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Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. [ABSTRACT FROM AUTHOR]

The estimation of physicochemical crop parameters based on spectral indices depend strongly on planting year, cultivar, and growing period. Therefore, the efficient monitoring of crop growth and nitrogen (N) fertilizer treatment requires that we develop a generic spectral index that allows the rapid assessment of the plant nitrogen content (PNC) of crops and that is independent of year, cultivar, and growing period. Thus, to obtain the best indicator for estimating potato PNC, herein, we provide an in-depth comparative analysis of the use of hyperspectral single-band reflectance and two- and three-band spectral indices of arbitrary bands for estimating potato PNC over several years and for different cultivars and growth periods. Potato field trials under different N treatments were conducted over the years 2018 and 2019. An unmanned aerial vehicle hyperspectral remote sensing platform was used to acquire canopy reflectance data at several key potato growth periods, and six spectral transformation techniques and 12 arbitrary band combinations were constructed. From these, optimal single-, two-, and three-dimensional spectral indices were selected. Finally, each optimal spectral index was used to estimate potato PNC under different scenarios and the results were systematically evaluated based on a correlation analysis and univariate linear modeling. The results show that, although the spectral transformation technique strengthens the correlation between spectral information and potato PNC, the PNC estimation model constructed based on single-band reflectance is of limited accuracy and stability. In contrast, the optimal three-band spectral index TBI 5 (530,734,514) performs optimally, with coefficients of determination of 0.67 and 0.65, root mean square errors of 0.39 and 0.39, and normalized root mean square errors of 12.64% and 12.17% for the calibration and validation datasets, respectively. The results thus provide a reference for the rapid and efficient monitoring of PNC in large potato fields. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is an important indicator for crop-growth monitoring and yield prediction, and accurate monitoring of AGB is beneficial to agricultural fertilization management and optimization of planting patterns. Imaging spectrometer sensors mounted on unmanned aerial vehicle (UAV) remote-sensing platforms have become an important technical method for monitoring AGB because the method is convenient, rapidly collects data and provides image data with high spatial and spectral resolution. To confirm the feasibility of UAV hyperspectral remote-sensing technology to estimate AGB, this study acquired hyperspectral images and measured AGB data over the potato bud, tuber formation, tuber growth, and starch-storage periods. The canopy spectrum obtained in each growth period was smoothed by using the Savitzky–Golay filtering method, and the spectral-reflection feature parameters, spectral-location feature parameters, and vegetation indexes were extracted. First, a Pearson correlation analysis was performed between the three types of characteristic spectral parameters and AGB, and the spectral parameters that reached a significant level of 0.01 in each growth period were selected. Next, the spectral parameters reaching a significance of 0.01 were optimized and screened by moving window partial least squares (MWPLS), Monte Carlo uninformative variable elimination (MC-UVE), and random frog (RF) methods, and the final model parameters were determined according to the thresholds of the root mean square error of cross-validation (RMSEcv), the reliability index, and the selected probability. Finally, the three optimal characteristic spectral parameters and their combinations were used to estimate the potato AGB in each growth period by combining the partial least squares regression (PLSR) and Gaussian process regression (GPR) methods. The results show that, (i) ranked from high to low, vegetation indexes, spectral-location feature parameters, and spectral-reflection feature parameters in each growth period are correlated with the AGB, and these correlations all first improve and then degrade in going from the budding period to the starch-storage period. (ii) The AGB estimation model based on the characteristic variables screened by the three methods in each growth period is most accurate with RF, less so with MC-UVE, and least accurate with MWPLS. (iii) Estimating the AGB with the same variables combined with the PLSR method in each growth period is more accurate than the corresponding GPR method, but the estimations produced by the two methods both show a trend of first improving and then worsening from the budding period to the starch-accumulation period. The accuracy of the estimation models constructed by PLSR and GPR from high to low is based on comprehensive variables, vegetation indexes, spectral-location feature parameters and spectral-reflection feature parameters. (iv) When combined with the RF-PLSR method to estimate AGB in each growth period, the best R2 values are 0.65, 0.68, 0.72, and 0.67, the corresponding RMSE values are 167.76, 162.98, 160.77, and 169.24 kg/hm2, and the corresponding NRMSE values are 19.76%, 16.01%, 15.04%, and 16.84%. The results of this study show that a variety of characteristic spectral parameters may be extracted from UAV hyperspectral images, that the RF method may be used for optimizing and screening, and that PLSR regression provides accurate estimates of the potato AGB. The proposed approach thus provides a rapid, accurate, and nondestructive way to monitor the growth status of potatoes. [ABSTRACT FROM AUTHOR]

