Mangroves play an extremely important role in purifying the atmosphere and responding to global temperature changes. The analysis of chemical elements (carbon, nitrogen, phosphorus, etc.) in mangroves is an effective way to investigate physiological activities, such as vegetation growth, development, and material metabolism. Therefore, the monitoring of mangrove stoichiometry is extremely important for mangrove restoration. Here, two mangrove species, Kandelia candel (KC) and Aegiceras corniculatum (AC), were studied in the Quanzhou Bay Estuary Wetland Nature Reserve. Two machine learning models [random forest (RF) and back propagation neural network (BPNN)] and partial least squares (PLS) were established with the original spectral data as independent variables, and the optimal model was selected by comparing the simple cross-validation VEcv, the ratio of performance to deviation, and the root mean square error (RMSE). The results showed that: (1) the contents of total phosphorous and total nitrogen decreased gradually and the content of total carbon of mangroves increased gradually with an increase in age at restoration; (2) hyperspectral modeling can invert the ecological stoichiometries of KC and AC, and it can be used to effectively monitor the growth status of the species studied; and (3) model performance ranking, PLS > RF > BPNN, where PLS (VEcv ≥ 0.60 and RMSE < 40) was significantly better than the other two models, and BPNN was the least effective and not suitable for hyperspectral inversion modeling of KC and AC ecological stoichiometries. This study provides a methodological basis for long-term and large-scale dynamic monitoring of mangrove ecological stoichiometries and mangrove restoration quality based on hyperspectral data.

中文翻译：

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基于机器学习模型的不同修复区域红树林叶片化学计量的高光谱预测

红树林在净化大气和应对全球气温变化方面发挥着极其重要的作用。分析红树林中的化学元素（碳、氮、磷等）是研究植被生长发育和物质代谢等生理活动的有效途径。因此，红树林化学计量的监测对于红树林恢复极为重要。在这里，在泉州湾河口湿地自然保护区研究了两种红树林树种，Kandelia candel (KC) 和 Aegiceras corniculatum (AC)。以原始光谱数据为自变量建立了两种机器学习模型[随机森林（RF）和反向传播神经网络（BPNN）]和偏最小二乘法（PLS），通过比较简单的交叉验证来选择最优模型VEcv, 性能与偏差的比率，以及均方根误差 (RMSE)。结果表明：(1)随着恢复树龄的增加，红树林全磷、全氮含量逐渐降低，全​​碳含量逐渐升高；(2) 高光谱建模可以反演KC和AC的生态化学计量，可用于有效监测所研究物种的生长状况；(3) 模型性能排名，PLS > RF > BPNN，其中PLS（VEcv≥0.60，RMSE<40）明显优于其他两种模型，BPNN效果最差，不适合KC和高光谱反演建模AC 生态化学计量。