Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle hyperspectral data. Three winter wheat layers were divided, and the multi-angle hyperspectral data of the different layers were obtained by removing the leaves from the lower layer to the top layer. The multi-angle vegetation index and LCC linear models were established, and the estimated model based on nadir view angle (i.e., conventional observation angle) was compared. Results show that (1) the best observation angle for the first layer, the second layer, and the third layer are 60°, 60°, 50°, respectively. (2) The accuracy of multi-angle-based estimation models (R² = 0.87, RMSE = 2.86 μg cm⁻²) are higher than nadir-based ones (R² = 0.72, RMSE = 4.24 μg cm⁻²). This study proved that vertical distribution has a positive influence on the estimation results, and multi-angle hyperspectral data could be promising in improving estimation accuracy.

叶绿素在作物的光合作用中起着重要的作用，与氮（N）密切相关。N缺乏首先发生在下部叶片中，但是由于叶片遮蔽，下部层的光谱检测不足。本文的目的是探讨使用多角度高光谱数据估算小麦叶片叶绿素含量（LCC）和LCC垂直分布的可行性。划分为三个冬小麦层，并通过从下层到上层去除叶片来获得不同层的多角度高光谱数据。建立了多角度植被指数和LCC线性模型，并比较了基于最低视角（即常规观测角）的估计模型。结果表明：（1）第一层的最佳观察角度，第二层和第三层分别为60°，60°，50°。（2）基于多角度的估计模型的准确性（R ²  = 0.87，RMSE = 2.86μgcm ^-2高于基于最低点的（R ²  = 0.72，RMSE = 4.24μgcm ^-2）。这项研究证明垂直分布对估计结果有积极的影响，多角度高光谱数据有望提高估计精度。