Accurate determination of crop phenology is key to field management and decision making. The existing research on phenology based on remote sensing data is mainly phenology monitoring, which cannot realize the prediction of phenology. In this paper, we propose a method to predict the maturity date (MD) of winter wheat based on a combination of phenology monitoring method and accumulated temperature. The method is divided into three steps. First, 2-band Enhanced Vegetation Index (EVI2) time series data were generated using the moderate-resolution imaging spectroradiometer (MODIS) reflectance data at 8-day intervals; then, the time series were reconstructed using polynomial fitting and the heading date (HD) of winter wheat was extracted using the maximum method. Secondly, the average cumulative temperature required for winter wheat to go from HD to MD was calculated based on historical phenological data and meteorological data. Finally, the timing of winter wheat HD and the current year's Meteorological Data were combined to predict winter wheat MD. The method was used to predict the MD of winter wheat in Hebei in 2018 and was validated with data from the phenology station and the Modis Land Cover Dynamics (MCD12Q2) product. The results showed that the coefficient of determination (R2) for predicting MD using this method was 0.48 and 0.74, the root mean square error (RMSE) was 7.03 and 4.91 days, and Bias was 4.93 and −3.59 days, respectively. In summary, the method is capable of predicting winter wheat MD at the regional scale. [ABSTRACT FROM AUTHOR]

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. [ABSTRACT FROM AUTHOR]

With the increase in the frequency of extreme weather events in recent years, apple growing areas in the Loess Plateau frequently encounter frost during flowering. Accurately assessing the frost loss in orchards during the flowering period is of great significance for optimizing disaster prevention measures, market apple price regulation, agricultural insurance, and government subsidy programs. The previous research on orchard frost disasters is mainly focused on early risk warning. Therefore, to effectively quantify orchard frost loss, this paper proposes a frost loss assessment model constructed using meteorological and remote sensing information and applies this model to the regional-scale assessment of orchard fruit loss after frost. As an example, this article examines a frost event that occurred during the apple flowering period in Luochuan County, Northwestern China, on 17 April 2020. A multivariable linear regression (MLR) model was constructed based on the orchard planting years, the number of flowering days, and the chill accumulation before frost, as well as the minimum temperature and daily temperature difference on the day of frost. Then, the model simulation accuracy was verified using the leave-one-out cross-validation (LOOCV) method, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized root mean square error (NRMSE) were 0.69, 18.76%, and 18.76%, respectively. Additionally, the extended Fourier amplitude sensitivity test (EFAST) method was used for the sensitivity analysis of the model parameters. The results show that the simulated apple orchard fruit number reduction ratio is highly sensitive to the minimum temperature on the day of frost, and the chill accumulation and planting years before the frost, with sensitivity values of ≥0.74, ≥0.25, and ≥0.15, respectively. This research can not only assist governments in optimizing traditional orchard frost prevention measures and market price regulation but can also provide a reference for agricultural insurance companies to formulate plans for compensation after frost. [ABSTRACT FROM AUTHOR]

