Leaf chlorophyll, as a key factor for carbon circulation in the ecosystem, is significant for the photosynthetic productivity estimation and crop growth monitoring in agricultural management. Hyperspectral remote sensing (RS) provides feasible solutions for obtaining crop leaf chlorophyll content (LCC) by the advantages of its repeated and high throughput observations. However, the data redundancy and the poor robustness of the inversion models are still major obstacles that prevent the widespread application of hyperspectral RS for crop LCC evaluation. For winter wheat LCC inversion from hyperspectral observations, this study described a novel hybrid method, which is based on the combination of amplitude- and shape- enhanced 2D correlation spectrum (2DCOS) and transfer learning. The innovative feature selection method, amplitude- and shape- enhanced 2DCOS, which originated from 2DCOS, additionally considered the relationships between external perturbations and hyperspectral amplitude and shape characteristics to enhance the dynamic spectrum response. To extract the representative LCC featured wavelengths, the amplitude- and shape- enhanced 2DCOS was conducted on the leaf optical PROperties SPECTra (PROSPECT) + Scattering from Arbitrarily Inclined Leaves (SAIL) (PROSAIL) simulated dataset, which covered most possible winter wheat canopy spectra. Nine wavelengths (i.e., 455, 545, 571, 615, 641, 662, 706, 728, and 756 nm) were then extracted as the sensitive wavelengths of LCC with the amplitude- and shape- enhanced 2DCOS. These wavelengths had specificity to LCC and showed good correlation with LCC from the aspect of photosynthesis mechanism, molecular structure, and optical properties. The transfer learning techniques based on the deep neural network was then introduced to transfer the knowledge learned from the PROSAIL simulated dataset to the inversion tasks of field measured LCC. Parts of the labeled samples in field observations were used to finetune the model pre-trained by the simulated dataset to improve the inversion accuracy of the winter wheat LCC in different field scenes, aiming to reduce the need for the field measured and labeled sample size. To further ascertain the universality, transferability and predictive ability of the proposed hybrid method, field samples collected from different locations at different phenological phases, including the jointing and heading stages in 2013, 2014, and 2018, were utilized as target tasks to validate the proposed hybrid method. Moreover, the LCC of winter wheat estimated with the proposed method was evaluated with the ground-based platform and the UAV-based platform to verify the model versatility for different monitoring platforms. Various validations demonstrated that the hybrid inversion method combining the amplitude- and shape- enhanced 2DCOS and the fine-tuned transfer learning model could effectively estimate winter wheat LCC with good accuracy and robustness, and can be extended to the detection and inversion of other key variables of crops.

叶绿素作为生态系统中碳循环的关键因素，对农业管理中的光合生产力估算和作物生长监测具有重要意义。高光谱遥感 (RS) 凭借其重复和高通量观测的优势，为获取作物叶片叶绿素含量 (LCC) 提供了可行的解决方案。然而，数据冗余和反演模型鲁棒性差仍然是阻碍高光谱RS在作物LCC评估中广泛应用的主要障碍。对于来自高光谱观测的冬小麦 LCC 反演，本研究描述了一种新的混合方法，该方法基于幅度和形状增强的二维相关谱 (2DCOS) 和迁移学习的组合。创新的特征选择方法，起源于2DCOS的幅度和形状增强的2DCOS额外考虑了外部扰动与高光谱幅度和形状特征之间的关系，以增强动态光谱响应。为了提取具有代表性的 LCC 特征波长，在叶子光学特性 SPECTra (PROSPECT) + Scattering from Arbitrary Inclined Leaves (SAIL) (PROSAIL) 模拟数据集上进行了幅度和形状增强的 2DCOS，该数据集涵盖了大多数可能的冬小麦冠层光谱. 然后提取9个波长（即455、545、571、615、641、662、706、728和756 nm）作为具有幅度和形状增强的2DCOS的LCC的敏感波长。这些波长对LCC具有特异性，从光合作用机制、分子结构、和光学特性。然后引入基于深度神经网络的迁移学习技术，将从 PROSAIL 模拟数据集学到的知识迁移到现场测量 LCC 的反演任务中。利用野外观测的部分标记样本对模拟数据集预训练的模型进行微调，提高不同野外场景下冬小麦LCC的反演精度，减少对野外实测和标记样本量的需求。为了进一步确定所提出的混合方法的普遍性、可转移性和预测能力，利用从不同物候阶段（包括 2013 年、2014 年和 2018 年拔节期和抽穗期）不同地点收集的田间样本作为目标任务来验证所提出的混合方法。混合方法。而且，通过陆基平台和无人机平台对提出方法估计的冬小麦LCC进行评估，验证模型对不同监测平台的通用性。各种验证表明，结合幅度和形状增强2DCOS和微调转移学习模型的混合反演方法可以有效地估计冬小麦LCC，具有良好的准确性和鲁棒性，并且可以扩展到其他关键变量的检测和反演的农作物。