随着社会生产力的发展，在现实需求和技术发展的双重推动下，智慧农田管理系统开始建立。智慧农田管理系统的建立为田块尺度的农作物精确管理提供新的解决途径。农田矢量边界是智慧农田管理系统建立的基础和前提条件。然而，目前小型田块边界的获取均由手动数字化完成，这种方式费时费力、且易受主观因素影响，已不能满足智慧农田管理系统建立的现实需求。所以，寻求一种高效、准确、自动化的农田边界提取方法已经成为智慧农田管理系统建立过程中亟待解决的关键问题。农田边界自动化提取技术可以为智慧农田管理系统的建立提供基础数据，而以遥感技术为支撑的田块尺度作物长势无损监测技术则是智慧农田管理系统建立的核心。如何运用卫星影像更加精确、快速地获取田块尺度的农作物长势信息已经成为智慧农田管理系统建立过程中必须要解决的基础问题。本文立足于智慧农田管理系统建立的现实需求，分别围绕农田边界自动化提取及田块尺度水稻生物量估测这两方面开展相关研究，其研究结果可直接服务于智慧农田管理系统的建立，并为相关研究提供强有力的理论和技术支撑。 在农田边界自动化提取部分，我们以高空间分辨率卫星影像为基础数据源，将WorldView-2影像的空间特征与Planet影像的物候学特征相融合，通过1）边缘检测；2）图像分割；3）基于物候学的农田对象判别；4）形态学后处理四个步骤，最终实现了农田边界快速、自动化提取。结果表明，基于WorldView-2影像的空间信息及Planet影像的物候学信息可以想成有效地互补，使得农田对象识别精度分别提高了6.24%~11.31%，Kappa系数分别降低了0.13~0.22。农田对象的总体识别精度分别在94.98%~98.84%之间，Kappa系数在0.90~0.98之间，农田对象识别精度较高。利用“整地期”这物候期的光谱特征可以实现农田对象的快速准确提取，且该特征非常稳定，应用前景广阔。通过将WorldView-2影像的空间信息及Planet影像的物候学信息相结合的方法我们可以实现对小型田块边界的快速、准确、自动化提取；该方法仅需两个普适性阈值即可实现农田对象的自动化判别，且对高分影像的要求较低，极具推广价值。所提取的农田边界与实测边界基本重合，且绝大部分保持一对一的关系，农田边界提取精度较高。在定量评价方面，80%以上的田块欠分割率（S_j^under）和过分割率（S_j^over）低于20%，所提取的农田边界与实测边界重合度高；85%以上田块匹配度（S_j^local）高于80%，农田边界定位精准。 在水稻地上部生物量估测部分，我们立足于农田提取的结果，聚焦于AGB估测的三个重要问题：1）多生态区水稻AGB估算模型构建；2）机器学习算法在水稻AGB估测中的性能比较；3）面向对象的影像分析技术在水稻AGB估测中的应用潜力。对WorldView-2影像在田块尺度农作物长势监测方面的应用价值进行深入地挖掘。研究表明，从不同生态点卫星影像提取的水稻像元存在明显的光谱差异，经过直方图匹配之后，不同影像之间的系统性光谱差异消失，基于光谱指数EVI与CIred-edge的跨生态点AGB估测模型精度得到明显提升，决定系数分别从最初的0.06和0.53提升至0.52和0.68。在所选用的6种回归算法中，随机森林算法（Random Forest, RF）的估测精度最高（R2 = 0.92，RMSE = 0.52 t/ha，RE = 8%），且其计算效率相对较高；偏最小二乘算法（Partial Least Squares Regression, PLSR）虽然水稻AGB估测精度相对较低（R2 = 0.71，RMSE = 0.97 t/ha，RE = 16%），但其在计算效率方面拥有明显的优势，其计算效率为其他机器学习算法的18~1296倍，这两种方法在水稻AGB估测中具有重要应用价值。与传统的基于像素影像分析方法相比，面向对象的图像分析技术可以进一步提升水稻AGB的估测精度，这种提升在PLSR算法中表现尤为明显，R2由原来的0.72提升至0.80，RMSE和RE分别下降至0.80t/ha 和13%；除此之外，面向对象参数回归算法的运算效率是基于像素参数回归算法的12倍以上；同时，面向对象图像分析技术的水稻AGB空间分布图，更符合农作物田间管理的现实需求，表明该技术在作物精确管理领域应用潜力巨大。

With the development of social productivity, the smart farmland management system began to be established. The establishment of a smart farm management system provides new opportunities for the precision crops management at the field level. Farmland vector boundary is the basis and prerequisite for the establishment of the smart farmland management system. However, the processing of delineating small field boundaries is manual, which is time-consuming and laborious, and arbitrary. This ways cannot meet the demand of precision crop management. Therefore, setting up a new farmland boundary delineation method is vital to smart farmland management system. Otherwise, the non-destructive crops monitoring technology at the field scale supported by remote sensing technology is the core of the establishment of the smart farmland management system. How to use satellite imagery to obtain crop growth information more accurately and quickly at fieldscale has become the top priority in the establishment of smart farmland management system. Therefore, based on the actual demand of the smart farmland management system, this paper fouses on two important issues: 1) the farmland boundaries automated delineation; and 2) rice aboveground biomass estimation at the field scale. The research results can be directly applied to the smart farmland management system. In addition, this research can provides strong theoretical and technical support for related research. In part of farmland boundaries automated delineation, we set up a new method for the delineation of farmland boundaries with spatial and phenological information derived from high resolution imagery. This method consists of the following four steps: 1) Edge detection; 2) Image segmentation; 3) Identifing farmland objects based on phenology; 4) Morphological post-processing.The results show that the spatial information based on WorldView-2 image and the phenological information of Planet image can be effectively complemented each other, which makes the overall identification accuracy of farmland objects increase by 6.24%~11.31% and Kappa