智慧农业的发展日益依赖遥感技术与数据智能处理手段，实现对农田中作物长势、物候、产量和生物量的高效监测与智能预测，成为推动粮食安全与智慧农业的关键路径。在大田及多品种育种试验中，作物的长势和物候进程存在一定的差异，导致田间作物生长状态存在一定的空间异质性，这会造成遥感数据中光谱特征的混杂效应，限制了模型的稳定性和预测精度。因此，如何定量监测作物群体长势和物候的均匀度特征，并探索其对建模性能的影响，是产量和生物量预测中要解决的重要问题。

本研究于2021-2023年在南京农业大学白马试验基地（江苏省溧水区，N 31°37′08″，E 119°10′28″）开展了为期两年的田间试验，试验涉及210个小麦栽培品种。研究围绕小麦群体的长势与物候进程的均匀度监测方法及产量和生物量预测展开。首先，本研究基于无人机图像提取农学参数用于计算小麦的长势均匀度指数，并评估了长势均匀度指数的有效性。然后，本研究使用高光谱数据构建了小麦物候期的识别模型，并评估了整个研究区域的物候均匀度（Phenological Uniformity, PU），物候均匀度指研究区域内小麦物候进程的一致性。最后，本研究分析了长势均匀度和物候均匀度对产量和生物量预测精度的影响。主要研究结果如下：

（1）小麦长势均匀度的定量监测及有效性评估

本研究提出了基于无人机图像的小麦长势均匀度定量评估方法。长势均匀度是指作物个体间生理、空间分布、形态结构等方面一致性。研究采用无人机在分蘖期、拔节期、抽穗期、开花期、灌浆早期、灌浆中期和灌浆后期采集小麦的高光谱和RGB图像，并从无人机图像中提取了时间序列的植被覆盖度（Fractional vegetation cover, FVC）、叶面积指数（Leaf area index, LAI）、叶绿素相对含量（Soil and plant analyzer development, SPAD）和冠层高度（Canopy height, CH），用于表征小麦的空间分布、生理状态与形态结构。然后根据本研究提出的基于图像的均匀度评估方法分别计算了四个长势参数的均匀度指数（小区尺度）：方差、变异系数、香农熵、Pielou指数和Alatalo指数，共20个均匀度指数。针对熵值类的均匀度指数难以适用于单一物种的问题，本研究引入分类参数以增强其在作物研究中的适用性。结果表明，当LAI的分类参数小于1，SPAD的分类参数小于20时，对应的熵类均匀度指数表现出较高的有效性；FVC仅有两个类别，分类参数设置为1；而CH相关的均匀度指数对分类参数不敏感。此外，均匀度指数的有效性对图像的空间分辨率较为敏感，空间分辨率为3cm时效果最好。分析各均匀度指数的动态变化趋势发现，除CH的均匀度指数外，从分蘖期至抽穗期均匀度快速升高，抽穗期至灌浆中期均匀度相对稳定，至灌浆后期再度下降。

进一步对20个均匀度指数进行有效性分析，结果表明开花期的LJ（从LAI提取的Pielou指数）与产量（r=–0.760）和生物量（r=–0.801）均具有最强的相关性，高于传统的平均值与产量（r=0.726）和生物量（r=0.754）的相关性。此外，基于产量与生物量对样本进行层次聚类分析，进一步利用LJ指数探讨了210个栽培品种间及年际间的长势均匀度差异。结果显示，小区群体内部的长势均匀度与产量和最终生物量之间具有显著正相关关系，说明均匀度不仅能够反映作物的长势状态，也对产量和生物量具有重要影响。

（2）小麦关键物候期及整个研究区域的物候均匀度的监测

提出了物候均匀度的概念，并定量评估了整个研究区域的物候均匀度。使用无人机在小麦孕穗期至灌浆早期高频率的采集高光谱图像，并在此期间每天下午人工调查所有小区的物候期。基于连续的田间物候调查结果，本研究发现整个试验区域的小麦PU呈波动变化趋势，大多数日期PU值在0.5至0.8之间，抽穗期和开花期的PU峰值约为0.8。随后，将小区的平均光谱作为输入，本研究比较了K近邻（K-nearest neighbors, KNN）、XGBoost（Extreme gradient boosting）、支持向量机（Support vector machine, SVM）、SF（SpectrumFormer）和EMS-GCN（End-to-end mixhop superpixel-based graph convolutional networks）模型对四个关键物候期的分类精度，其中，深度学习模型EMS-GCN的分类效果最好，总体分类精度为86.2%，Kappa系数为0.804，孕穗期、抽穗期、开花期和灌浆早期的分类精度分别为86.4%，84.7%，87.7%和87.4%。

基于EMS-GCN的分类结果，本研究将孕穗期光谱数据作为PLSR（Partial Least Squares Regression, PLSR）模型的输入，分别预测了小麦的抽穗日期（R²=0.883, RMSE=2.012 d, MAE=1.584 d）和开花日期（R²=0.923, RMSE=2.463 d, MAE=1.977 d），平均绝对误差均小于2天。本研究对预测的抽穗日期和开花日期进一步分析发现，抽穗和开花累计百分比达80%（PU=0.8）的日期与实测的日期相差在1-2天。此外，研究进一步探讨了不同PU数据集在开花期预测中的表现，发现预测精度与PU值呈正相关关系，即较高的PU能够有效提升预测精度。上述结果表明，物候均匀度不仅影响表型参数的遥感估算精度，而且本研究提出的物候期分类方法可有效预测PU的峰值日期，为选取最佳数据采集窗口提供了依据。

（3）基于均匀度的产量和生物量预测

本研究通过融合长势均匀度指数和高物候均匀度的高光谱特征，有效提升了产量和生物量预测的精度。本研究基于与产量和生物量相关性最高的四个均匀度指数：FE（从FVC提取的Alatalo指数）、LJ、SJ（从SPAD提取的Pielou指数）和CCV（从CH提取的变异系数）构建了多元线性回归（Multiple linear regression, MLR）模型，并以相应的四个农学参数的平均值构建对照模型进行比较。结果显示，均匀度指数构建的模型在预测精度上优于平均值模型：基于均匀度指数的模型预测精度为产量R²=0.592，RMSE=1.178 t/ha，CV=0.162，生物量R²=0.735，RMSE=2.325 t/ha，CV=0.137；而基于平均值的模型预测精度为产量R²=0.574，RMSE=1.290 t/ha，CV=0.178，生物量R²=0.701，RMSE=2.491 t/ha，CV=0.149。在PU方面，本研究进一步构建了具有不同PU水平的高光谱数据集，用于分析PU对产量和生物量预测精度的影响。结果表明，PU值与预测精度之间存在正相关关系。此外，本研究分析四个物候期的预测精度发现，开花期的预测精度最高。以开花期（PU=0.69）的数据集为例，PLSR模型预测精度为：产量R²=0.643，RMSE=1.248 t/ha，CV=0.175；生物量R²=0.806，RMSE=2.063 t/ha，CV=0.125。而在开花期PU=1的数据集上，预测精度明显提升：产量R²=0.692，RMSE=1.091 t/ha，CV=0.152；生物量R²=0.827，RMSE=1.873 t/ha，CV=0.113。在此基础上，本研究进一步将长势均匀度指数引入PU=1的高光谱数据中作为新增输入特征进行建模，发现模型预测精度进一步提升，增加长势均匀度指数后，产量预测精度为R²=0.708，RMSE=1.084 t/ha，CV=0.151，生物量预测精度为R²=0.847，RMSE=1.782 t/ha，CV=0.105。为了明确各输入特征的贡献，本研究采用RReliefF特征选择算法对高光谱反射率和长势均匀度指数进行重要性分析，结果显示，长势均匀度指数在多个模型中均具有较高的特征权重，表明其在预测模型中发挥了积极作用。综上所述，长势均匀度指数和物候均匀度在提升小麦产量和生物量预测精度方面具有明显的效果，其在高精度农业遥感建模中的应用潜力值得进一步关注和推广。

本研究构建了小麦长势均匀度和物候均匀度的定量监测方法，系统评估了其在产量和生物量遥感预测中的应用价值。结果表明，均匀度明显提升了产量与生物量预测精度，并可用于优化遥感数据采集时机。该方法具备良好的适应性和推广价值，为作物群体监测和精准农业管理提供了新的技术路径和理论支持。

The development of smart agriculture increasingly relies on remote sensing technologies and intelligent data processing methods. Efficient monitoring and intelligent prediction of crop growth status, phenology, yield, and biomass have become key pathways to enhancing food security and advancing smart agricultural practices. In open-field and multi-cultivars breeding trials, crops often exhibit variations in growth and phenological processes, resulting in spatial heterogeneity across the field. This spatial variability introduces mixed spectral effects in remote sensing data, thereby limiting model stability and predictive accuracy. Therefore, quantitatively monitoring the uniformity of crop growth and phenological development, and investigating its impact on modeling performance, is a critical issue in yield and biomass prediction.

