盐城滨海湿地优势植物碳氮磷生态化学计量高光谱反演

滨海湿地是海陆交界的生态过渡带，是自然界生物多样性最丰富的生态系统之一。湿地植物作为湿地生态系统重要的组成部分，研究其碳、氮、磷生态化学计量特征是了解植物生长状况与适应策略的有效途径。以江苏盐城滨海湿地为研究区，采集互花米草(Spartina alterifora)、芦苇(Phragmites australis)、白茅(Imperata cylindrica)、柽柳(Tamarix chinensis)、盐地碱蓬(Suaeda salsa)共5种优势湿地植物样本及冠层高光谱数据，对植物的碳、氮、磷生态化学计量特征进行高光谱反演研究。结果表示白茅、柽柳与芦苇的最佳反演模型为随机森林(RF)模型，对互花米草反演效果最好的是偏最小二乘(PLSR)模型，而对盐地碱蓬反演精度最高的是BP神经网络(BPNN)模型。研究表明利用高光谱数据可以实现湿地植物碳、氮、磷生态化学计量特征的准确反演，不同模型对于不同湿地植物的反演存在差异，RF模型的反演稳定性最强，是反演湿地植物生态化学计量特征的较优模型。

Hyperspectral inversion of carbon, nitrogen and phosphorus stoichiometry of dominant plants in Yancheng Coastal Wetland

The coastal wetland is an ecological transition zone at the junction of sea and land, and is one of the most biodiverse ecosystems in nature. As an important part of wetland ecosystems, studying the eco-stoichiometric characteristics of carbon, nitrogen and phosphorus is an effective way to understand plant growth status and adaptation strategies. Taking Yancheng Coastal Wetland in Jiangsu Province as the research area, this paper collected five dominant wetland plant samples and canopy hyperspectral data, including Spartina alterifora, Phragmites australis, Imperata cylindrica, Tamarix chinensis, and Suaeda salsa, and conducted hyperspectral inversion research on the eco stoichiometric characteristics of carbon, nitrogen and phosphorus of plants. The results showed that the random forest (RF) model was the best inversion model for P. australis, I. cylindrica and T. chinensis, the partial least squares (PLSR) model was the best inversion of S. alterifora, and the model with the highest inversion accuracy of S. salsa was BP Neural Network (BPNN) model. This study shows that the use of hyperspectral data can achieve accurate inversion of the ecological stoichiometric characteristics of carbon, nitrogen and phosphorus in wetland plants. Different models have different inversions for different wetland plants. The RF model has the strongest inversion stability and is a better model for inversion of wetland plant ecological stoichiometric characteristics.