Maize Leaf Area Index Research Articles

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020–2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0–40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient k(vartheta ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing k(vartheta ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The k(vartheta ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval.

Estimating maize LAI by exploring deep features of vegetation index map from UAV multispectral images

Visible and near-infrared imaging spectroscopy is an efficient method for non-destructive estimation of crop leaf area index (LAI), in which effective features extraction is a key factor affecting model accuracy. Vegetation index like normalized difference vegetation index (NDVI) is a typical parameter extracted from spectral images for LAI estimation. However, it always faces challenges of values saturation and sensitivity decreasing due to the canopy coverage changes and complex environment influences. Therefore, the study aims to propose a method to explore deep features based on deep learning model (ResNet50) and vegetation index (VI) map, by which contributes to improve the accuracy of maize LAI estimation. In two-years’ experiments of 2020 and 2021, the multi-spectral imaging sensor carried by the unmanned aerial vehicle (UAV) was used to collected remote sensing images of the maize canopy during multi-growth stages under different nitrogen fertilizer treatments, and a total of 792 LAI values were collected. In terms of feature extraction, 10 VIs were extracted based on the spectral images of maize canopy, and comprehensive features (texture and deep features) were extracted based on the VI map. In terms of model construction, partial least squares regression (PLS) models were constructed based on three feature types. The results showed that normalized difference red edge (NDRE) VI was the best indicator for monitoring maize LAI among all the VIs investigated. Meanwhile, LAI multivariate linear model constructed based on texture features extracted from NDRE images had better accuracy. Deep features of NDRE images were further extracted by ResNet50 model. And the maize LAI estimation models based on PLS were constructed with VI, texture features and deep features of two-year data, respectively. The results showed that compared with VI and texture features, the LAI estimation model based on deep features showed optimal accuracy in two-year data ( R 2 = 0.827, greater than 0.781 and 0.780; RMSEP = 0.405, less than 0.455 and 0.455; MAE = 0.320, less than 0.366 and 0.363). In addition, the LAI estimation model based on deep features was successfully evaluated with 2020 and 2021 validation data (2020: R 2 = 0.872, RMSEP = 0.379, MAE = 0.294; 2021: R 2 = 0.733, RMSEP = 0.433, MAE = 0.348). The experimental results demonstrate the effectiveness of the proposed deep feature extraction method based on deep learning model and VI images in LAI estimation, which provides a feasible method for monitoring crop growth information based on UAV platform.

Enabling Deep-Neural-Network-Integrated Optical and SAR Data to Estimate the Maize Leaf Area Index and Biomass with Limited In Situ Data

Accurate estimation of the maize leaf area index (LAI) and biomass is of great importance in guiding field management and early yield estimation. Physical models and traditional machine learning methods are commonly used for LAI and biomass estimation. However, these models and methods mostly rely on handcrafted features and theoretical formulas under idealized assumptions, which limits their accuracy. Deep neural networks have demonstrated great superiority in automatic feature extraction and complicated nonlinear approximation, but their application to LAI and biomass estimation has been hindered by the shortage of in situ data. Therefore, bridging the gap of data shortage and making it possible to leverage deep neural networks to estimate maize LAI and biomass is of great significance. Optical data cannot provide information in the lower canopy due to the limited penetrability, but synthetic aperture radar (SAR) data can do this, so the integration of optical and SAR data is necessary. In this paper, 158 samples from the jointing, trumpet, flowering, and filling stages of maize were collected for investigation. First, we propose an improved version of the mixup training method, which is termed mixup+, to augment the sample amount. We then constructed a novel gated Siamese deep neural network (GSDNN) based on a gating mechanism and a Siamese architecture to integrate optical and SAR data for the estimation of the LAI and biomass. We compared the accuracy of the GSDNN with those of other machine learning methods, i.e., multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and a multilayer perceptron (MLP). The experimental results show that without the use of mixup+, the GSDNN achieved a similar accuracy to that of the simple neural network MLP in terms of R2 and RMSE, and this was slightly lower than those of MLR, SVR, and RFR. However, with the help of mixup+, the GSDNN achieved state-of-the-art performance (R2 = 0.71, 0.78, and 0.86 and RMSE = 0.58, 871.83, and 150.76 g/m2, for LAI, Biomass_wet, and Biomass_dry, respectively), exceeding the accuracies of MLR, SVR, RFR, and MLP. In addition, through the integration of optical and SAR data, the GSDNN achieved better accuracy in LAI and biomass estimation than when optical or SAR data alone were used. We found that the most appropriate amount of synthetic data from mixup+ was five times the amount of original data. Overall, this study demonstrates that the GSDNN + mixup+ has great potential for the integration of optical and SAR data with the aim of improving the estimation accuracy of the maize LAI and biomass with limited in situ data.

