and Radiation Use Efficiency at Different Canopy Layers

Machine learning approaches for estimation of the fraction of absorbed photosynthetically active radiation and net photosynthesis rate of maize using multi-spectral sensor

The fraction of absorbed photosynthetically active radiation (FAPAR) and the photosynthesis rate (Pn) of maize canopies were identified as essential photosynthetic parameters for accurately estimating vegetation growth and productivity using multispectral vegetation indices (VIs). Despite their importance, few studies have compared the effectiveness of multispectral imagery and various machine learning techniques in estimating these photosynthetic traits under high vegetation coverage. In this study, seventeen multispectral VIs and four machine learning (ML) algorithms were utilized to determine the most suitable model for estimating maize FAPAR and Pn during the kharif and rabi seasons at Tamil Nadu Agricultural University, Coimbatore, India. Results demonstrate that indices such as OSAVI, SAVI, EVI-2, and MSAVI-2 during the kharif and MNDVIRE and MSRRE during the rabi season outperformed others in estimating FAPAR and Pn values. Among the four ML methods of random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), and multiple linear regression (MLR) considered, RF consistently showed the most effective fitting effect and XGBoost demonstrated the least fitting accuracy for FAPAR and Pn estimation. However, SVR with R2 = 0.873 and RMSE = 0.045 during the kharif and MLR with R2 = 0.838 and RMSE = 0.053 during the rabi season demonstrated higher fitting accuracy, particularly notable for FAPAR prediction. Similarly, in the prediction of Pn, MLR showed higher fitting accuracy with R2 = 0.741 and RMSE = 2.531 during the kharif and R2 = 0.955 and RMSE = 1.070 during the rabi season. This study demonstrated the potential of combining UAV-derived VIs with ML to develop accurate FAPAR and Pn prediction models, overcoming VI saturation in dense vegetation. It underscores the importance of optimizing these models to improve the accuracy of maize vegetation assessments during various growing seasons.

A method to rapidly construct 3D canopy scenes for maize and their spectral response evaluation

Three-dimensional (3D) scenes of a maize canopy can be utilized to depict its vertical structure and serve as the foundation for a 3D radiative transfer model. Given the inherent heterogeneity of canopy structures, evaluating their spectral response is crucial for remote-sensing inversion algorithms. Yet existing methods to model 3D canopy scenes of crops lack the high efficiency required for large-scale breeding applications. Hence, this study aims to develop a method to rapidly construct 3D canopy scenes of maize based on key structural parameters—namely, leaf area, base angle, and inclination angle—across multiple cultivars and growth stages. The accuracy of this canopy scenes modeling method was validated by comparison with a fine-scale 3D model and a multi-growth stage reflectance dataset. The root mean square error (RMSE) and normalized root mean square error (NRMSE) between the fine-scale 3D model and the constructed 3D model were less than 0.021 and 6.4%, respectively. Moreover, the RMSE and NRMSE between the simulated reflectance and measured reflectance were less than 0.042 and 11.1%, respectively. Next, we integrated the 3D radiative transfer model with 3D scenes to analyze the spectral response of maize canopy structure. The findings revealed that the near-infrared band was affected more by leaf area than leaf base angle, while the opposite is observed for the red-edge band. Furthermore, the middle layer contributed more to the canopy reflectance than either the upper or lower layers. Notably, two maize scenes where two structural parameters simultaneously change could elicit the same canopy spectra. The proposed method thus offers the dual advantages of rapid modeling and high efficiency, making it highly applicable for maize and other similar crops.