From 2021 to 2023, a two-year field experiment was conducted at the Baima Experimental Station of Nanjing Agricultural University (Lishui District, Jiangsu Province, N 31°37′08″, E 119°10′28″), involving 210 wheat cultivars. This study focused on the monitoring of growth and phenological uniformity within wheat populations and their implications for yield and biomass prediction. Firstly, agronomic parameters were extracted from UAV imagery to calculate the wheat growth uniformity indices, and the effectiveness of these indices were evaluated. Secondly, hyperspectral data were used to identify phenological stages of wheat, and the phenological uniformity (PU) across the study area was evaluated. PU refers to the consistency of phenological progression within the research region. Finally, the effects of both growth uniformity and PU on the accuracy of yield and biomass prediction were investigated. The main findings are as follows:

(1) Quantitative Monitoring and Evaluation of Wheat Growth Uniformity

A quantitative assessment method for wheat growth uniformity was developed based on UAV imagery.UAV-based hyperspectral and RGB imagery were collected during key wheat growth stages: tillering, jointing, heading, flowering, early filling, mid-filling, and late filling stage. Four agronomic parameters—fractional vegetation cover (FVC), leaf area index (LAI), soil and plant analyzer development (SPAD), and canopy height (CH)—were extracted to characterize spatial distribution, physiological status, and morphological structure. Based on a proposed image-based uniformity assessment method, five uniformity indices (variance, coefficient of variation, Shannon entropy, Pielou’s index, and Alatalo’s index) were calculated at the plot scale for each parameter, yielding a total of 20 uniformity indices. To address limitations of entropy-based indices for single-species analysis, a classification parameter was introduced to enhance their applicability. The results showed that entropy-based indices were most effective when the classification parameter was set below 1 for LAI and below 20 for SPAD. FVC, with only two value classes, was set at 1, while CH-based indices were insensitive to classification thresholds. Additionally, the effectiveness of uniformity indices was sensitive to image spatial resolution, with 3 cm resolution performing best.

Temporal analysis of the indices revealed that, except for CH, uniformity rose rapidly from tillering to heading stage, remained stable through mid-filling stage, and declined thereafter. Among the 20 indices, the LJ index at flowering stage showed the strongest correlation with yield (r = –0.760) and biomass (r = –0.801), outperforming traditional means (yield: r = 0.726; biomass: r = 0.754). Hierarchical clustering based on yield and biomass further confirmed inter-varietal and inter-annual differences in growth uniformity, indicating a significant positive correlation between population uniformity and final yield and biomass.

(2) Monitoring of Key Wheat Phenological Stages and Phenological Uniformity (PU)

The concept of phenological uniformity (PU) was proposed and quantitatively evaluated across the entire study area. High-frequency UAV hyperspectral imagery was acquired from booting to early grain filling stages, accompanied by daily manual phenological surveys for all plots. The survey revealed fluctuating PU values across the study area, mostly ranging between 0.5 and 0.8, with peak PU (~0.8) observed during heading and flowering stage. Multiple classification models were compared, including K-nearest neighbors (KNN), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), SpectrumFormer (SF), and End-to-End Mixhop Superpixel-Based Graph Convolutional Network (EMS-GCN). EMS-GCN outperformed others, achieving 86.2% overall accuracy and a Kappa coefficient of 0.804. Classification accuracies for booting, heading, flowering, and early filling stage were 86.4%, 84.7%, 87.7%, and 87.4%, respectively.

Based on EMS-GCN results, PLSR (Partial Least Squares Regression) models using booting-stage spectral data successfully predicted heading (R² = 0.883, RMSE = 2.012 d, MAE = 1.584 d) and flowering dates (R² = 0.923, RMSE = 2.463 d, MAE = 1.977 d), with mean absolute errors below 2 days. Dates when cumulative number of heading and flowering plots reached 80% (PU = 0.8) differed by only 1–2 days from field observations. Furthermore, model accuracy during flowering stage was found to increase with higher PU, highlighting PU’s value in improving phenological prediction.

(3) Yield and Biomass Prediction Based on Uniformity Indices

By integrating growth uniformity indices and hyperspectral features with high phenological uniformity, the accuracy of yield and biomass prediction was effectively improved. To evaluate the predictive value of UI in yield and biomass modeling, MLR (Multiple Linear Regression) models were built using the most relevant uniformity indices—FE, LJ, SJ, and CCV—compared to models using mean agronomic values (FM, LM, SM, CM). The UI-based model yielded higher prediction accuracy: yield R² = 0.592, RMSE = 1.178 t/ha, CV = 0.162; biomass R² = 0.735, RMSE = 2.325 t/ha, CV = 0.137. In contrast, the mean-value model yielded lower performance: yield R² = 0.574, RMSE = 1.290 t/ha, CV = 0.178; biomass R² = 0.701, RMSE = 2.491 t/ha, CV = 0.149. Additionally, hyperspectral datasets with varying PU values were constructed to assess PU’s influence on prediction accuracy. Results showed a clear positive correlation. For instance, a dataset with PU = 0.69 at flowering yielded yield R² = 0.643 and biomass R² = 0.806, while PU = 1 resulted in improved performance (yield R² = 0.692, biomass R² = 0.827).

Incorporating UI into the PU = 1 hyperspectral dataset further improved model accuracy: yield R² = 0.708, RMSE = 1.084 t/ha, CV = 0.151; biomass R² = 0.847, RMSE = 1.782 t/ha, CV = 0.105. Feature importance analysis using RReliefF showed that UI features consistently ranked among the top contributors, confirming their predictive value. To clarify the contribution of each input feature, the RReliefF feature selection algorithm was employed to analyze the importance of hyperspectral reflectance and growth uniformity indices. The results indicated that growth uniformity indices consistently exhibited high feature weights across multiple models, suggesting their active role in enhancing model performance. In summary, both the growth uniformity index and phenological uniformity (PU) significantly improved the accuracy of wheat yield and biomass predictions, demonstrating substantial potential for application in high-precision agricultural remote sensing modeling.

This study established a quantitative monitoring framework for wheat growth uniformity and phenological uniformity and systematically evaluated their application value in remote sensing-based prediction. The findings confirmed that incorporating uniformity metrics notably enhanced the prediction accuracy of yield and biomass and can inform the optimal timing of remote sensing data acquisition. The proposed method exhibits strong adaptability and scalability, offering a novel technical pathway and theoretical foundation for crop population monitoring and precision agricultural management.

[1] 陈鹏飞, N. Tremblay, 王纪华，等. 估测作物冠层生物量的新植被指数的研究[J]. 光谱学与光谱分析, 2010, 30, 512-517.

[2] 陈雯. 基于无人机图像的小麦出苗均匀度评价[D]. 扬州: 扬州大学, 2018: 55-62.

[3] 韩少宇. 基于多平台遥感数据的冬小麦长势监测和产量预测[D]. 郑州: 河南农业大学, 2023: 59-74.

[4] 李海涛. 植物种群分布格局研究概况[J]. 植物学通报, 1995, 12(2), 19-26.

[5] 罗传文. 均匀论[M]. 北京: 科学出版社, 2014: 103-115.

[6] 欧阳玲, 毛德华, 王宗明，等. 基于gf-1与landsat8 oli影像的作物种植结构与产量分析[J]. 农业工程学报, 2017, 33, 147-156+316.

[7] 王亚梁, 朱德峰, 陈若霞，等. 杂交稻低播量精准条播育秧机插提高群体均匀度和产量的效应分析[J]. 中国农业科学, 2022, 55, 666-679.

[8] 薛盈文. 小麦群体均匀度的调控效应及机理研究[D]. 北京: 中国农业大学, 2014: 20-28.

[9] 杨利华, 马瑞崑. 植株田间分布均匀度的定义与计算[J]. 玉米科学, 2006, 92-94+96.

[10] Alatalo R V. Problems in the measurement of evenness in ecology[J]. Oikos, 1981, 199-204.

[11] Amarasingam N, Salgadoe A S A, Powell K, et al. A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops[J]. Remote Sensing Applications: Society and Environment, 2022, 26, 100712.

[12] Ang K L-M and Seng J K P. Big data and machine learning with hyperspectral information in agriculture[J]. Institute of Electrical and Electronics Engineers, 2021, 9, 36699-36718.

[13] Aparicio N, Villegas D, Araus J L, et al. Relationship between growth traits and spectral vegetation indices in durum wheat[J]. Crop science, 2002, 42 (5), 1547-1555.

[14] Asrar G Q, Fuchs M, Kanemasu E T, et al. Estimating absorbed photosynthetic radiation and leaf area index from spectral reflectance in wheat 1[J]. Agronomy Journal, 1984, 76 (2), 300-306.

[15] Atzberger C and Rembold F. Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR MDVI time series and neural nets[J]. Remote Sensing, 2013, 5 (3), 1335-1354.

[16] Bannayan M, Crout N M J and Hoogenboom G. Application of the ceres‐wheat model for within‐season prediction of winter wheat yield in the united kingdom[J]. Agronomy Journal, 2003, 95 (1), 114-125.

[17] Baret F, Guyot G and Major D J. Crop biomass evaluation using radiometric measurements[J]. Photogrammetria, 1989, 43 (5), 241-256.

[18] Baret F, Houlès V and Guerif M. Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management[J]. Journal of Experimental Botany, 2007, 58 (4), 869-880.

