水稻作为世界三大作物之一，有着广泛的种植分布和种植历史。在农业智能化发展的新要求下，对水稻生长全周期精细化监测是精准农业的重要环节。本研究基于中国广东省广州市两年水稻氮肥梯度试验，获取了水稻长势参数及品质参数等农学指标，借助遥感技术，分别获取了水稻关键生育期地面非成像冠层高光谱数据、无人机尺度高光谱影像和多光谱影像，将传统农学采样与遥感技术相结合，定量探究了水稻长势参数与冠层光谱间的关系，构建了基于光谱信息的水稻关键生育期多种长势参数的估算模型，得到了长势参数最优估算结果，在此基础上，耦合冠层结构信息、色素信息及空间纹理信息，探究了多源因素与水稻收获后籽粒蛋白含量间的关系，建立了具有一定农学机理的水稻籽粒蛋白含量早期估算模型。研究的内容及主要结论如下：

（1）探究了水稻不同生育期及不同氮素梯度下叶片氮素含量（LNC）、植株氮素积累量（PNA）、相对叶绿素含量（SPAD）、叶面积指数（LAI）、植株地上生物量（AGB）五种长势参数的变化及分布情况，以及不同生育期及不同氮素梯度下，水稻地面冠层光谱及无人机冠层光谱的变化特征。结果显示：水稻各长势参数在不同生育期分布及变化情况存在一定差异，LNC及SPAD在水稻生长初期均处于较高水平，随着生育期推进，LNC逐渐减少，SPAD有一定减少但整体趋于平稳，PNA、LAI、AGB则随着生育期的推进迅速增大。不同氮素等级下大多数长势参数值随着氮素等级的增加而增加，但在过量施氮时存在一定的饱和现象。由地面及无人机平台获取的冠层光谱在同氮素等级下各生育期反射率具有较好的一致性，但在部分时期由于数据获取条件不同而导致光谱存在一定差异，不同氮素等级冠层光谱在近红外区域表现出较为明显的随氮素等级增加反射率也增加的规律。

（2）基于地面冠层光谱及无人机冠层高光谱影像，构建了两波段任意组合光谱指数DVI(λ1,λ2) 、NDVI(λ1,λ2) 和三波段任意组合光谱指数EVI(λ1,λ2,λ3) 、PSRI(λ1,λ2,λ3) ，在二维和三维分布上采用相关系数对长势参数敏感的光谱指数进行了筛选，以多元逐步回归进行二次筛选并构建水稻关键生育期长势参数估算模型，对非成像光谱和成像光谱的长势监测能力进行对比。结果显示：最优原始光谱及最优光谱指数组合的方式能有效完成多种长势参数的估算，基于无人机冠层高光谱影像的估算模型较基于地面冠层光谱的估算模型估算效果更好。分蘖期LNC、PNA、SPAD、LAI、AGB估算模型的决定系数R²（均方根误差RMSE）分别达到0.592（0.177）、0.789（0.200）、0.865（0.647）、0.732（0.080）、0.743（6.940），分化期估算模型R²（RMSE）分别达到0.675（0.195）、0.783（0.704）、0.916（0.958）、0.783（0.410）、0.771（29.211），抽穗期估算模型R²（RMSE）分别达到0.817（0.177）、0.806（1.849）、0.840（1.382）、0.751（0.747）、0.611（111.681）。

    （3）水稻籽粒蛋白含量受多源因素影响，高光谱影像兼顾空间和光谱信息，构建多源参数数据集（长势信息、结构信息、色素信息、空间纹理信息），以此为基础分别构建单参数形式、不同类型参数集和多参数集组合形式的籽粒蛋白含量估算模型。结果显示：以多种线性函数形式构建的籽粒蛋白含量单参数估算模型，各时期估算精度R²达到0.8左右；以偏最小二乘方法构建的基于不同参数集及多参数集组合的籽粒蛋白含量估算模型，能够有效提升估算精度，实现水稻籽粒蛋白含量的早期估算。分蘖期时，估算结果的R²达到0.885，RMSE为0.303；分化期时，估算结果在2019年和2020年R²分别达到0.905、0.880，RMSE为0.283、0.235；抽穗期时，估算结果的R²达到0.941，RMSE为0.217。