Swin-Roleaf: A new method for characterizing leaf azimuth angle in large-scale maize plants

Maize is the most productive grain crop in the world in terms of total production and yield. Leaf azimuth angle, the directional orientation of the maize leaf, is an important characteristic that affects the efficiency of sunlight reception and photosynthesis in maize canopies. To study maize plant architecture, most of the previous studies have been focusing on leaf vertical angle characterization due to the inherent difficulty of measuring leaf azimuth angle accurately. Achieving accurate and efficient measurement of leaf azimuth angle in maize is important for yield improvement. Traditionally, leaf azimuth angle is measured manually using a protractor or manual annotations of images, which is time-consuming, costly, and prone to human error. Recent advances in Unmanned Aerial Vehicle (UAV) and artificial intelligence have shown great potential in leaf azimuth angle detection. However, most of the deep learning algorithms for object detection are developed for Horizontal Bounding Boxes (HBBs). To measure the azimuth angle of maize leaves, a robust Oriented Bounding Boxes (OBBs) detection algorithm is needed. In this study, a dataset containing top-view images of maize plants was collected using an UAV. An automated tool, Swin-Roleaf, was developed for maize leaf detection. It is an improvement of the YOLOv5 model, uniquely integrating the Swin Transformer (ST) and Circular Smooth Label (CSL). Swin-Roleaf was able to accurately detect OBBs of individual leaves and thereby enable the estimation of leaf azimuth angle. The experimental results showed that Swin-Roleaf was able to accurately detect maize leaves and measure the azimuth angle with a model OBBAP50 of 90.59% on OBBs. The system-derived leaf azimuth angle was highly correlated with the ground truth with a correlation coefficient larger than 0.96. Our study demonstrates for the first time the feasibility of using UAVs coupled with deep learning algorithms to detect leaf azimuth angles under field conditions. The system can significantly speed up the measurement of maize leaf azimuth angles and provide a new tool to accelerate breeding programs.

Extraction of maize canopy temperature and variation factor analysis based on multi-source unmanned aerial vehicle remote sensing data

Extraction of maize canopy temperature and variation factor analysis based on multi-source unmanned aerial vehicle remote sensing data

Plant Growth Regulators Enhance Maize (Zea mays L.) Yield under High Density by Optimizing Canopy Structure and Delaying Leaf Senescence

Shaping the canopy architecture and delaying leaf senescence in maize are pivotal strategies for extending the crop’s photosynthetic period and improving yield. The application of plant growth regulators (PGRs) is a critical cultivation measure, with the timing of application being of paramount importance. To explore the effects of PGR application time on maize canopy structure, leaf senescence characteristics and yield, a comparative two-year field study was undertaken during the 2019–2020 growing seasons at the Gongzhuling Experimental Station of the Jilin Academy of Agricultural Sciences, utilizing a PGR containing ethephon as the active ingredient. The experiment was structured with two plant densities of 60,000 and 90,000 plants ha−1, and three distinct PGR application protocols: T10 (application of PGR at the 10th leaf stage), T15 (application at the 15th leaf stage), and CK (control group sprayed with water). The result indicated that the yield increased by 5.62% following T15 treatment compared to the CK under high density (90,000 plants ha−1). Furthermore, the kernel per ear and the 1000-kernel weight increased by 3.93% and 5.62% respectively, while the abortion rate decreased. Correlation analysis showed that yield and yield components were correlated with plant morphology, physiology, and aging characteristics under 90,000 plants ha−1. Pollen density was also positively correlated with the top leaf area and the top leaf angle (p < 0.01). Furthermore, relative green leaf area at maturity (RGLAM) showed positive correlations with chlorophyll b, superoxide dismutase activity (SOD), peroxidase activity (POD), catalase activity (CAT), and soluble protein content (p < 0.01), while displaying a negative correlation with malondialdehyde content (MDA) (p < 0.01). Spraying plant growth regulators at the 15-leaf stage under high density can effectively enhance the top canopy structure of the maize and reduce the upper leaf area and angle, increase pollen density, and boost the number of grains. Furthermore, it delayed the senescence of leaves, prolonged the functional period of the leaves, increased kernel weight, optimized light resource utilization, and ultimately enhanced the maize yield.

