区域叶面积指数（Leaf Area Index, LAI）定量反演是开展大尺度农作物长势监测和产量估算的重要基础。针对当前区域LAI遥感定量反演存在的反演精度不理想和模型稳定性弱等问题，提出了一种基于少量训练样本进行LAI高精度反演的深度神经网络（Small Simple Learning LAI-Net, SSLLAI-Net）。该网络由2个卷积层、1个池化层和3个全连接层构成，将光谱反射率数据作为网络输入端、输出端得到LAI反演值，且该网络模型可支持小样本数据量的训练。以德国阿尔卑斯山麓高光谱遥感卫星影像Environmental Mapping and Analysis Program （EnMAP）为数据源，以该区域的谷物、玉米、油菜、其他作物为研究对象，数值实验结果表明当各作物类别的训练样本量均为50时，基于SSLLAI-Net的LAI反演精度分别为0.95、0.99、0.98、0.90；且在添加噪声的情况下，各作物类别的LAI反演精度分别为0.95、0.98、0.96、0.89。综上，提出的基于深度神经网络的区域LAI遥感定量反演方法SSLLAI-Net是鲁棒可靠的，且该模型能够支持稳定的小样本建模。

Regional leaf area index （LAI） mapping is important for crop growth monitoring and yield estimation. Due to the lower accuracy and instability of statistical models for regional LAI estimation, we proposed a new deep neural network model, i.e. Small Simple Learning LAI-Net （SSLLAI-Net）, based on small sample training, to achieve stable relationship between hyperspectral reflectance and LAI. The new proposed SSLLAI-Net was constructed with two convolution layers, one pooling layer and three connect layers, for which the inputs and outputs were hyperspectral reflectance and LAI estimation. Moreover, SSLLAI-Net could support small training sets. We applied SSLLAI-Net to an Environmental Mapping and Analysis Program （EnMAP） hyperspectral imagery for regional LAI mapping, in which cereals, maize, rape seed and other crops are selected as our objects. The achieved R² values for estimated LAI of cereals, maize, rape seed and other crops were 0.95, 0.99, 0.98 and 0.90 based on small training sets with 50 samples, while for the inputs with noise, the R² values were 0.95、0.98、0.96 and 0.89, respectively. In all, our new proposed SSLLAI-Net has high precision of regional LAI mapping, stability and noise resistance with hyperspectral remote sensing observations.