[19] Baret F, Jacquemoud S, Guyot G, et al. Modeled analysis of the biophysical nature of spectral shifts and comparison with information content of broad bands[J]. Remote Sensing of Environment, 1992, 41 (2-3), 133-142.

[20] Basso B, Liu L and Ritchie J T. A comprehensive review of the ceres-wheat,-maize and-rice models’ performances[J]. Advances in agronomy, 2016, 136, 27-132.

[21] Basso B and Liu L. Seasonal crop yield forecast: Methods, applications, and accuracies[J]. Advances in agronomy, 2019, 154, 201-255.

[22] Bendig J, Yu K, Aasen H, et al. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39, 79-87.

[23] Berra E F and Gaulton R. Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics[J]. Forest Ecology and Management, 2021, 480, 118663.

[24] Berry P M and Spink J. Predicting yield losses caused by lodging in wheat[J]. Field Crops Research, 2012, 137, 19-26.

[25] Blackman G E. Statistical and ecological studies in the distribution of species in plant communities: I. Dispersion as a factor in the study of changes in plant populations[J]. Annals of botany, 1942, 6 (22), 351-370.

[26] Bonfil D J, Karnieli A, Raz M, et al. Decision support system for improving wheat grain quality in the mediterranean area of israel[J]. Field Crops Research, 2004, 89 (1), 153-163.

[27] Bouras E H, Olsson P-O, Thapa S, et al. Wheat yield estimation at high spatial resolution through the assimilation of sentinel-2 data into a crop growth model[J]. Remote Sensing, 2023, 15 (18), 4425.

[28] Bouras E H, Jarlan L, Er-Raki S, et al. Cereal yield forecasting with satellite drought-based indices, weather data and regional climate indices using machine learning in morocco[J]. Remote Sensing, 2021, 13 (16), 3101.

[29] Broge N H and Leblanc E. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density[J]. Remote Sensing of Environment, 2001, 76 (2), 156-172.

[30] Cassman K G. Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture[J]. Proceedings of the National Academy of Sciences, 1999, 96 (11), 5952-5959.

[31] Chapman S C, Merz T, Chan A, et al. Pheno-copter: A low-altitude, autonomous remote-sensing robotic helicopter for high-throughput field-based phenotyping[J]. Agronomy, 2014, 4 (2), 279-301.

[32] Chen K, O'leary R A and Evans F H. A simple and parsimonious generalised additive model for predicting wheat yield in a decision support tool[J]. Agricultural Systems, 2019, 173, 140-150.

[33] Chen P, Haboudane D, Tremblay N, et al. New spectral indicator assessing the efficiency of crop nitrogen treatment in corn and wheat[J]. Remote Sensing of Environment, 2010, 114 (9), 1987-1997.

[34] Chen Q, Zheng B, Chenu K, et al. Unsupervised plot-scale LAI phenotyping via UAV-based imaging, modelling, and machine learning[J]. Plant Phenomics, 2022, 2022, 9768253.

[35] Clark P J and Evans F C. Distance to nearest neighbor as a measure of spatial relationships in populations[J]. Ecology, 1954, 35 (4), 445-453.

[36] Clevers J G P W, De Jong S M, Epema G F, et al. Meris and the red-edge position[J]. International Journal of Applied Earth Observation and Geoinformation, 2001, 3 (4), 313-320.

[37] Colombo R, Meroni M, Marchesi A, et al. Estimation of leaf and canopy water content in poplar plantations by means of hyperspectral indices and inverse modeling[J]. Remote Sensing of Environment, 2008, 112 (4), 1820-1834.

[38] Coyne P I, Aiken R M, Maas S J, et al. Evaluating yieldtracker forecasts for maize in western kansas[J]. Agronomy Journal, 2009, 101 (3), 671-680.

[39] Croft H, Chen J M, Luo X, et al. Leaf chlorophyll content as a proxy for leaf photosynthetic capacity[J]. Global change biology, 2017, 23 (9), 3513-3524.

[40] Croft H, Chen J M and Zhang Y. The applicability of empirical vegetation indices for determining leaf chlorophyll content over different leaf and canopy structures[J]. Ecological Complexity, 2014, 17, 119-130.

[41] Croft H, Chen J M, Wang R, et al. The global distribution of leaf chlorophyll content[J]. Remote Sensing of Environment, 2020, 236, 111479.

[42] Cuaran J and Leon J. Crop monitoring using unmanned aerial vehicles: A review[J]. Agricultural Reviews, 2021, 42 (2), 121-132.

[43] Curran P J, Dungan J L and Gholz H L. Exploring the relationship between reflectance red edge and chlorophyll content in slash pine[J]. Tree physiology, 1990, 7 (1-2-3-4), 33-48.

[44] Darvishzadeh R, Skidmore A, Schlerf M, et al. LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2008, 63 (4), 409-426.

[45] Datt B. Visible/near infrared reflectance and chlorophyll content in eucalyptus leaves[J]. International Journal of Remote Sensing, 1999, 20 (14), 2741-2759.

[46] David F N and Moore P G. Notes on contagious distributions in plant populations[J]. Annals of botany, 1954, 18 (1), 47-53.

[47] De Castro A I, Ehsani R, Ploetz R, et al. Optimum spectral and geometric parameters for early detection of laurel wilt disease in avocado[J]. Remote Sensing of Environment, 2015, 171, 33-44.

[48] Delbart N, Le Toan T, Kergoat L, et al. Remote sensing of spring phenology in boreal regions: A free of snow-effect method using NOAA-AVHRR and SPOT-VGT data (1982–2004)[J]. Remote Sensing of Environment, 2006, 101 (1), 52-62.

[49] Delegido J, Fernandez G, Gandia S, et al. Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques: Application to proba/chris data[J]. International Journal of Remote Sensing, 2008, 29 (24), 7107-7127.

[50] Denny E G, Gerst K L, Miller-Rushing A J, et al. Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications[J]. International Journal of Biometeorology, 2014, 58 (4), 591-601.

[51] Dettori M, Cesaraccio C, Motroni A, et al. Using ceres-wheat to simulate durum wheat production and phenology in southern sardinia, Italy[J]. Field Crops Research, 2011, 120 (1), 179-188.

[52] Dias A S and Lidon F C. Evaluation of grain filling rate and duration in bread and durum wheat, under heat stress after anthesis[J]. Journal of Agronomy and Crop Science, 2009, 195 (2), 137-147.

[53] Dietz K J, Zörb C and Geilfus C M. Drought and crop yield[J]. Plant Biology, 2021, 23 (6), 881-893.

[54] Ding Y, Zhang H, Zhao K, et al. Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the prosail model[J]. International Journal of Remote Sensing, 2017, 38 (14), 4206-4223.

[55] Dong J, Lu H, Wang Y, et al. Estimating winter wheat yield based on a light use efficiency model and wheat variety data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160, 18-32.

[56] Dorigo W A, Zurita-Milla R, De Wit A J W, et al. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling[J]. International Journal of Applied Earth Observation and Geoinformation, 2007, 9 (2), 165-193.

[57] Duchemin B, Maisongrande P, Boulet G, et al. A simple algorithm for yield estimates: Evaluation for semi-arid irrigated winter wheat monitored with green leaf area index[J]. Environmental Modelling & Software, 2008, 23 (7), 876-892.

[58] Dudewicz E J and Van Der Meulen E C. Entropy-based tests of uniformity[J]. Journal of the American Statistical Association, 1981, 76 (376), 967-974.

[59] Durbha S S, King R L and Younan N H. Support vector machines regression for retrieval of leaf area index from multiangle imaging spectroradiometer[J]. Remote Sensing of Environment, 2007, 107 (1-2), 348-361.

[60] Duveiller G and Defourny P. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing[J]. Remote Sensing of Environment, 2010, 114 (11), 2637-2650.

[61] Ehrlén J and Münzbergová Z. Timing of flowering: Opposed selection on different fitness components and trait covariation[J]. The American Naturalist, 2009, 173 (6), 819-830.

[62] Eitel J U H, Gessler P E, Smith A M S, et al. Suitability of existing and novel spectral indices to remotely detect water stress in populus spp[J]. Forest Ecology and Management, 2006, 229 (1), 170-182.

[63] Fan J, Zhou J, Wang B, et al. Estimation of maize yield and flowering time using multi-temporal UAV-based hyperspectral data[J]. Remote Sensing, 2022, 14 (13), 3052.

[64] Feilhauer H, Asner G P and Martin R E. Multi-method ensemble selection of spectral bands related to leaf biochemistry[J]. Remote Sensing of Environment, 2015, 164, 57-65.

[65] Feng A, Zhang M, Sudduth K A, et al. Cotton yield estimation from UAV-based plant height[J]. Transactions of the American Society of Agricultural and Biological Engineers, 2019, 62 (2), 393-404.

[66] Feng H, Tao H, Fan Y, et al. Comparison of winter wheat yield estimation based on near-surface hyperspectral and UAV hyperspectral remote sensing data[J]. Remote Sensing, 2022, 14 (17).

[67] Féret J-B, François C, Gitelson A, et al. Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling[J]. Remote Sensing of Environment, 2011, 115 (10), 2742-2750.

[68] Flohr B M, Hunt J R, Kirkegaard J A, et al. Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia[J]. Field Crops Research, 2017, 209, 108-119.