coefficient by 0.13~0.22, respectively. Otherwise, the overall identification accuracy of farmland objects is between 94.98% and 98.84%, and the Kappa coefficient is between 0.90 and 0.98. This way is effective in term of objects indentifing. In addition, we notice that it is easy to indentify farmland objects automatically by using the spectral characteristics of the phenology phase of the “soil preperation” and the feature is very stable and has broad application prospects. By combining the spatial information derived from WorldView-2 imagery with the phenological information derived from Planet images, we can delineate small field boundaries efficiently, accurately and automatically; In particular, this method can work well only with two universal thresholds, and the requirements for high-resolution images are low. Additionally, the delineated farmland boundaries and the referenced boundaries are basically coincident, and most of them maintain a one-to-one relationship. The farmland boundary extraction accuracy is high. In terms of quantitative evaluation, more than 80% of the field under-segmentation rate (S_j^under) and over-segmentation rate (S_j^over) are lower than 20%, and the extracted farmland boundaries and the referenced boundaries coincide with each other; more than 85% of the field. The matching degree (S_j^local) is higher than 80%, and the farmland boundary is positioned accurately. In part of AGB estimation, we focus on three important issues in the AGB estimation at the field scale: 1) the construction of a rice AGB estimation model involving multi-ecological zones; 2) comparison of the performance of machine learning algorithms in rice AGB estimation; and 3) the application potential of object-oriented image analysis techniques in rice AGB estimation. And exploring the potential of WorldView-2 imagery in crop growth monitoring at the field scale. Results have demonstrated that there are obvious spectral differences in rice pixels extracted from satellite images at different ecological point. However, after histogram matching, the systematic spectral differences between different images disappear. The accuracy of the AGB estimated model based on the spectral index EVI and CIred-edge referring to different ecological point has been significantly improved, and the decision coefficients have been increased from the initial 0.06 and 0.53 to 0.52 and 0.68, respectively. In addition, we can find that the random forest algorithm (Random Forest, RF) has the highest estimation accuracy (R2=0.92, RMSE=0.52t/ha, RE=8%) among the six selected regression algorithms, and its computational efficiency is relatively high. Partial Least Squares Regression (PLSR) has obvious advantages in terms of computational efficiency, although the estimation accuracy of rice AGB is relatively low based on PLSR (R2=0.71, RMSE=0.97t/ha, RE=16). It''s worth noting that the computational efficiency of PLSR algorithm is faster 18~1296 times than other machine learning algorithms. In conclusion, The RF and PLSR algorithms have important application value in rice AGB estimation. Additionally, compared with the traditional pixel-based image analysis method, object-oriented image analysis technology can further improve the estimation accuracy of rice AGB. Significantly, this improvement is particularly evident in the PLSR algorithm with R2 increasing from 0.72 to 0.80. RMSE and RE decreasing to 0.80t/ha and 13%, respectively. And the computational efficiency of object-oriented parameter regression algorithm is more than 12 times that of pixel parameter regression algorithm. At the same time, the spatial distribution map of rice AGB based on object-oriented image analysis technology is more in line with the actual needs of crop field management. These suggest that object-oriented image analysis technology has great potential in the field of crop precision management.