Diverse tillage practices with straw mulched management strategies to improve water use efficiency and maize productivity under a dryland farming system

Straw mulching incorporation has a wide range of environmental benefits that make it an effective practice for sustainable agro-ecosystem in the semi-arid regions. There is an urgent need to improve the 13C-photosynthates distribution, water use efficiency (WUE) and maize canopy characteristics under the diverse tillage practices with straw mulched management strategies for sustainable intensification of maize production. The field study consists of three diverse tillage systems (RT: rotary tillage; CT, conventional tillage; MT, minimum tillage) with three straws mulching (NS: no straw mulch; SS: straw mulch on the soil surface; SI: straw incorporated into the soil) were assessed under the ridge-furrow rainfall harvesting system. Our results showed that the rotary tillage with straw incorporated into the soil significantly reduces the ET rate (11 %), and leaf rolling index; as a result considerably improves LAI, LEI, 13C-photosynthates distribution, N accumulation, and above ground biomass under various growth stages. The RTSI treatment significantly improved soil water storage, soil organic carbon (52 %, SOC), soil C storage (39 %, SCS), and NPK nutrients uptake (70 %, 62 %, and 69 %) of maize than observed for the rest of all other treatments, respectively. The RTSI treatment improves soil water balance, grain yield (53 %), biomass yield (37 %), WUEg (51 %), WUEb (35 %), nutrients uptake, and mitigating soil water depletion than the MTNS treatment. Although RTSS can achieve optimal soil water storage in the short term, RTSI has a great potential in improving soil carbon stability, canopy characteristics, soil water storage, and WUE, contributing to sustainable and intensive corn production in agricultural ecosystems in semi-arid regions.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

Complement time-series UAV spectral data based on Ambrals kernel-driven model to monitor soil moisture content

ABSTRACT Continuous time-series spectral data are important for inversion of crop or soil information. UAV remote sensing is usually selected under clear and windless weather conditions, but it is not possible to have such weather every day, which results in the UAV not collecting continuous daily spectral information. To explore this issue, we focused on summer maize with four irrigation levels as the research subject. A UAV platform with a multispectral sensor was used to acquire measured spectra of the maize canopy. The solar zenith angle was calculated and substituted into the Ambrals kernel-driven model to obtain simulated spectral data for the maize canopy, and the time-series UAV spectral data were complemented. Then, four vegetation indices (VIs) were established using simulated and measured spectral data, respectively, and the accuracy of the simulated VIs was evaluated. Finally, the simulated and measured VIs were used to monitor and evaluate variations in soil surface moisture content, respectively, and provide irrigation warning. The results demonstrated that Ambrals kernel-driven model can be used to simulate the reflectance of maize canopy collected by UAV. The simulated reflectance can complement time-series UAV spectral data and be used to establish VIs, among which Structure Intensive Pigment Index (SIPI) was established with the highest accuracy (R = 0.729). The VIs established by simulated reflectance can be used to monitor soil surface moisture content, and the monitoring effect is similar to the measured data (R2 = 0.642, RMSE = 0.42). It can evaluate the soil moisture a few days after irrigation and ensure the continuity and timeliness of soil moisture data, so as to improve the crop irrigation system and carry out irrigation warning. These results have certain reference for the supplementation of time-series spectral data and farmland irrigation using UAV multispectral remote sensing.

On recursive partitioning to refine coordinate rotation in Eddy covariance applications

While the use of three-dimensional sonic anemometers for eddy covariance is well established, there remain questions about its deployment and consequent data analysis. Convention has largely accepted that coordinates must be rotated so that the corrected transverse and vertical mean velocities become zero. In non-ideal terrain, a planar fit coordinate rotation is widely accepted. However, if the coordinate system changes through time, such as in a rapidly growing maize canopy, or is affected by the local topography, finding temporally and directionally stable regions for a reference point is crucial for flux estimation. A model-based recursive partitioning (MOB) algorithm is proposed to answer this problem. The algorithm creates individual wind sectors and accounts for temporal or spatially dependent variables that may affect planar fits. A cross-validation fitting procedure of the MOB algorithm has proved crucial in identifying variables that influence the choice among different parametric models, such as day of year, solar altitude, and wind direction. Momentum exchange is the most sensitive to alternative coordinate rotation systems, especially in stable conditions. The MOB methodology would be most useful during these conditions. The analyses presented here show that in quantifying scalar fluxes during the day, especially in the case of sensible heat flux and by extension to evapotranspiration and carbon dioxide exchange, there is little need for coordinate rotation if the sensors were aligned with the vertical parallel to gravity.