[69] Fotso Kamga G A, Bitjoka L, Akram T, et al. Advancements in satellite image classification: Methodologies, techniques, approaches and applications[J]. International Journal of Remote Sensing, 2021, 42 (20), 7662-7722.

[70] Fu Y, Huang J, Shen Y, et al. A satellite-based method for national winter wheat yield estimating in China[J]. Remote Sensing, 2021, 13 (22), 4680.

[71] Fu Y, Yang G, Wang J, et al. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements[J]. Computers and Electronics in Agriculture, 2014, 100, 51-59.

[72] Gamon J A, Peñuelas J and Field C B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency[J]. Remote Sensing of Environment, 1992, 41 (1), 35-44.

[73] Gamuyao R, Chin J H, Pariasca-Tanaka J, et al. The protein kinase pstol1 from traditional rice confers tolerance of phosphorus deficiency[J]. Nature, 2012, 488 (7412), 535-539.

[74] Gao M, Yang F, Wei H, et al. Automatic monitoring of maize seedling growth using unmanned aerial vehicle-based rgb imagery[J]. Remote Sensing, 2023, 15 (14), 3671.

[75] Gaso D V, Berger A G and Ciganda V S. Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with landsat images[J]. Computers and Electronics in Agriculture, 2019, 159, 75-83.

[76] Geipel J, Link J and Claupein W. Combined spectral and spatial modeling of corn yield based on aerial images and crop surface models acquired with an unmanned aircraft system[J]. Remote Sensing, 2014, 6 (11), 10335-10355.

[77] Gitelson A A, Peng Y, Arkebauer T J, et al. Relationships between gross primary production, green LAI, and canopy chlorophyll content in maize: Implications for remote sensing of primary production[J]. Remote Sensing of Environment, 2014, 144, 65-72.

[78] Gitelson A A, Keydan G P and Merzlyak M N. Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves[J]. Geophysical research letters, 2006, 33 (11).

[79] Gitelson A A, Gritz † Y and Merzlyak M N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves[J]. Journal of plant physiology, 2003, 160 (3), 271-282.

[80] Gitelson A A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation[J]. Journal of plant physiology, 2004, 161 (2), 165-173.

[81] Gitelson A A, Kaufman Y J and Merzlyak M N. Use of a green channel in remote sensing of global vegetation from EOS-MODIS[J]. Remote Sensing of Environment, 1996, 58 (3), 289-298.

[82] Gómez D, Salvador P, Sanz J, et al. Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in mexico[J]. Agricultural and Forest Meteorology, 2021, 300, 108317.

[83] Grover A, Sabat S C and Mohanty P. Relative sensitivity of various spectral forms of photosynthetic pigments to leaf senescence in wheat (triticum aestivum l.)[J]. Excitation Energy and Electron Transfer in Photosynthesis: Dedicated to Warren L. Butler, 1987, 77-83.

[84] Gu X, Wang Y, Sun Q, et al. Hyperspectral inversion of soil organic matter content in cultivated land based on wavelet transform[J]. Computers and Electronics in Agriculture, 2019, 167, 105053.

[85] Guo H, Jiapaer G, Bao A, et al. Effects of the tarim river’s middle stream water transport dike on the fractional cover of desert riparian vegetation[J]. Ecological Engineering, 2017, 99, 333-342.

[86] Guo L, An N and Wang K. Reconciling the discrepancy in ground‐and satellite‐observed trends in the spring phenology of winter wheat in China from 1993 to 2008[J]. Journal of Geophysical Research: Atmospheres, 2016, 121 (3), 1027-1042.

[87] Gupta R K, Vijayan D and Prasad T S. New hyperspectral vegetation characterization parameters[J]. Advances in Space Research, 2001, 28 (1), 201-206.

[88] Gutman G and Ignatov A. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models[J]. International Journal of Remote Sensing, 1998, 19 (8), 1533-1543.

[89] Haboudane D, Miller J R, Pattey E, et al. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture[J]. Remote Sensing of Environment, 2004, 90 (3), 337-352.

[90] Hämmerle M and Höfle B. Direct derivation of maize plant and crop height from low-cost time-of-flight camera measurements[J]. Plant Methods, 2016, 12 (1), 50.

[91] Han J, Zhang Z, Cao J, et al. Prediction of winter wheat yield based on multi-source data and machine learning in China[J]. Remote Sensing, 2020, 12 (2).

[92] Harremoes P and Vajda I. On the bahadur-efficient testing of uniformity by means of the entropy[J]. IEEE Transactions on Information Theory, 2008, 54 (1), 321-331.

[93] Hassan M A, Yang M, Fu L, et al. Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat[J]. Plant Methods, 2019, 15 (1), 37.

[94] Hassan N M, Ebeed H T and Ahmed H S. Exogenous application of the polyamine spermine delays natural leaf senescence in wheat through protecting chlorophyll from degradation and preventing oxidative stress[J]. Scientific Journal for Damietta Faculty of Science, 2021, 11 (1), 38-46.

[95] Heip C. A new index measuring evenness[J]. Journal of the Marine Biological Association of the United Kingdom, 1974, 54 (3), 555-557.

[96] Hill M J, Zhou Q, Sun Q, et al. Relationships between vegetation indices, fractional cover retrievals and the structure and composition of brazilian cerrado natural vegetation[J]. International Journal of Remote Sensing, 2017, 38 (3), 874-905.

[97] Holgate P. Some new tests of randomness[J]. The Journal of Ecology, 1965, 261-266.

[98] Holman F H, Riche A B, Michalski A, et al. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing[J]. Remote Sensing, 2016, 8 (12).

[99] Hong D, Han Z, Yao J, et al. Spectralformer: Rethinking hyperspectral image classification with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60, 1-15.

[100] Hoogenboom G, Porter C H, Boote K J, et al. The dssat crop modeling ecosystem[J]. Advances in crop modelling for a sustainable agriculture, 2019, 173-216.

[101] Horie T and Nakagawa H. Modelling and prediction of developmental process in rice: I. Structure and method of parameter estimation of a model for simulating developmental process toward heading[J]. Japanese Journal of Crop Science, 1990, 59 (4), 687-695.

[102] Houles V, Guerif M and Mary B. Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations[J]. European Journal of Agronomy, 2007, 27 (1), 1-11.

[103] Hsiao T C, Heng L, Steduto P, et al. Aquacrop—the fao crop model to simulate yield response to water: III. Parameterization and testing for maize[J]. Agronomy Journal, 2009, 101 (3), 448-459.

[104] Hu P, Chapman S C, Wang X, et al. Estimation of plant height using a high throughput phenotyping platform based on unmanned aerial vehicle and self-calibration: Example for sorghum breeding[J]. European Journal of Agronomy, 2018, 95, 24-32.

[105] Huang J, Gómez-Dans J L, Huang H, et al. Assimilation of remote sensing into crop growth models: Current status and perspectives[J]. Agricultural and Forest Meteorology, 2019, 276, 107609.

[106] Huang L, Liu Y, Huang W, et al. Combining random forest and xgboost methods in detecting early and mid-term winter wheat stripe rust using canopy level hyperspectral measurements[J]. Agriculture, 2022, 12 (1), 74.

[107] Huang X, Zhu W, Wang X, et al. A method for monitoring and forecasting the heading and flowering dates of winter wheat combining satellite-derived green-up dates and accumulated temperature[J]. Remote Sensing, 2020, 12 (21), 3536.

[108] Huete A, Didan K, Miura T, et al. Overview of the radiometric and biophysical performance of the modis vegetation indices[J]. Remote Sensing of Environment, 2002, 83 (1-2), 195-213.

[109] Huete A R. A soil-adjusted vegetation index (SAVI)[J]. Remote Sensing of Environment, 1988, 25 (3), 295-309.

[110] Hussain J, Khaliq T, Ahmad A, et al. Performance of four crop model for simulations of wheat phenology, leaf growth, biomass and yield across planting dates[J]. PloS one, 2018, 13 (6), e0197546.

[111] Inoue Y, Guérif M, Baret F, et al. Simple and robust methods for remote sensing of canopy chlorophyll content: A comparative analysis of hyperspectral data for different types of vegetation[J]. Wiley Online Library, 2016, 39, 2609-2623.

[112] Jacquemoud S and Baret F. Prospect: A model of leaf optical properties spectra[J]. Remote Sensing of Environment, 1990, 34 (2), 75-91.

[113] Jahromi M N, Zand-Parsa S, Razzaghi F, et al. Developing machine learning models for wheat yield prediction using ground-based data, satellite-based actual evapotranspiration and vegetation indices[J]. European Journal of Agronomy, 2023, 146, 126820.

[114] Jamieson P D, Semenov M A, Brooking I R, et al. Sirius: A mechanistic model of wheat response to environmental variation[J]. European Journal of Agronomy, 1998, 8 (3-4), 161-179.

[115] Jiang Q, Fang S, Peng Y, et al. UAV-based biomass estimation for rice-combining spectral, tin-based structural and meteorological features[J]. Remote Sensing, 2019, 11 (7), 890.

[116] Jiang T, Dou Z, Liu J, et al. Simulating the influences of soil water stress on leaf expansion and senescence of winter wheat[J]. Agricultural and Forest Meteorology, 2020, 291, 108061.