The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization

Crop model–based irrigation scheduling is essential for improving crop production and water resource allocation. However, the parametrization of crop growth processes, for which relevant genetic coefficients must be determined through field scale experiments, is usually hampered by poor spatial representation; this limitation compromises the robustness of irrigation scheduling at the regional scale. We propose a framework for optimizing regional irrigation schedules; this framework involves using a distributed crop model with crop parameters determined through data assimilation. We first conducted a sensitivity analysis to identify the key genetic coefficients (P2: photoperiod sensitivity coefficient; P5: thermal time from silking to physiological maturity; G3: kernel filling rate during the linear grain filling stage and under optimal conditions) affecting the leaf area index (LAI) in the CERES-Maize model. Subsequently, the field-scale CERES-Maize model was calibrated and validated using data collected from field experiments conducted in 2016 and 2017, respectively. Subsequently, the LAI estimated from 2020 microwave backscattering data was used to assimilate remote sensing information and crop model in order to obtain the key genetic coefficients of maize in the distributed crop model. Finally, on the basis of the distributed crop model, a multiobjective genetic algorithm was executed to optimize irrigation schedules under various meteorological scenarios (e.g., precipitation and reference evapotranspiration). The LAI of maize at a regional scale was accurately estimated using backscattering information extracted from microwave remote sensing images (R2 = 0.94, RMSE = 0.27). Compared with the crop model parameters calibrated at the field scale, the crop model parameters determined through data assimilation resulted in more accurate simulations of LAI, soil water content, and yield at the regional scale. The irrigation schedule based on the distributed model increased the maize yield corresponding to the local current irrigation schedule by 125–851 kg/ha, increased the water use efficiency by 0.02–0.17 kg/m3, and reduced the irrigation amount by 2–60 mm. The proposed framework for estimating genetic coefficients based on data assimilation methods helps in regional irrigation scheduling for other crops.

Estimating leaf area index of maize using UAV-based digital imagery and machine learning methods

Leaf area index (LAI) is a fundamental indicator of crop growth status, timely and non-destructive estimation of LAI is of significant importance for precision agriculture. In this study, a multi-rotor UAV platform equipped with CMOS image sensors was used to capture maize canopy information, simultaneously, a total of 264 ground‐measured LAI data were collected during a 2-year field experiment. Linear regression (LR), backpropagation neural network (BPNN), and random forest (RF) algorithms were used to establish LAI estimation models, and their performances were evaluated through 500 repetitions of random sub-sampling, training, and testing. The results showed that RGB-based VIs derived from UAV digital images were strongly related to LAI, and the grain-filling stage (GS) of maize was identified as the optimal period for LAI estimation. The RF model performed best at both whole period and individual growth stages, with the highest R2 (0.71–0.88) and the lowest RMSE (0.12–0.25) on test datasets, followed by the BPNN model and LR models. In addition, a smaller 5–95% interval range of R2 and RMSE was observed in the RF model, which indicated that the RF model has good generalization ability and is able to produce reliable estimation results.

Predicting maize leaf area index by partial least square combined with wavelet transform

AbstractThe leaf area index (LAI) is crucial for assessing and monitoring maize (Zea mays L.) vegetation status and photosynthetic ability. Predicting maize LAI by hyperspectral remote sensing technology is significant for managing agricultural production. In this study, three N rates (0, 120, and 240 kg N ha−1) and four drought stress treatments (60–70%, 45–55%, 30–40%, and 15–25% of field capacity), were imposed to provide the different environments for maize. The canopy spectral reflectance and LAI of maize were measured at the V6 and V12 stages. In this study, the main objectives were to investigate the performance of a new statistical method for monitoring LAI from canopy spectral reflectance. We used the canopy spectral reflectance to estimate the LAI and compared several methods of spectral analysis, including vegetation indices, wavelet functions, and the combination of continuous wavelet transform (CWT)–uninformative variable elimination (UVE)–partial least squares (PLS) (i.e., CWT–UVE–PLS), for spectral reflectance data analysis and model construction of maize LAI. Results showed that the model using the combination of db3–UVE–PLS achieved the best performance (with the highest coefficient of determination [R2] and lowest RMSE for the calibration [R2 = .979, RMSEC = .172] and validation [R2 = .861, RMSEP = .533] datasets, respectively) in estimating the maize LAI. The CWT–UVE–PLS exhibited a considerable advantage in avoiding redundant or noise information interaction and achieving excellent correlations with maize LAI.