Rapid and accurate crop aboveground biomass estimation is beneficial for high-throughput phenotyping and site-specific field management. This study explored the utility of high-definition digital images acquired by a low-flying unmanned aerial vehicle (UAV) and ground-based hyperspectral data for improved estimates of winter wheat biomass. To extract fine textures for characterizing the variations in winter wheat canopy structure during growing seasons, we proposed a multiscale texture extraction method (Multiscale_Gabor_GLCM) that took advantages of multiscale Gabor transformation and gray-level co-occurrency matrix (GLCM) analysis. Narrowband normalized difference vegetation indices (NDVIs) involving all possible two-band combinations and continuum removal of red-edge spectra (SpeCR) were also extracted for biomass estimation. Subsequently, non-parametric linear (i.e., partial least squares regression, PLSR) and nonlinear regression (i.e., least squares support vector machine, LSSVM) analyses were conducted using the extracted spectral features, multiscale textural features and combinations thereof. The visualization technique of LSSVM was utilized to select the multiscale textures that contributed most to the biomass estimation for the first time. Compared with the best-performing NDVI (1193, 1222 nm), the SpeCR yielded higher coefficient of determination (R2), lower root mean square error (RMSE), and lower mean absolute error (MAE) for winter wheat biomass estimation and significantly alleviated the saturation problem after biomass exceeded 800 g/m2. The predictive performance of the PLSR and LSSVM regression models based on SpeCR decreased with increasing bandwidths, especially at bandwidths larger than 11 nm. Both the PLSR and LSSVM regression models based on the multiscale textures produced higher accuracies than those based on the single-scale GLCM-based textures. According to the evaluation of variable importance, the texture metrics "Mean" from different scales were determined as the most influential to winter wheat biomass. Using just 10 multiscale textures largely improved predictive performance over using all textures and achieved an accuracy comparable with using SpeCR. The LSSVM regression model based on the combination of the selected multiscale textures, and SpeCR with a bandwidth of 9 nm produced the highest estimation accuracy with R2val = 0.87, RMSEval = 119.76 g/m2, and MAEval = 91.61 g/m2. However, the combination did not significantly improve the estimation accuracy, compared to the use of SpeCR or multiscale textures only. The accuracy of the biomass predicted by the LSSVM regression models was higher than the results of the PLSR models, which demonstrated LSSVM was a potential candidate to characterize winter wheat biomass during multiple growth stages. The study suggests that multiscale textures derived from high-definition UAV-based digital images are competitive with hyperspectral features in predicting winter wheat biomass. [ABSTRACT FROM AUTHOR]

Juiciness is a primary index of pear quality and freshness, which is also considered as important as sweetness for the consumers. Development of a non-destructive detection method for pear juiciness is meaningful for producers and sellers. In this study, visible−near-infrared (VIS/NIR) spectroscopy combined with different spectral preprocessing methods, including normalization (NOR), first derivative (FD), detrend (DET), standard normal variate (SNV), multiplicative scatter correction (MSC), probabilistic quotient normalization (PQN), modified optical path length estimation and correction (OPLECm), linear regression correction combined with spectral ratio (LRC-SR) and orthogonal spatial projection combined with spectral ratio (OPS-SR), was used for comparison in detection of pear juiciness. Partial least squares (PLS) regression was used to establish the calibration models between the preprocessing spectra (650–1100 nm) and juiciness measured by the texture analyzer. In addition, competitive adaptive reweighted sampling (CARS) was used to identify the characteristic wavelengths and simplify the PLS models. All obtained models were evaluated via Monte Carlo cross-validation (MCCV) and external validation. The PLS model established by 19 characteristic variables after LRC-SR preprocessing displayed the best prediction performance with external verification determination coefficient (R2v) of 0.93 and root mean square error (RMSEv) of 0.97%. The results demonstrate that VIS/NIR coupled with LRC-SR method can be a suitable strategy for the quick assessment of juiciness for pears. [ABSTRACT FROM AUTHOR]

The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards. [ABSTRACT FROM AUTHOR]

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content ( C h l a + b ) and water content ( C w )) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of C h l a + b mainly affected the reflectivity of visible region; the vertical profile of C w only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, C h l a + b and C w affected VIs estimation related to LAI, C h l a + b and C w respectively. The Root Mean Square Error (RMSE) of the new-proposed N D V I g r e e n was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers. [ABSTRACT FROM AUTHOR]

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R2 = 1.00) and RE (R2 = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R2 = 0.524) demonstrated higher accuracy than the empirical linear model (R2 = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction. [ABSTRACT FROM AUTHOR]

Yield evaluation of breeding lines is the key to successful release of cultivars, which is becoming a serious issue due to soil heterogeneity in enlarged field tests. This study aimed at establishing plot-yield prediction models using unmanned aerial vehicle (UAV)-based hyperspectral remote sensing for yield-selection in large-scale soybean breeding programs. Three sets of soybean breeding lines (1103 in total) were tested in blocks-in-replication experiments for plot yield and canopy spectral reflectance on 454~950 nm bands at different growth stages using a UAV-based hyperspectral spectrometer (Cubert UHD185 Firefly). The four elements for plot-yield prediction model construction were studied respectively and concluded as: the suitable reflectance-sampling unit-size in a plot was its 20%–80% central part; normalized difference vegetation index (NDVI) and ration vegetation index (RVI) were the best combination of vegetation indices; the initial seed-filling stage (R5) was the best for a single stage prediction, while another was the best combination for a two growth-stage prediction; and multi-variate linear regression was suitable for plot-yield prediction. In model establishment for each material-set, a random half was used for modelling and another half for verification. Twenty-one two growth-stage two vegetation-index prediction models were established and compared for their modelling coefficient of determination (RM2) and root mean square error of the model (RMSEM), verification RV2 and RMSEV, and their sum RS2 and RMSES. Integrated with the coincidence rate between the model predicted and the practical yield-selection results, the models, MA1-2, MA4-2 and MA6-2, with coincidence rates of 56.8%, 58.5% and 52.4%, respectively, were chosen for yield-prediction in yield-test nurseries. The established model construction elements and methods can be used as local models for pre-harvest yield-selection and post-harvest integrated yield-selection in advanced breeding nurseries as well as yield potential prediction in plant-derived-line nurseries. Furthermore, multiple models can be used jointly for plot-yield prediction in soybean breeding programs. [ABSTRACT FROM AUTHOR]