[117] Jiang Z, Huete A R, Didan K, et al. Development of a two-band enhanced vegetation index without a blue band[J]. Remote Sensing of Environment, 2008, 112 (10), 3833-3845.

[118] Jiapaer G, Chen X and Bao A. A comparison of methods for estimating fractional vegetation cover in arid regions[J]. Agricultural and Forest Meteorology, 2011, 151 (12), 1698-1710.

[119] Jin S, Zhang F, Zheng Y, et al. Csknn: Cost-sensitive K-nearest neighbor using hyperspectral imaging for identification of wheat varieties[J]. Computers and Electrical Engineering, 2023, 111, 108896.

[120] Jin X, Li Z, Feng H, et al. Estimation of maize yield by assimilating biomass and canopy cover derived from hyperspectral data into the aquacrop model[J]. Agricultural Water Management, 2020, 227, 105846.

[121] Johnson M D, Hsieh W W, Cannon A J, et al. Crop yield forecasting on the canadian prairies by remotely sensed vegetation indices and machine learning methods[J]. Agricultural and Forest Meteorology, 2016, 218, 74-84.

[122] Kanning M, Kühling I, Trautz D, et al. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction[J]. Remote Sensing, 2018, 10 (12).

[123] Karami A, Tabib Mahmoudi F and Sharifi A. Spectral features fusion of effective criteria on wheat yield prediction[J]. Journal of Food and Bioprocess Engineering, 2022, 5 (2), 109-114.

[124] Katal N, Rzanny M, Mäder P, et al. Deep learning in plant phenological research: A systematic literature review[J]. Frontiers in Plant Science, 2022, 13, 805738.

[125] Kawakita S, Takahashi H and Moriya K. Prediction and parameter uncertainty for winter wheat phenology models depend on model and parameterization method differences[J]. Agricultural and Forest Meteorology, 2020, 290, 107998.

[126] Kayad A, Rodrigues Jr F A, Naranjo S, et al. Radiative transfer model inversion using high-resolution hyperspectral airborne imagery–retrieving maize LAI to access biomass and grain yield[J]. Field Crops Research, 2022, 282, 108449.

[127] Kershaw K A. The detection of pattern and association[J]. The Journal of Ecology, 1960, 233-242.

[128] Khan M J, Razzaq A, Khattak M K, et al. Effect of different pre-sowing water application depths on wheat yield under spate irrigation in dera ismael khan district of pakistan[J]. Agricultural Water Management, 2009, 96 (10), 1467-1474.

[129] Khan Z, Chopin J, Cai J, et al. Quantitative estimation of wheat phenotyping traits using ground and aerial imagery[J]. Remote Sensing, 2018, 10 (6).

[130] Khush G S. Green revolution: The way forward[J]. Nature reviews genetics, 2001, 2 (10), 815-822.

[131] Kimura R, Okada S, Miura H, et al. Relationships among the leaf area index, moisture availability, and spectral reflectance in an upland rice field[J]. Agricultural Water Management, 2004, 69 (2), 83-100.

[132] Kipf T N and Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.

[133] Kira K and Rendell L A. A practical approach to feature selection[J]. Machine Learning Proceedings, 1992, 249-256.

[134] Kira O, Linker R and Gitelson A. Non-destructive estimation of foliar chlorophyll and carotenoid contents: Focus on informative spectral bands[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 38, 251-260.

[135] Küçük Ç, Taşkın G and Erten E. Paddy-rice phenology classification based on machine-learning methods using multitemporal co-polar x-band sar images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9 (6), 2509-2519.

[136] Kümmerer R, Noack P O and Bauer B. Using high-resolution UAV imaging to measure canopy height of diverse cover crops and predict biomass[J]. Remote Sensing, 2023, 15 (6), 1520.

[137] Le Maire G, François C, Soudani K, et al. Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass[J]. Remote Sensing of Environment, 2008, 112 (10), 3846-3864.

[138] Li B, Xu X, Zhang L, et al. Above-ground biomass estimation and yield prediction in potato by using UAV-based rgb and hyperspectral imaging[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020a, 162, 161-172.

[139] Li D, Chen J M, Zhang X, et al. Improved estimation of leaf chlorophyll content of row crops from canopy reflectance spectra through minimizing canopy structural effects and optimizing off-noon observation time[J]. Remote Sensing of Environment, 2020b, 248, 111985.

[140] Li D, Tian L, Wan Z, et al. Assessment of unified models for estimating leaf chlorophyll content across directional-hemispherical reflectance and bidirectional reflectance spectra[J]. Remote Sensing of Environment, 2019, 231, 111240.

[141] Li F, Chen W, Zeng Y, et al. Improving estimates of grassland fractional vegetation cover based on a pixel dichotomy model: A case study in inner mongolia, China[J]. Remote Sensing, 2014, 6 (6), 4705-4722.

[142] Li J, Li Y, Lin N, et al. Ammoniated straw returning: A win-win strategy for increasing crop production and soil carbon sequestration[J]. Agriculture, Ecosystems & Environment, 2024, 363, 108879.

[143] Li W, Niu Z, Chen H, et al. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system[J]. Ecological Indicators, 2016, 67, 637-648.

[144] Li W, Niu Z, Huang N, et al. Airborne lidar technique for estimating biomass components of maize: A case study in zhangye city, northwest China[J]. Ecological Indicators, 2015, 57, 486-496.

[145] Li Z, Zhao Y, Taylor J, et al. Comparison and transferability of thermal, temporal and phenological-based in-season predictions of above-ground biomass in wheat crops from proximal crop reflectance data[J]. Remote Sensing of Environment, 2022, 273, 112967.

[146] Li Z, Taylor J, Yang H, et al. A hierarchical interannual wheat yield and grain protein prediction model using spectral vegetative indices and meteorological data[J]. Field Crops Research, 2020c, 248, 107711.

[147] Liang B, Liu S, Qu Y, et al. Estimating fractional vegetation cover using the hand-held laser range finder: Method and validation[J]. Remote Sensing Letters, 2015, 6 (1), 20-28.

[148] Lin W, Zhang F-C, Jing Y-S, et al. Multi-temporal detection of rice phenological stages using canopy spectrum[J]. Rice Science, 2014, 21 (2), 108-115.

[149] Liu B, Yu X, Yu A, et al. Deep few-shot learning for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 57 (4), 2290-2304.

[150] Liu D-Z, Yang F-F and Liu S-P. Estimating wheat fractional vegetation cover using a density peak K-means algorithm based on hyperspectral image data[J]. Journal of Integrative Agriculture, 2021, 20 (11), 2880-2891.

[151] Liu T, Li R, Jin X, et al. Evaluation of seed emergence uniformity of mechanically sown wheat with UAV RGB imagery[J]. Remote Sensing, 2017, 9 (12), 1241.

[152] Liu X, Ma Q, Wu X, et al. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds[J]. Remote Sensing of Environment, 2022, 282, 113280.

[153] Liu Y, Mu X, Wang H, et al. A novel method for extracting green fractional vegetation cover from digital images[J]. Journal of Vegetation Science, 2012, 23 (3), 406-418.

[154] Liu Y, Feng H, Yue J, et al. Improving potato agb estimation to mitigate phenological stage impacts through depth features from hyperspectral data[J]. Computers and Electronics in Agriculture, 2024a, 219, 108808.

[155] Lloyd M. Mean crowding[J]. The Journal of Animal Ecology, 1967, 36 (1), 1-30.

[156] Longchamps L and Philpot W. Full-season crop phenology monitoring using two-dimensional normalized difference pairs[J]. Remote Sensing, 2023, 15 (23).

[157] Lu P, Jiang B and Weiner J. Crop spatial uniformity, yield and weed suppression[J]. Advances in agronomy, 2020, 161, 117-178.

[158] Lu Y, Zhang X, Chen S, et al. Increasing the planting uniformity improves the yield of summer maize[J]. Agronomy Journal, 2017, 109 (4), 1463-1475.

[159] Luo C, Yi C, Wang G, et al. The mathematical description of uniformity and related theorems[J]. Chaos, Solitons & Fractals, 2009, 42 (5), 2748-2753.

[160] Lyu M, Lu X, Shen Y, et al. UAV time-series imagery with novel machine learning to estimate heading dates of rice accessions for breeding[J]. Agricultural and Forest Meteorology, 2023a, 341, 109646.

[161] Ma J, Wang L and Chen P. Comparing different methods for wheat LAI inversion based on hyperspectral data[J]. Agriculture, 2022, 12 (9).

[162] Macarthur R. Fluctuations of animal populations and a measure of community stability[J]. Ecology, 1955, 36 (3), 533-536.

[163] Maccioni A, Agati G and Mazzinghi P. New vegetation indices for remote measurement of chlorophylls based on leaf directional reflectance spectra[J]. Journal of Photochemistry and Photobiology B: Biology, 2001, 61 (1), 52-61.

[164] Magney T S, Eitel J U H, Huggins D R, et al. Proximal MDVI derived phenology improves in-season predictions of wheat quantity and quality[J]. Agricultural and Forest Meteorology, 2016, 217, 46-60.