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence. An initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture. The subsequent model considers the 3D phytomers of maize as intelligent agents. The aim is to maximize the ratio of sunlit leaf area, and by iteratively modifying the azimuth angle of the 3D phytomers, a 3D maize canopy model that maximizes light resource interception can be constructed. Additionally, the method incorporates a reflective approach to optimize the canopy and utilizes a mesh deformation technique for detecting and responding to leaf collisions within the canopy. Six canopy models of 2 varieties plus 3 planting densities was constructed for validation. The average R2 of the difference in azimuth angle between adjacent leaves is 0.71, with a canopy coverage error range of 7% to 17%. Another 3D maize canopy model constructed using 12 distinct density gradients demonstrates the proportion of leaves perpendicular to the row direction increases along with the density. The proportion of these leaves steadily increased after 9 × 104 plants ha-1. This study presents a 3D modeling method for the maize canopy. It is a beneficial exploration of swarm intelligence on crops and generates a new way for exploring efficient resources utilization of crop canopies.

Maximizing the Radiation Use Efficiency by Matching the Leaf Area and Leaf Nitrogen Vertical Distributions in a Maize Canopy: A Simulation Study.

The radiation use efficiency (RUE) is one of the most important functional traits determining crop productivity. The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence. However, previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth, rarely considering the optimization of the leaf area distribution, especially for C4 crops. The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy. To test this hypothesis, various virtual maize canopies were generated by combining the leaf inclination angle, vertical leaf area distribution, and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model. We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE. The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios, particularly in dense canopies. This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.

Diurnal Trends of Maize Canopy Cover Under Water Stress

Highlights RGB trail cams above maize crop collected images every 15 minutes, day and night, to monitor leaf curl. Fractional canopy cover (fc,%) was constant for full irrigation but reduced during the day for limited irrigation. Techniques were developed to estimate reductions in water use (i.e., evapotranspiration) using images of fc. Measurements of fc prior to wilting represent transpiration demand (Kcb ), and reductions of fc represent stress (Ks ). Abstract. By differentiating green and non-green pixels of RGB images of crop canopy, the fractional canopy cover (fc,%) can be estimated and subsequently used to estimate crop transpiration demand (e.g., Kcb, basal crop coefficient, following FAO-56 methodology). While unstressed maize crops maintain consistent fc through the day, leaves of heat or water stressed crops will curl, thereby reducing the instantaneous fc and the associated transpiration. This paper reports the effects of diurnal leaf curl and subsequent fc reduction on full and limited water treatments of irrigated maize through nadir images obtained every 15 minutes. Results suggest that fc images obtained in the early morning (fcNS) are typically representative of a non-stressed condition and can be used to estimate Kcb. Reduction of fc throughout the day (i.e., fc/fcNS) was compared to water stress coefficient Ks obtained from neutron probe and TDR measurement and showed that fc/fcNS from hours 11 a.m. to 2 p.m. had a high correlation (R2 = 0.773), indicating that mid-day fractional canopy cover reduction could be used as a proxy for Ks. This method creates new opportunities for estimating crop water stress during vegetative growth but also highlights potentially unknown challenges regarding lower resolution remote sensing during the same growth stages. Keywords: Basal crop coefficient, Canopy cover, Crop coefficient, Evapotranspiration, Stomatal conductance, Stress crop coefficient, Transpiration, Water stress.

Prediction of vertical distribution of SPAD values within maize canopy based on unmanned aerial vehicles multispectral imagery.

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R2 and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods.