玉米作为我国第一大粮食作物，其产量安全对保障国家粮食安全具有重要意义。地 上生物量是评估作物长势的关键指标，和产量预测有着密切联系。为了更加准确地估算 地上生物量，实时有效地分析农情状况，本研究以 2021 至 2023 年多品种多种植密度的 玉米作物为研究对象，获取了地上生物量等实测数据，采集了无人机多光谱影像等遥感 数据，探讨了耦合无人机遥感技术和物候信息构建玉米叶片和茎秆全物候期地上生物量 模型的可行性。

首先利用无人机遥感影像计算了玉米小区冠层光谱和纹理指数，利用激光雷达点云 提取结构信息；其次分析了光谱、纹理和结构特征与玉米茎秆和叶片生物量的相关性， 并进行特征筛选；然后，通过筛选的特征构建了 5 种生物量估算的机器学习模型，评估叶片生物量和茎秆生物量估算的最佳模型；最后，根据每个生育期预测最佳结果，结合物候信息，尝试构建玉米全生育期生物量预测模型。论文主要研究内容及结论如下：

（1）茎粗提取。基于地基激光雷达点云，根据每个点的特征值和特征角度对玉米植株进行茎叶分离，采用 DBSCAN 算法对茎粗进行拟合。拟合茎粗与实测值相比决定系数（coefficient of determination，R²）达到了 0.71，均方根误差（Root Mean Squared Error，RMSE）为 0.99 cm，归一化均方根误差（normalized root mean squared error，nRMSE）为 6.81%。进一步地，将茎粗作为结构参数纳入到茎秆生物量估算模型中，其中 XG Boost 算法的精度结果提高最大，其 R²提升了 0.15，RMSE 减小了15.82 g/m²，nRMSE 减小了 2.76%。整体来说，茎粗对 AGBS 估算模型的精度提升不明显，仅依靠高度参数也能达到相近的效果。

（2）植被指数、纹理指数和结构参数与生物量相关性分析。叶片生物量（Above ground biomass of leaf, AGBL）与所选植被指数（Vegetation index, VIs）的相关系数大部 分均在 0.5 以上，其中叶片叶绿素指数（Leaf Chlorophyll Index, LCI）与 AGBL 的相关性最高，达到了 0.72，而土壤调节植被指数（Soil Adjusted Vegetation Index, SAVI）则与 AGBL 的相关性最低且不显著，部分 VIs 与 AGBL 的相关性较高，相关系数均处于 0.65 以上，对光谱尺度的变化敏感。此外，部分纹理指数（Texture index, TIs）与 AGBL的相关较高，如 R、G、B 三个波段的方差（Variance, Var）、对比度（Contrast, Con）与AGBL 的相关系数均在 0.5 以上。茎秆生物量（Above ground biomass of stem, AGBS）与植被指数相关性稍低。其中，LCI 的相关性最高为 0.59，与 SAVI 的相关性最低，并且部分指数与 AGBS 无显著关系。而基于无人机激光雷达提取的株高、百分位高度等结构参数（Structural parameters, SPs）与 AGBS 的相关系数均在 0.65 以上，相关性较强。

（3）机器学习估算生物量。通过 5 种机器学习算法分别为偏最小二乘模型、随机森林模型、XG Boost 算法、Light GBM 算法、Cat Boost 算法。估算玉米 AGBL 和 AGBS，Cat Boost 算法的估测模型精度较好，其次为随机森林模型。其中，结合 VIs 和 TIs 估算AGBL 的模型精度最佳，在四个关键生育期中模型估测性能均最高，AGBL-VIs-TIs 模型估测精度 R² 范围为 0.65-0.86，RMSE 范围为 9.67-89.42 g/m2，nRMSE 范围为 8.77-13.52%。在 AGBS 估测模型中，结合 VIs 和 SPs 为输入参数的模型，其精度在四个关键生育期效果较好，AGBS-VIs-SPs 模型估测精度 R 2范围为 0.53-0.77，RMSE 范围为 12.18-223.27 g/m²，nRMSE 范围为 11.04-15.88%。结合 VIs、TIs、SPs 估算玉米整体地上生物量，除拔节期之外，模型精度均较差，尤其是灌浆期的估测精度最差，分别估算 AGBL 和 AGBS 的最佳模型精度明显更准确。

（4）全生育期生物量预测模型构建。根据关键生育期模型最佳估测结果，构建了基于生长度日（Growing day degree，GDD）和物候指标（BBCH-Maize）的生物量动态变化曲线，分析了 6 个品种在不同种植密度下的 AGBL 和 AGBS 全生育期曲线的拟合效果。结果显示，基于 GDD 的生物量曲线相对更加收敛，在峰值处呈现平稳状态。除农科糯 336 品种，在其他品种中，基于 GDD 或 BBCH-Maize 的叶片生物量估测结果相差不大。对于农科糯 336 品种，基于 GDD 构建的 AGBL 曲线精度更加精准，R²相对提升了 0.50，RMSE 减小了 48.49g/m²，nRMSE 减小了 16.73%。对于 AGBS，依据 GDD或 BBCH-Maize 的曲线精度相差不大，相对来说基于 GDD 的生物量曲线估测结果精度更佳。本文基于遥感数据估算玉米关键生育期地上生物量，进而构建全生育期生物量曲线，实现了生物量动态变化的定量描述，为田间长势诊断、管理调控和产量预测提供了参考。

As the largest grain crop in China, the yield security of maize is of great significance to guarantee national food security. Aboveground biomass is a key indicator for assessing crop growth and is closely related to yield prediction. In order to estimate aboveground biomass more accurately and analyze the agricultural situation in real time and effectively, this study takes the corn crop with multiple varieties and planting densities from 2021 to 2023 as the research object, acquires ground truth data such as aboveground biomass, plant height, stem thickness, and meteorology, and collects remote sensing data such as ground-based LiDAR, unmanned aerial vehicle (UAV) LiDAR, UAV multispectral, and UAV digital, and discusses the coupling of UAV We explored the feasibility of coupling UAV remote sensing technology and phenology information to construct an aboveground biomass model of maize leaves and stalks during the whole phenology period.

For the critical reproductive period of maize. Firstly, the spectral and textural indices of the canopy of maize plots were calculated using UAV multispectral and digital images, and the structural information was extracted using LiDAR point clouds; secondly, the correlation between the spectral, textural, and structural features and the biomass of maize stalk and leaf was analyzed and feature screening was carried out; and then, five machine learning models for biomass estimation were constructed by the screened features to evaluate the leaf biomass and stalk biomass estimation of the best model; finally, based on the best results of prediction at each growing stage, combined with the phenological information, we tried to construct a biomass prediction model for the whole maize fertility stage. The main contents and conclusions of this paper are as follows:

(1) Stem thickness extraction. Based on the ground-based LiDAR point cloud, stem and leaf separation of maize plants was carried out according to the eigenvalue and eigenangle of each point, and stem thickness was fitted using the DBSCAN algorithm. The coefficient of determination (R²) of the fitted stem thickness compared with the measured value reached 0.71, the root mean squared error (RMSE) was 0.99 cm, the normalized root mean squared error (nRMSE) was 6.81%. Further, incorporating stem thickness as a structural parameter into the stem biomass estimation model, the estimation accuracies of the models were all significantly improved, with the XG Boost algorithm showing the greatest improvement in accuracy results, with its R² improved by 0.15, RMSE reduced by 15.82 g/m², and nRMSE reduced by 2.76%. In general, stem thickness does not significantly improve the accuracy of AGBS estimation model, and similar results can be achieved only by height parameter.

(2) Correlation analysis of vegetation index, texture index and structural parameters with biomass. Most of the correlation coefficients between above ground biomass of leaf (AGBL) and selected vegetation indices (VIs) were above 0.5, among which the Leaf Chlorophyll Index (LCI) of leaf had the highest correlation with AGBL of 0.72, while the Soil Adjusted Vegetation Index (SAVI) had the lowest and non-significant correlation with AGBL, and some of the VIs had high correlations with AGBL, with correlation coefficients of 0.65 or more, which were sensitive to changes in spectral scales. In addition, some Texture indexes (TIs) have higher correlation with AGBL, such as Variance (Var) and Contrast (Con) of the three bands, which have correlation coefficients above 0.5 with AGBL. 

Above ground biomass of stem (AGBS) had a slightly lower correlation with vegetation index. Among them, LCI had the highest correlation of 0.59, the lowest correlation with SAVI, and some indices had no significant relationship with AGBS. On the other hand, the correlation coefficients of Structural parameters (SPs) such as plant height and percentile height extracted based on UAV LiDAR with AGBS were above 0.65, which was a strong correlation.

(3) Machine learning estimation of biomass. Maize AGBL and AGBS were estimated by five machine learning algorithms, namely partial least squares model, random forest model, XG Boost algorithm, Light GBM algorithm, and Cat Boost algorithm, and Cat Boost algorithm had the better estimation model accuracy, followed by random forest model. Among them, the best model accuracy for estimating AGBL by combining VIs and TIs, and the highest model estimation performance in all four key fertility periods, the estimation accuracy of AGBL-VIs-TIs model ranged from 0.65-0.86 R², RMSE ranged from 9.67-89.42 g/m², and nRMSE ranged from 8.77-13.52%. In the AGBS estimation model, the accuracy of the model combining VIs and SPs as input parameters worked better in the four critical fertility periods, and the estimation accuracy of the AGBS-VIs-SPs model ranged from 0.53-0.77 for R², from 12.18-223.27 g/m² for RMSE, and from 11.04-15.88% for nRMSE. Combining VIs, TIs, and SPs to estimate the overall aboveground biomass of maize, the model accuracy was poor except at the nodulation stage, especially the estimation accuracy at the filling stage was the poorest, and the optimal model accuracy for estimating AGBL and AGBS, respectively, was significantly more accurate.

(4) Construction of biomass prediction model for the whole fertility stage. According to the best estimation results of the key fertility period model, the dynamic biomass changeable curves based on the growing day degree (GDD) and the physical weather index (BBCH) were constructed, and the fitting effects of the full-family-life curves of the six varieties under different planting densities were analyzed for AGBL and AGBS. The results showed that the biomass curves based on GDD were relatively more convergent and showed a smooth state at the peak. Except for Nongkenuo 336 variety, the results of leaf biomass estimation based on GDD or BBCH did not differ much among the other varieties. For Nongkenuo 336 variety, the accuracy of the AGBL curve constructed based on GDD was more precise, with a relative improvement of 0.50 in R², a reduction of 48.49 g/m² in RMSE, and a reduction of 16.73% in nRMSE. For AGBS, the accuracy of the curves based on GDD or BBCH did not differ much, and the biomass curve estimation results based on GDD were relatively better in accuracy. In this paper, we estimated the aboveground biomass of maize during the critical fertility period based on remote sensing data, and then constructed the biomass curve during the whole fertility period, realizing the quantitative description of the dynamic changes of biomass, which provided a reference for the diagnosis of field growth, management regulation and yield prediction.

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