[165] Malambo L, Popescu S C, Murray S C, et al. Multitemporal field-based plant height estimation using 3d point clouds generated from small unmanned aerial systems high-resolution imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 64, 31-42.

[166] Manafifard M and Huang J. A comprehensive review on wheat yield prediction based on remote sensing[J]. Multimedia Tools and Applications, 2024, 1-74.

[167] Manivasagam V S and Rozenstein O. Practices for upscaling crop simulation models from field scale to large regions[J]. Computers and Electronics in Agriculture, 2020, 175, 105554.

[168] Mao B, Cheng Q, Chen L, et al. Multi-random ensemble on partial least squares regression to predict wheat yield and its losses across water and nitrogen stress with hyperspectral remote sensing[J]. Computers and Electronics in Agriculture, 2024, 222, 109046.

[169] Marshak A, Knyazikhin Y, Davis A B, et al. Cloud‐vegetation interaction: Use of normalized difference cloud index for estimation of cloud optical thickness[J]. Geophysical research letters, 2000, 27 (12), 1695-1698.

[170] Martínez-Moreno F, Ammar K and Solís I. Global changes in cultivated area and breeding activities of durum wheat from 1800 to date: A historical review[J]. Agronomy, 2022, 12 (5), 1135.

[171] Mccown R L, Hammer G L, Hargreaves J N G, et al. Apsim: A novel software system for model development, model testing and simulation in agricultural systems research[J]. Agricultural Systems, 1996, 50 (3), 255-271.

[172] Mcintosh R P. An index of diversity and the relation of certain concepts to diversity[J]. Ecology, 1967, 48 (3), 392-404.

[173] Meng X-S, Bai H, Zhang Q, et al. Simultaneous determination of 15 nitroimidazoles in cosmetics by hplc coupled with electrospray ionization-tandem mass spectrometry[J]. Journal of AOAC INTERNATIONAL, 2014, 97 (6), 1538-1545.

[174] Mlambo R, Woodhouse I H, Gerard F, et al. Structure from motion (SfM) photogrammetry with drone data: A low cost method for monitoring greenhouse gas emissions from forests in developing countries[J]. Forests, 2017, 8 (3).

[175] Mohamed M S, Barakat H M, Alyami S A, et al. Cumulative residual tsallis entropy-based test of uniformity and some new findings[J]. Mathematics, 2022, 10 (5), 771.

[176] Moharana S and Dutta S. Spatial variability of chlorophyll and nitrogen content of rice from hyperspectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122, 17-29.

[177] Morisette J T, Richardson A D, Knapp A K, et al. Tracking the rhythm of the seasons in the face of global change: Phenological research in the 21st century[J]. Frontiers in Ecology and the Environment, 2009, 7 (5), 253-260.

[178] Mu X, Song W, Gao Z, et al. Fractional vegetation cover estimation by using multi-angle vegetation index[J]. Remote Sensing of Environment, 2018, 216, 44-56.

[179] Mueller N D, Gerber J S, Johnston M, et al. Closing yield gaps through nutrient and water management[J]. Nature, 2012, 490 (7419), 254-257.

[180] Nagai S, Nasahara K N, Muraoka H, et al. Field experiments to test the use of the normalized-difference vegetation index for phenology detection[J]. Agricultural and Forest Meteorology, 2010, 150 (2), 152-160.

[181] Naghdyzadegan Jahromi M, Razzaghi F and Zand-Parsa S. Strategies to increase barley production and water use efficiency by combining deficit irrigation and nitrogen fertilizer[J]. Irrigation Science, 2023, 41 (2), 261-275.

[182] Naqvi S, Tahir M N, Shah G A, et al. Remote estimation of wheat yield based on vegetation indices derived from time series data of landsat 8 imagery[J]. Applied Ecology & Environmental Research, 2019, 17 (2).

[183] Nordt B, Hensen I, Bucher S F, et al. The phenobs initiative: A standardised protocol for monitoring phenological responses to climate change using herbaceous plant species in botanical gardens[J]. Functional Ecology, 2021, 35 (4), 821-834.

[184] Okin G S. Relative spectral mixture analysis—a multitemporal index of total vegetation cover[J]. Remote Sensing of Environment, 2007, 106 (4), 467-479.

[185] Olesen J E and Bindi M. Consequences of climate change for european agricultural productivity, land use and policy[J]. European Journal of Agronomy, 2002, 16 (4), 239-262.

[186] Olgun M, Onarcan A O, Özkan K, et al. Wheat grain classification by using dense sift features with svm classifier[J]. Computers and Electronics in Agriculture, 2016, 122, 185-190.

[187] Ollinger S V. Sources of variability in canopy reflectance and the convergent properties of plants[J]. New Phytologist, 2011, 189 (2), 375-394.

[188] Olsen J, Kristensen L, Weiner J, et al. Increased density and spatial uniformity increase weed suppression by spring wheat[J]. Weed research, 2005, 45 (4), 316-321.

[189] Palosuo T, Kersebaum K C, Angulo C, et al. Simulation of winter wheat yield and its variability in different climates of europe: A comparison of eight crop growth models[J]. European Journal of Agronomy, 2011, 35 (3), 103-114.

[190] Pan D, Li C, Yang G, et al. Identification of the initial anthesis of soybean varieties based on UAV multispectral time-series images[J]. Remote Sensing, 2023, 15 (22), 5413.

[191] Pan Z, Huang J, Zhou Q, et al. Mapping crop phenology using MDVI time-series derived from hj-1 a/b data[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 34, 188-197.

[192] Pandey B R, Burton W A, Salisbury P A, et al. Non-destructive measurement of canopy cover is an alternative to biomass sampling at anthesis to predict yield of canola-quality'brassica juncea'[J]. Australian Journal of Crop Science, 2016, 10 (4), 482-489.

[193] Pantazi X E, Moshou D, Alexandridis T, et al. Wheat yield prediction using machine learning and advanced sensing techniques[J]. Computers and Electronics in Agriculture, 2016, 121, 57-65.

[194] Pielou E C. An introduction to mathematical ecology. New York, USA, Wiley-Inter-science1969.

[195] Pimstein A, Eitel J U H, Long D S, et al. A spectral index to monitor the head-emergence of wheat in semi-arid conditions[J]. Field Crops Research, 2009, 111 (3), 218-225.

[196] Piper E L, Boote K J, Jones J W, et al. Comparison of two phenology models for predicting flowering and maturity date of soybean[J]. Crop science, 1996, 36 (6), 1606-1614.

[197] Prasad A K, Chai L, Singh R P, et al. Crop yield estimation model for iowa using remote sensing and surface parameters[J]. International Journal of Applied Earth Observation and Geoinformation, 2006, 8 (1), 26-33.

[198] Psiroukis V, Papadopoulos G, Kasimati A, et al. Cotton growth modelling using uas-derived dsm and RGB imagery[J]. Remote Sensing, 2023, 15 (5), 1214.

[199] Pu R, Gong P, Biging G S, et al. Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41 (4), 916-921.

[200] Quemada M, Gabriel J L and Zarco-Tejada P. Airborne hyperspectral images and ground-level optical sensors as assessment tools for maize nitrogen fertilization[J]. Remote Sensing, 2014, 6 (4), 2940-2962.

[201] Richardson A J and Wiegand C L. Distinguishing vegetation from soil background information[J]. Photogrammetric engineering and remote sensing, 1977, 43 (12), 1541-1552.

[202] Rodene E, Xu G, Palali Delen S, et al. A UAV‐based high‐throughput phenotyping approach to assess time‐series nitrogen responses and identify trait‐associated genetic components in maize[J]. The Plant Phenome Journal, 2022, 5 (1), e20030.

[203] Rondeaux G, Steven M and Baret F. Optimization of soil-adjusted vegetation indices[J]. Remote Sensing of Environment, 1996, 55 (2), 95-107.

[204] Ryan J C, Hubbard A L, Box J E, et al. UAV photogrammetry and structure from motion to assess calving dynamics at store glacier, a large outlet draining the greenland ice sheet[J]. The Cryosphere, 2015, 9 (1), 1-11.

[205] Sadeghi-Tehran P, Sabermanesh K, Virlet N, et al. Automated method to determine two critical growth stages of wheat: Heading and flowering[J]. Frontiers in Plant Science, 2017, 8, 252.

[206] Saini P, Singh C, Kumar P, et al. Breeding for ideotype designing[J]. Classical and Molecular Approaches in Plant Breeding. Narendra Publishing House, New Delhi, 2020.

[207] Sakamoto T, Yokozawa M, Toritani H, et al. A crop phenology detection method using time-series modis data[J]. Remote Sensing of Environment, 2005, 96 (3-4), 366-374.

[208] Schreiber L V, Atkinson Amorim J G, Guimarães L, et al. Above-ground biomass wheat estimation: Deep learning with UAV-based RGB images[J]. Applied Artificial Intelligence, 2022, 36 (1), 2055392.

[209] Shannon C E. A mathematical theory of communication[J]. The Bell system technical journal, 1948, 27 (3), 379-423.

[210] Sheldon A L. Equitability indices: Dependence on the species count[J]. Ecology, 1969, 50 (3), 466-467.