Estimation of Maize LAI Using Ensemble Learning and UAV Multispectral Imagery under Different Water and Fertilizer Treatments

The leaf area index (LAI), commonly used as an indicator of crop growth and physiological development, is mainly influenced by the degree of water and fertilizer stress. Accurate assessment of the LAI can help to understand the state of crop water and fertilizer deficit, which is important for crop management and the precision agriculture. The objective of this study is to evaluate the unmanned aerial vehicle (UAV)-based multispectral imaging to estimate the LAI of maize under different water and fertilizer stress conditions. For this, multispectral imagery of the field was conducted at different growth stages (jointing, trumpet, silking and flowering) of maize under three water treatments and five fertilizer treatments. Subsequently, a stacking ensemble learning model was built with Gaussian process regression (GPR), support vector regression (SVR), random forest (RF), least absolute shrinkage and selection operator (Lasso) and cubist regression as primary learners to predict the LAI using UAV-based vegetation indices (VIs) and ground truth data. Results showed that the LAI was influenced significantly by water and fertilizer stress in both years’ experiments. Multispectral VIs were significantly correlated with maize LAI at multiple growth stages. The Pearson correlation coefficients between UAV-based VIs and ground truth LAI ranged from 0.64 to 0.89. Furthermore, the fusion of multiple stage data showed that the correlations were significantly higher between ground truth LAI and UAV-based VIs than that of single growth stage data. The ensemble learning algorithm with MLR as the secondary learner outperformed as a single machine learning algorithm with high prediction accuracy R2 = 0.967 and RMSE = 0.198 in 2020, and R2 = 0.897 and RMSE = 0.220 in 2021. We believe that the ensemble learning algorithm based on stacking is preferable to the single machine learning algorithm to build the LAI prediction model. This study can provide certain theoretical guidance for the rapid and precise management of water and fertilizer for large experimental fields.

Growth Dynamics and Yield of Zero-Till Maize as Influenced by Microbial Consortia Treated Paddy Residue Incorporation

A field experiment was conducted at College farm, Professor Jayashankar Telangana State Agricultural University, Rajendranagar, Hyderabad during 2020-21 and 2021-22 to assess the influence of paddy residue management practices at different fertility levels on certain growth parameters and the yield of rabi maize succeeding kharif rice in a strip plot design. The growth analysis indicated that the leaf area index, crop growth rate, net assimilation rate and leaf area duration remained the same with residue management practices at 0-30 DAS and 30-60 DAS but between 60 DAS-harvest, maize leaf area index (LAI), crop growth rate (CGR), net assimilation rate (NAR) and leaf area duration (LAD) was significantly increased by incorporation of crop residues treated with microbial consortia and SSP. Different fertility levels affected leaf area index, crop growth rate and leaf area duration at all the growth intervals whereas net assimilation rate remained unaffected. Interaction effect of residue management practices and fertility levels on leaf area index, crop growth rate, net assimilation rate and leaf area duration was found non significant at 0-30 DAS and 30-60 DAS, while at 60 DAS-harvest integration of residues and microbial consortia along with chemical fertilizers resulted in a significant increase of LAI, CGR and LAD with no significant improvement in NAR. In 2020-21 and 2021-22, the grain yield and straw yield were higher in the incorporation of crop residues treated with microbial consortia and single super phosphate (SSP) in combination with 125 % recommended dose of fertilizers (RDF) which was on par with 100% RDF and 75 % RDF and was significantly superior over other treatments.

Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data

ABSTRACT Maize is an important cereal crop and plays an important role in agriculture. The leaf area index (LAI) and the fraction of absorbed photosynthetically active radiation (FPAR) are important vegetation canopy structure parameters that are closely related to crop photosynthesis, transpiration and respiration and are of great significance for crop yield estimation. Hyperspectral data can obtain the spectral characteristic parameters of crops, and LiDAR data can obtain the vertical structure index. The former focuses on horizontal characteristic information, and the latter focuses on vertical characteristic information. In this paper, random forest method is used to carry out the inversion modelling of maize LAI and FPAR, and the effects of hyperspectral data resolution, LiDAR data density and scanning angle on the inversion accuracy are tested and analysed. The results show that there is good accuracy and stability of the random forest inversion model (LAI: 0.53 <R2 < 0.78, 0.094 < RMSE < 0.158, 0.009 < MSE < 0.025, 0.074 < MAE < 0.136; FPAR: 0.323 < R2 < 0.594, 0.09 < RMSE < 0.261, 0.008 < MSE < 0.068, 0.068 < MAE < 0.212). When using Hyperspectral data to invert LAI, the accuracy is the highest when the resolution is 1 metre (R2 = 0.742, RMSE = 0.141, MSE = 0.02, MAE = 0.119). When using LiDAR data to invert LAI, the accuracy is the highest when the density is 30% (R2 = 0.78, RMSE = 0.115, MSE = 0.013, MAE = 0.094). When using Hyperspectral data to invert FPAR, the accuracy is the highest when the resolution is 0.128 metre(R2 = 0.594, RMSE = 0.09, MSE = 0.008, MAE = 0.068). When using LiDAR data to invert FPAR, the accuracy is the highest when the density is 20% (R2 = 0.525, RMSE = 0.141, MSE = 0.02, MAE = 0.113).

Estimating the maize above-ground biomass by constructing the tridimensional concept model based on UAV-based digital and multi-spectral images

Above-ground biomass (AGB) is an important basis for the formation of crop yield. The accurate estimation of maize AGB based on unmanned aerial vehicle (UAV) images is important for superior varieties selection, field management and maize yield prediction. The previous studies mainly focused on constructing empirical models of AGB by using spectral vegetation indices (VIs), plant height (PH), texture, and is lacked of universality. We conducted the field experiments of maize breeding materials for three years, and obtained UAV digital and multi-spectral images. Considering that the maize AGB before tasseling stage was composed of stem and leaf, we constructed a tridimensional concept model to predict maize AGB coordinated by integrating leaf area index (LAI) and PH, in order to improve the accuracy and universality of UAV data on monitoring maize AGB at multiple growth stages. Firstly, the maize PH was estimated based on the maize canopy height model constructed using the UAV digital images. Secondly, the maize LAI was estimated based on UAV multi-spectrum images and the modified Beer-Lambert law. Finally, the tridimensional concept model of maize AGB was constructed by integrating PH and LAI, and compared with the AGB regression model based on the normalized difference vegetation index (NDVI). The results showed that the maize PH could be estimated well, and the R ², RMSE and rRMSE of the measured and estimated PH were 0.87, 11.17 cm and 16.04% respectively. The LAI could be estimated effectively, and the R ², RMSE, and rRMSE of the sample set were 0.78, 0.49 and 30% respectively. Compared with the maize AGB estimation model based on NDVI ( R ² = 0.79, RMSE = 41.95 g/m², rRMSE = 31.79%), the tridimensional concept model could better estimate the maize AGB ( R ² = 0.82, RMSE = 38.53 g/m², rRMSE = 29.19%). Testing the tridimensional concept model by stand-alone data of 2019 and 2021 years, the accuracy of the AGB estimation model based on the tridimensional concept was much higher than that of the NDVI model. In conclusion，the tridimensional concept model of maize AGB proposed in this study effectively improved the accuracy, stability and universality, which could provide a reference for the estimation of maize AGB by UAV technology at plot scale of the breeding materials.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

Combining Spectral and Texture Features of UAS-Based Multispectral Images for Maize Leaf Area Index Estimation

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.

Exploring the utility of Sentinel-2 for estimating maize chlorophyll content and leaf area index across different growth stages

ABSTRACT This study investigated the utility of Sentinel-2 spectral data for estimating leaf area index (LAI), leaf and canopy chlorophyll content of maize at different growth stages. Vegetation indices based on the visible-near infrared and red-edge regions of the spectrum were computed from Sentinel-2 imagery acquired within one or two days of field data collection. Results showed that green chlorophyll index (CIgreen) and red-edge chlorophyll index (CIred-edge), using the red-edge variant centred at 705 nm, consistently showed higher relationship to maize LAI with r 2 of 0.65 and 0.63 during the early stages of growth, respectively, and an r 2 of 0.79 and 0.81 during tassel stage, respectively. Regarding canopy chlorophyll content the results indicated the spectral advantage of the Sentinel-2 sensor with the presence of two red-edge bands for continuous monitoring of maize chlorophyll content. Overall, the results indicated that maize biophysical variables can be monitored at satellite level using Sentinel-2 data.