Grain protein content (GPC) is an important indicator of wheat quality. Earlier estimation of wheat GPC based on remote sensing provided effective decision to adapt optimized strategies for grain harvest, which is of great significance for agricultural production. The objectives of this field study are: (i) To assess the ability of spectral vegetation indices (VIs) of Sentinel 2 data to detect the wheat nitrogen (N) attributes related to the grain quality of winter wheat production, and (ii) to examine the accuracy of wheat N status and GPC estimation models based on different VIs and wheat nitrogen parameters across Analytical Spectra Devices (ASD) and Unmanned Aerial Vehicle (UAV) hyper-spectral data-simulated sentinel data and the real Sentinel-2 data. In this study, four nitrogen parameters at the wheat anthesis stage, including plant nitrogen accumulation (PNA), plant nitrogen content (PNC), leaf nitrogen accumulation (LNA), and leaf nitrogen content (LNC), were evaluated for their relationship between spectral parameters and GPC. Then, a multivariate linear regression method was used to establish the wheat nitrogen and GPC estimation model through simulated Sentinel-2A VIs. The coefficients of determination ( R 2 ) of four nitrogen parameter models were all greater than 0.7. The minimum R 2 of the prediction model of wheat GPC constructed by four nitrogen parameters combined with VIs was 0.428 and the highest R 2 was 0.467. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 26.333% to 29.530% when verified by the ground-measured data collected from the Beijing suburbs, and the corresponding nRMSE for the GPC-predicted models ranged from 17.457% to 52.518%. The accuracy of the estimated model was verified by UAV hyper-spectral data which had resized to different spatial resolution collected from the National Experimental Station for Precision Agriculture. The normalized root mean square error (nRMSE) of the four nitrogen estimation models ranged from 16.9% to 37.8%, and the corresponding nRMSE for the GPC-predicted models ranged from 12.3% to 13.2%. The relevant models were also verified by Sentinel-2A data collected in 2018 while the minimum nRMSE for GPC invert model based on PNA was 7.89% and the maximum nRMSE of the GPC model based on LNC was 12.46% in Renqiu district, Hebei province. The nRMSE for the wheat nitrogen estimation model ranged from 23.200% to 42.790% for LNC and PNC. These data demonstrate that freely available Sentinel-2 imagery can be used as an important data source for wheat nutrition and grain quality monitoring. [ABSTRACT FROM AUTHOR]

Accurate and dynamic monitoring of crop nitrogen status is the basis of scientific decisions regarding fertilization. In this study, we compared and analyzed three types of spectral variables: Sensitive spectral bands, the position of spectral features, and typical hyperspectral vegetation indices. First, the Savitzky-Golay technique was used to smooth the original spectrum, following which three types of spectral parameters describing crop spectral characteristics were extracted. Next, the successive projections algorithm (SPA) was adopted to screen out the sensitive variable set from each type of parameters. Finally, partial least squares (PLS) regression and random forest (RF) algorithms were used to comprehensively compare and analyze the performance of different types of spectral variables for estimating corn leaf nitrogen content (LNC). The results show that the integrated variable set composed of the optimal ones screened by SPA from three types of variables had the best performance for LNC estimation by the validation data set, with the values of R2, root means square error (RMSE), and normalized root mean square error (NRMSE) of 0.77, 0.31, and 17.1%, and 0.55, 0.43, and 23.9% from PLS and RF, respectively. It indicates that the PLS model with optimally multitype spectral variables can provide better fits and be a more effective tool for evaluating corn LNC. [ABSTRACT FROM AUTHOR]