[211] Sherwin W B and Prat I Fornells N. The introduction of entropy and information methods to ecology by ramon margalef[J]. Entropy, 2019, 21 (8), 794.

[212] Shi W, Wang M and Liu Y. Crop yield and production responses to climate disasters in China[J]. Science of The Total Environment, 2021, 750, 141147.

[213] Shirzadifar A, Maharlooei M, Bajwa S G, et al. Mapping crop stand count and planting uniformity using high resolution imagery in a maize crop[J]. Biosystems Engineering, 2020, 200, 377-390.

[214] Shu C, Li F, Liu D, et al. Heading uniformity: A new comprehensive indicator of rice population quality[J]. Agriculture, 2021, 11 (8), 770.

[215] Shu M, Fei S, Zhang B, et al. Application of UAV multisensor data and ensemble approach for high-throughput estimation of maize phenotyping traits[J]. Plant Phenomics, 2022, 2022, 9802585.

[216] Simpson E H. Measurement of diversity[J]. Nature, 1949, 163.

[217] Sims D A and Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages[J]. Remote Sensing of Environment, 2002, 81 (2-3), 337-354.

[218] Singhal G, Bansod B, Mathew L, et al. Estimation of leaf chlorophyll concentration in turmeric (curcuma longa) using high-resolution unmanned aerial vehicle imagery based on kernel ridge regression[J]. Journal of the Indian Society of Remote Sensing, 2019, 47 (7), 1111-1122.

[219] Skellam J G. Studies in statistical ecology: I. Spatial pattern[J]. Biometrika, 1952, 39 (3/4), 346-362.

[220] Smith M W, Carrivick J L and Quincey D J. Structure from motion photogrammetry in physical geography[J]. Progress in physical geography, 2016, 40 (2), 247-275.

[221] Son H, Kim C, Kim C, et al. Prediction of government-owned building energy consumption based on an rrelieff and support vector machine model[J]. Journal of Civil Engineering and Management, 2015, 21 (6), 748-760.

[222] Song Y, Wang J and Shan B. Estimation of winter wheat yield from UAV-based multi-temporal imagery using crop allometric relationship and safy model[J]. Drones, 2021, 5 (3).

[223] Soudani K and François C. A green illusion[J]. Nature, 2014, 506 (7487), 165-166.

[224] Stanton C, Starek M J, Elliott N, et al. Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment[J]. Journal of Applied Remote Sensing, 2017, 11 (2), 026035-026035.

[225] Sun S, Li C and Paterson A H. In-field high-throughput phenotyping of cotton plant height using lidar[J]. Remote Sensing, 2017, 9 (4).

[226] Sytar O, Brestic M, Zivcak M, et al. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance[J]. Science of The Total Environment, 2017, 578, 90-99.

[227] Szulc P, Bocianowski J, Nowosad K, et al. The dynamics of a dry matter accumulation in the initial period of growth of four varieties of the „stay-green” type of maize (zea mays l.)[J]. Pak. J. Bot, 2017, 49 (3), 1017-1022.

[228] Tanabe R, Matsui T and Tanaka T S T. Winter wheat yield prediction using convolutional neural networks and UAV-based multispectral imagery[J]. Field Crops Research, 2023, 291, 108786.

[229] Tanaka Y, Watanabe T, Katsura K, et al. Deep learning-based estimation of rice yield using RGB image[J]. 2021.

[230] Taniguchi S, Sakamoto T, Imase R, et al. Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice[J]. Frontiers in Plant Science, 2022, 13, 998803.

[231] Timsina J and Connor D J. Productivity and management of rice–wheat cropping systems: Issues and challenges[J]. Field Crops Research, 2001, 69 (2), 93-132.

[232] Tits L, Somers B, Stuckens J, et al. Integration of in situ measured soil status and remotely sensed hyperspectral data to improve plant production system monitoring: Concept, perspectives and limitations[J]. Remote Sensing of Environment, 2013, 128, 197-211.

[233] Tu H-M, Fan M-W and Ko J C-J. Different habitat types affect bird richness and evenness[J]. Scientific Reports, 2020, 10 (1), 1221.

[234] Tucker C J. Red and photographic infrared linear combinations for monitoring vegetation[J]. Remote Sensing of Environment, 1979, 8 (2), 127-150.

[235] Tuğaç M G, Özbayoğlu A M, Torunlar H, et al. Wheat yield prediction with machine learning based on modis and landsat MDVI data at field scale[J]. International Journal of Environment and Geoinformatics, 2022, 9 (4), 172-184.

[236] Uno Y, Prasher S O, Lacroix R, et al. Artificial neural networks to predict corn yield from compact airborne spectrographic imager data[J]. Computers and Electronics in Agriculture, 2005, 47 (2), 149-161.

[237] Van Diepen C A, Wolf J, Van Keulen H, et al. Wofost: A simulation model of crop production[J]. Soil Use and Management, 1989, 5 (1), 16-24.

[238] Velusamy P, Rajendran S, Mahendran R K, et al. Unmanned aerial vehicles (UAV) in precision agriculture: Applications and challenges[J]. Energies, 2021, 15 (1), 217.

[239] Viljevac Vuletić M and Španić V. Special issue in honour of prof. Reto j. Strasser–characterization of photosynthetic performance during natural leaf senescence in winter wheat: Multivariate analysis as a tool for phenotypic characterization[J]. Photosynthetica, 2020, 58 (SPECIAL ISSUE), 301-313.

[240] Vogelmann J E, Rock B N and Moss D M. Red edge spectral measurements from sugar maple leaves[J]. Remote Sensing, 1993, 14 (8), 1563-1575.

[241] Vong C N, Conway L S, Feng A, et al. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning[J]. Computers and Electronics in Agriculture, 2022, 198, 107008.

[242] Wahabzada M, Mahlein A-K, Bauckhage C, et al. Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants[J]. Scientific Reports, 2016, 6 (1), 22482.

[243] Walter J, Edwards J, Mcdonald G, et al. Photogrammetry for the estimation of wheat biomass and harvest index[J]. Field Crops Research, 2018, 216, 165-174.

[244] Wang B, Gu F, Hu Z, et al. Analysis and evaluation of influencing factors on uniform sowing of wheat with wide seed belt after sowing and soil throwing device[J]. Agriculture, 2022, 12 (9), 1455.

[245] Wang E and Engel T. Simulation of phenological development of wheat crops[J]. Agricultural Systems, 1998, 58 (1), 1-24.

[246] Wang J, Zhou Q, Shang J, et al. UAV-and machine learning-based retrieval of wheat spad values at the overwintering stage for variety screening[J]. Remote Sensing, 2021, 13 (24), 5166.

[247] Wang Q, Chen X, Meng H, et al. UAV hyperspectral data combined with machine learning for winter wheat canopy spad values estimation[J]. Remote Sensing, 2023a, 15 (19), 4658.

[248] Wang X, Zhang R, Song W, et al. Dynamic plant height qtl revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)[J]. Scientific Reports, 2019, 9 (1), 3458.

[249] Watanabe K, Guo W, Arai K, et al. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling[J]. Frontiers in Plant Science, 2017, 8.

[250] Weiner J, Griepentrog H-W and Kristensen L. Suppression of weeds by spring wheat triticum aestivum increases with crop density and spatial uniformity[J]. Journal of applied ecology, 2001, 784-790.

[251] Whittaker R H. Dominance and diversity in land plant communities: Numerical relations of species express the importance of competition in community function and evolution[J]. Science, 1965, 147 (3655), 250-260.

[252] Wu J, Bai T and Li X. Inverting chlorophyll content in jujube leaves using a back-propagation neural network–random forest–ridge regression algorithm with combined hyperspectral data and image color channels[J]. Agronomy, 2024, 14 (1), 140.

[253] Xiang X and Solaymani S. Change in cereal production caused by climate change in malaysia[J]. Ecological Informatics, 2022, 70, 101741.

[254] Xie C and Yang C. A review on plant high-throughput phenotyping traits using UAV-based sensors[J]. Computers and Electronics in Agriculture, 2020, 178, 105731.

[255] Xie Q, Dash J, Huang W, et al. Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (5), 1482-1493.

[256] Xie Q, Mayes S and Sparkes D L. Preanthesis biomass accumulation of plant and plant organs defines yield components in wheat[J]. European Journal of Agronomy, 2016, 81, 15-26.

[257] Xu C, Li R, Song W, et al. High density and uniform plant distribution improve soybean yield by regulating population uniformity and canopy light interception[J]. Agronomy, 2021a, 11 (9), 1880.

[258] Xu M, Liu R, Chen J M, et al. Retrieving leaf chlorophyll content using a matrix-based vegetation index combination approach[J]. Remote Sensing of Environment, 2019, 224, 60-73.

[259] Xu X, Nie C, Jin X, et al. A comprehensive yield evaluation indicator based on an improved fuzzy comprehensive evaluation method and hyperspectral data[J]. Field Crops Research, 2021b, 270, 108204.

[260] Yamaguchi T and Katsura K. A novel neural network model to achieve generality for diverse morphologies and crop science interpretability in rice biomass estimation[J]. Computers and Electronics in Agriculture, 2024, 218, 108653.

[261] Yang F, Liu Y, Yan J, et al. Winter wheat yield estimation with color index fusion texture feature[J]. Agriculture, 2024, 14 (4), 581.