Maize leaf functional responses to blending urea and slow-release nitrogen fertilizer under various drip irrigation regimes

The effects of water deficit on maize growth and physiological activities have been extensively studied. However, there are few studies about how various nitrogen (N) fertilizer types alleviate the impact of water stress on maize yield. A two-year field experiment was conducted to explore the effects of N type on maize growth, physiological characteristics and gain yield under various water-stressed conditions in 2019 and 2020. This experiment included three irrigation levels of W1 (60% ETc, ETc is crop evapotranspiration), W2 (80% ETc) and W3 (100% ETc), and three N fertilizer types of urea (U), slow-release nitrogen fertilizer (SRF) and blending U and SRF (UNS). The results showed that W1 and W2 significantly reduced maize leaf area index and leaf N content, which declined maize net photosynthetic rate (Pn), transpiration rate (Tr) and chlorophyll fluorescence, inhibited chlorophyll synthesis and thus reduced maize yield. W3 not only increased maize growth, Pn and Tr, but also enhanced the correlation between Pn, chlorophyll content, chlorophyll fluorescence parameters and leaf area index/leaf N content. The application of UNS alleviated the grain yield reduction associated with water stress. Under W1 (W2), the grain yield in UNS were 3.6–22.1% (10.4–25.0%) and 4.4–24.2% (5.3–15.4%) greater than that in U and SRF, respectively. Although W3SRF obtained the highest yield grain among all the treatments, no significant difference was found between W3UNS and W3SRF. Under the same irrigation amount, UNS increased the water productivity compared with U. In conclusion, UNS could stabilize grain yield by regulating maize leaf functional traits to cope with future climate change and ensure regional food security.

Improving estimation of LAI dynamic by fusion of morphological and vegetation indices based on UAV imagery

As an important indicator reflecting plant growth and canopy structure, accurate and rapid monitoring of the leaf area index (LAI) is very important for modern precision agriculture. The purpose of this study is to explore the potential of fusion of morphological information and spectral information in multiple growth periods of maize to improve the accuracy of LAI dynamic estimation. The multi-spectral sensor carried by the unmanned aerial vehicle (UAV) was used to collect remote sensing images of the maize canopy during the six growth stages. Three morphological parameters (canopy height, canopy coverage, and canopy volume) and two vegetation indices (normalized vegetation index (NDVI) and visible atmospheric vegetation index (VARI)) were extracted from image information and spectral information, respectively, and a LAI estimation model was constructed based on parameters fusion. The results showed that the morphological parameters and vegetation indices had the same time distribution law as LAI, and could be used to monitor crop LAI. At the same time, the study found that the fusion of canopy height, canopy coverage and canopy volume could further characterize the external morphological changes of crops and improved the accuracy of LAI dynamic estimation based on morphology, but there were still limitations in the seedling and milk stages. Furthermore, the fusion of canopy morphological parameters and vegetation indices could further improve the dynamic estimate accuracy of maize LAI, and showed better performance in all growth stages (Seedling stage: Rv2 = 0.688, RMSEP = 0.0493; Jointing stage: Rv2 = 0.860, RMSEP = 0.0847; Tasseling stage: Rv2 = 0.780, RMSEP = 0.1829; Silking stage: Rv2 = 0.794, RMSEP = 0.1981; Blister stage: Rv2 = 0.793, RMSEP = 0.1584; Milk stage: Rv2 = 0.708, RMSEP = 0.1396; All: Rv2 = 0.943, RMSEP = 0.2618). The results show that the fusion of image information and spectral information can improve the estimation accuracy of crop LAI and provide a feasible method for crop growth information monitoring based on UAV platform.

An optimized surface aerodynamic temperature approach to estimate maize sensible heat flux and evapotranspiration

Typical errors for single-source sensible heat flux estimation models range from 15-40% for vegetated surfaces, with 10 to 20% errors introduced by atmosphere stability functions and remaining errors due to the quality of the input data and assumptions regarding the surface aerodynamic temperature (To). This paper describes the development and evaluation of an optimized To model, which serves as key input to estimate and improve sensible heat fluxes for maize fields. The best resulting optimized linear model included four variables: air temperature, surface temperature, fractional vegetation cover, and a proposed new variable that accounts for wind speed and the relative angle of attack between wind direction and crop row orientation. During the 2017 to 2019 maize growing seasons, on two different fields located at a Research Farm near Greeley, Colorado, USA, field data were collected. Micrometeorological data were measured in situ at the height of 3.30 m above the ground surface. Nadir-looking stationary infrared thermometers measured surface temperature. Multispectral surface reflectance data were collected on-site weekly using a handheld multispectral radiometer. Net radiation and soil heat fluxes were acquired at approximately ¼ and ½ of the field's length. Sensible heat fluxes were measured with two sets of Large Aperture Scintillometers to calibrate the To model. A one-way Analysis of Variance was performed to model To for different ranges of maize leaf area index values. A local sensitivity analysis was performed to identify the most influential explanatory variables in the To optimization model. The sensitivity analysis indicated that uncertainty on air temperature and surface temperature measurements were responsible for the most variability in sensible heat flux estimates when using the developed optimized To model. Overall, results indicated that the optimized To model improved the estimation of maize sensible heat fluxes by 31%; which resulted in an improvement on latent heat flux (or evapotranspiration) by 9% compared to a non-optimized linear To model. The interactions between the crop row layout and wind direction promote more turbulent mixing for heat transfer mechanisms.

The effects of crop residues and air temperature on variations in interannual ecosystem respiration in a wheat-maize crop rotation in China

Understanding the mechanisms of interannual variations (IAVs) in cropland respiration (ER) can aid in optimizing field management strategies and accurately assessin bon budget. The long-term carbon exchange over a (winter) wheat-(summer) maize double-cropping system was measured with the eddy covariance technique to identify the IAVs in ER and the influencing factors, and investigate whether field management strategy (crop residue retuning) affects ER. Results showed that the growing season-related ER values for wheat (w-ER) and maize (s-ER) ranged from 471.77 to 681.96 g C m−2 y−1 and from 433.91 to 826.89 g C m−2 y−1, respectively, during 2007–2012. Pearson’s correlation analysis indicated that the IAVs in w-ER were controlled by the crop residual carbon levels for maize from the prior season (s-Cr) and the gross ecosystem productivity. The seasonal mean air temperature (Ta) and maximum leaf area index for maize were found to be factors affecting the IAVs in s-ER. Because of the high correlation between the controlling factors, path analysis was used to deconvolute their respective contributions to the IAVs in ER. Integration analysis of path coefficients showed that s-Cr and Ta were the primary influencing factors, because they explained approximately 81% and 68% of the IAVs in ER for wheat and maize, respectively. Overall, our findings highlighted the importance of crop residue management for agroecosystem carbon cycling, but its effects may depend on ecosystem types.

Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index

Application of multi-layer discrete anisotropic radiative transfer model in vertical distribution inversion of maize leaf area index

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.

Nitrogen Supply Affects Grain Yield by Regulating Antioxidant Enzyme Activity and Photosynthetic Capacity of Maize Plant in the Loess Plateau

Nitrogen (N) is the most limiting nutrient for maize, and appropriate N fertilization can promote maize growth and yield. The effect of N fertilizer rates and timings on morphology, antioxidant enzymes, and grain yield of maize (Zea mays L.) in the Loess Plateau of China was evaluated. The four N levels, i.e., 0 (N0), 100 (N1), 200 (N2), and 300 (N3) kg ha−1, were applied at two timings (T1, one-third N at sowing and two-thirds at the six-leaf stage of maize; T2, one-third applied at sowing, six-leaf stage, and eleven-leaf stage of maize). The results show that N2 and N3 significantly increased the plant height, stem and leaf dry weight, and leaf area index of maize compared with a non-N-fertilized control (N0). The net photosynthetic rate, transpiration rate, stomatal conductance, and leaf chlorophyll contents were lower, while the intercellular carbon dioxide concentration was higher for non-fertilized plants compared to fertilized plants. The activities of peroxidase (POD) and superoxide dismutase (SOD) increased with N rate, but the difference between 200 and 300 kg ha−1 was not significant; further, the isozyme bands of POD and SOD also changed with their activities. Compared with a non-N-fertilized control, N2 and N3 significantly increased grain yield by 2.76- and 3.11-fold in 2018, 2.74- and 2.80-fold in 2019, and 2.71- and 2.89-fold in 2020, and there was no significant difference between N2 and N3. N application timing only affected yield in 2018. In conclusion, 200 kg N ha−1 application increased yield through optimizing the antioxidant enzyme system, increasing photosynthetic capacity, and promoting dry matter accumulation. Further research is necessary to evaluate the response of more cultivars under more seasons to validate the results obtained.