Rice, one of the world's three major crops, has a long history of planting and cultivation. In the era of intelligent agricultural development, precision agriculture demands fine monitoring of the entire rice growth cycle. This study conducted a two-year rice nitrogen fertilizer gradient experiment in Guangzhou City, Guangdong Province, China, to obtain agronomic indicators such as rice growth and quality parameters. During the critical period of rice, remote sensing technology was employed to acquire crop canopy hyperspectral data, unmanned aerial multispectral images, and hyperspectral images. The relationship between rice growth parameters and canopy spectra was quantitatively investigated by combining traditional agronomic sampling with remote sensing technology. The study constructed an early estimation model of rice grain protein content with specific agronomic mechanisms using canopy structure information, pigment information, and spatial texture information. The results provide valuable insights into the relationship between multi-source factors and post-harvest rice grain protein content. The contents and main results of the study are as follows:

（1）The changes and distributions of five growth parameters, leaf nitrogen content (LNC), plant nitrogen accumulation (PNA), relative chlorophyll content (SPAD), leaf area index (LAI) and aboveground biomass (AGB), as well as ground canopy spectra and unmanned canopy spectra of rice at different growth stages and nitrogen gradients were investigated. The results showed that there were some differences in the distributions and changes of various growth parameters at different rich growth stages. LNC and SPAD were at high levels at the start of rice growth. LNC gradually decreased as the fertility stage progressed, SPAD reduced somewhat but stabilized overall, and PNA, LAI, and AGB increased rapidly as the growth stage continued. The values of most growth parameters grew as the nitrogen levels rose, but there was a specific saturation phenomenon when too much nitrogen was used. Although there were some differences in the spectra at some points due to the different acquisition conditions, the canopy spectra of varying nitrogen levels showed a more pronounced pattern of increasing reflectance with increasing nitrogen levels in the NIR region. The canopy spectra obtained from the ground and UAV platforms showed good consistency in reflectance at all fertility stages under the same nitrogen level.

(2) Based on ground canopy spectra and UAV canopy spectra, two-band arbitrary combination spectral indices  DVI(λ1,λ2) 、NDVI(λ1,λ2)  and three-band arbitrary combination spectral indices  EVI(λ1,λ2,λ3) 、PSRI(λ1,λ2,λ3)  were constructed. The sensitive spectral indices of growth parameters in two- and three-dimensional distributions were screened using correlation coefficients. The model for estimating rice growth parameters at critical stages was constructed using multivariate stepwise regression. The findings demonstrated that the best raw spectra and spectral indices could be combined to predict various growth parameters accurately and that the estimation model based on UAV canopy spectra was superior to that found on ground canopy spectra. The coefficients of determination (R²) and root mean square error (RMSE) of the estimation models for LNC, PNA, SPAD, LAI and AGB at the tillering stage were 0.592 (0.177), 0.789 (0.200), 0.865 (0.647), 0.732 (0.080) and 0.743 (6.940), respectively. The R² (RMSE) of the estimation models for the panicle initiation stage were 0. 675 (0.195), 0.783 (0.704), 0.916 (0.958), 0.783 (0.410) and 0.771 (29.211), respectively, and the estimated model R² (RMSE) for the heading stage reached 0.817 (0.177), 0.806 (1.849), 0.840 (1.382) and 0.751 (0.747) and 0.611 (111.681), respectively.

      (3) Multiple source factors influence the grain protein content of rice. Hyperspectral images use both spatial and spectral information to construct multi-source parameter data sets (growth information, structure information, pigment information, spatial texture information), from which grain protein content estimation models in the form of a single parameter, different types of parameter sets, and multi-parameter set combinations are developed. Results indicated that the single-parameter estimation model of rice grain protein content constructed in the form of multiple linear functions achieved an estimation accuracy of R² of approximately 0.80 in each period; the estimation model based on different parameter sets and a combination of multi-parameter sets constructed by partial least squares method could effectively improve the estimation accuracy and accomplish the early estimation of rice seed protein content. In the tillering stage, the R² of the estimation results reached 0.885, and the RMSE was 0.303. In the panicle initiation stage in 2019 and 2020, the R² of the estimation results reached 0.905 and 0.880, and the RMSE was 0.283 and 0.235, respectively. Finally, in the heading stage, the R² of the estimation results reached 0.941, and the RMSE was 0.217.

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