[262] Yang Q, Shi L, Han J, et al. A near real-time deep learning approach for detecting rice phenology based on UAV images[J]. Agricultural and Forest Meteorology, 2020, 287, 107938.

[263] Yang X, Yang R, Ye Y, et al. Winter wheat spad estimation from UAV hyperspectral data using cluster-regression methods[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 105.

[264] Yang Z, Li K, Liu L, et al. Rice growth monitoring using simulated compact polarimetric c band SAR[J]. Radio Science, 2014, 49 (12), 1300-1315.

[265] Yao X, Zhu Y, Tian Y, et al. Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat[J]. International Journal of Applied Earth Observation and Geoinformation, 2010, 12 (2), 89-100.

[266] Ye X, Sakai K, Okamoto H, et al. A ground-based hyperspectral imaging system for characterizing vegetation spectral features[J]. Computers and Electronics in Agriculture, 2008, 63 (1), 13-21.

[267] Yin X, Mcclure M A, Jaja N, et al. In‐season prediction of corn yield using plant height under major production systems[J]. Agronomy Journal, 2011, 103 (3), 923-929.

[268] Yuan J, Zhang Y, Zheng Z, et al. Grain crop yield prediction using machine learning based on UAV remote sensing: A systematic literature review[J]. Drones, 2024, 8 (10), 559.

[269] Yuan W, Li J, Bhatta M, et al. Wheat height estimation using Lidar in comparison to ultrasonic sensor and uas[J]. Sensors, 2018, 18 (11).

[270] Yuan W, Chen Y, Xia J, et al. Estimating crop yield using a satellite-based light use efficiency model[J]. Ecological Indicators, 2016, 60, 702-709.

[271] Yue J, Yang G, Li C, et al. Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models[J]. Remote Sensing, 2017, 9 (7), 708.

[272] Yue J, Zhou C, Guo W, et al. Estimation of winter-wheat above-ground biomass using the wavelet analysis of unmanned aerial vehicle-based digital images and hyperspectral crop canopy images[J]. International Journal of Remote Sensing, 2021, 42 (5), 1602-1622.

[273] Zając T, Synowiec A, Oleksy A, et al. Accumulation of biomass and bioenergy in culms of cereals as a factor of straw cutting height[J]. International Agrophysics, 2017, 31 (2), 273-285.

[274] Zarco‐Tejada P J, Ustin S L and Whiting M L. Temporal and spatial relationships between within‐field yield variability in cotton and high‐spatial hyperspectral remote sensing imagery[J]. Agronomy Journal, 2005, 97 (3), 641-653.

[275] Zeng L, Wardlow B D, Wang R, et al. A hybrid approach for detecting corn and soybean phenology with time-series modis data[J]. Remote Sensing of Environment, 2016, 181, 237-250.

[276] Zhang H, Zou J and Zhang L. Ems-gcn: An end-to-end mixhop superpixel-based graph convolutional network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, 5526116.

[277] Zhang L, Wang A, Zhang H, et al. Estimating leaf chlorophyll content of winter wheat from UAV multispectral images using machine learning algorithms under different species, growth stages, and nitrogen stress conditions[J]. Agriculture, 2024a, 14 (7).

[278] Zhang R, Jin S, Zhang Y, et al. Phenonet: A two-stage lightweight deep learning framework for real-time wheat phenophase classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024b, 208, 136-157.

[279] Zhang X, Friedl M A, Schaaf C B, et al. Monitoring vegetation phenology using modis[J]. Remote Sensing of Environment, 2003, 84 (3), 471-475.

[280] Zhang Y, Hui J, Qin Q, et al. Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data[J]. Remote Sensing of Environment, 2021, 267, 112724.

[281] Zhang Z, Li Z, Chen Y, et al. Improving regional wheat yields estimations by multi-step-assimilating of a crop model with multi-source data[J]. Agricultural and Forest Meteorology, 2020, 290, 107993.

[282] Zhao L, Guo W, Wang J, et al. An efficient method for estimating wheat heading dates using UAV images[J]. Remote Sensing, 2021, 13 (16), 3067.

[283] Zheng C, Wang N and Cui J. Hyperspectral image classification with small training sample size using superpixel-guided training sample enlargement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019a, 57 (10), 7307-7316.

[284] Zheng H, Cheng T, Zhou M, et al. Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery[J]. Precision Agriculture, 2019b, 20, 611-629.

[285] Zheng H, Cheng T, Yao X, et al. Detection of rice phenology through time series analysis of ground-based spectral index data[J]. Field Crops Research, 2016, 198, 131-139.

[286] Zhou M, Ma X, Wang K, et al. Detection of phenology using an improved shape model on time-series vegetation index in wheat[J]. Computers and Electronics in Agriculture, 2020, 173, 105398.

[287] Zhou M, Zheng H, He C, et al. Wheat phenology detection with the methodology of classification based on the time-series UAV images[J]. Field Crops Research, 2023a, 292, 108798.

[288] Zhou Q, Robson M and Pilesjo P. On the ground estimation of vegetation cover in australian rangelands[J]. International Journal of Remote Sensing, 1998, 19 (9), 1815-1820.

[289] Zhou Q, Guo W, Chen N, et al. Analyzing nitrogen effects on rice panicle development by panicle detection and time-series tracking[J]. Plant Phenomics, 2023b, 5, 0048.

[290] Zhou X, Zhu X, Dong Z, et al. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data[J]. The Crop Journal, 2016, 4 (3), 212-219.

[291] Zhou X, Kono Y, Win A, et al. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches[J]. Plant Production Science, 2021, 24 (2), 137-151.

[292] Zwart S J and Bastiaanssen W G M. Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize[J]. Agricultural Water Management, 2004, 69 (2), 115-133.

[293] Acreche M M, Briceño-Félix G, Sánchez J a M, et al. Radiation interception and use efficiency as affected by breeding in mediterranean wheat[J]. Field Crops Research, 2009, 110 (2), 91-97.

[294] Aranguren M, Castellón A and Aizpurua A. Wheat yield estimation with MDVI values using a proximal sensing tool[J]. Remote Sensing, 2020, 12 (17), 2749.

[295] Calderini D F and Reynolds M P. Changes in grain weight as a consequence of de-graining treatments at pre-and post-anthesis in synthetic hexaploid lines of wheat (triticum durum x t. Tauschii)[J]. Functional Plant Biology, 2000, 27 (3), 183-191.

[296] Fan Y, Feng H, Yue J, et al. Comparison of different dimensional spectral indices for estimating nitrogen content of potato plants over multiple growth periods[J]. Remote Sensing, 2023, 15 (3).

[297] Fei S, Hassan M A, Xiao Y, et al. UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat[J]. Precision Agriculture, 2023, 24 (1), 187-212.

[298] Fischer R A. The effect of duration of the vegetative phase in irrigated semi-dwarf spring wheat on phenology, growth and potential yield across sowing dates at low latitude[J]. Field Crops Research, 2016, 198, 188-199.

[299] Kamir E, Waldner F and Hochman Z. Estimating wheat yields in australia using climate records, satellite image time series and machine learning methods[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 160, 124-135.

[300] Liu Y, Feng H, Fan Y, et al. Improving potato above ground biomass estimation combining hyperspectral data and harmonic decomposition techniques[J]. Computers and Electronics in Agriculture, 2024b, 218, 108699.

[301] Lyu X, Du W, Zhang H, et al. Classification of different winter wheat cultivars on hyperspectral UAV imagery[J]. Applied Sciences, 2023b, 14 (1), 250.

[302] Montesinos-López O A, Montesinos-López A, Crossa J, et al. Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data[J]. Plant Methods, 2017, 13, 1-23.

[303] Shen Y, Mercatoris B, Cao Z, et al. Improving wheat yield prediction accuracy using LSTM-RF framework based on UAV thermal infrared and multispectral imagery[J]. Agriculture, 2022, 12 (6), 892.

[304] Suprayogi Y, Clarke J M, Bueckert R, et al. Nitrogen remobilization and post-anthesis nitrogen uptake in relation to elevated grain protein concentration in durum wheat[J]. Canadian Journal of Plant Science, 2011, 91 (2), 273-282.

[305] Varela S, Pederson T, Bernacchi C J, et al. Understanding growth dynamics and yield prediction of sorghum using high temporal resolution UAV imagery time series and machine learning[J]. Remote Sensing, 2021, 13 (9).

[306] Wang W, Xu R, Wei R, et al. Effects of different pressures and laying lengths of micro-sprinkling hose irrigation on irrigation uniformity and yield of spring wheat[J]. Agricultural Water Management, 2023b, 288, 108495.

[307] Wittstruck L, Jarmer T, Trautz D, et al. Estimating LAI from winter wheat using UAV data and cnns[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 1-5.

[308] Yue J, Feng H, Tian Q, et al. A robust spectral angle index for remotely assessing soybean canopy chlorophyll content in different growing stages[J]. Plant Methods, 2020, 16 (1), 104.

[309] Zeng L, Peng G, Meng R, et al. Wheat yield prediction based on unmanned aerial vehicles-collected red–green–blue imagery[J]. Remote Sensing, 2021, 13 (15), 2937.