Improved Na + estimation from hyperspectral data of saline vegetation by machine learning

Monitoring the growth state of vegetation using remote sensing is the current trends in agricultural research. This study aims to identify an optimal hyperspectral vegetation extraction framework to improve leaf Na + monitoring in the northwestern part of China based on the hyperspectral data of saline vegetation. The Partial Least Squares (PLS), Support Vector Machine (SVM), Random Forest (RF) models were constructed to model the leaf Na +, while the Aggregated Boosted Tree (ABT) and Random Forest (RF) variable importance screening methods were used to optimize the variables in the leaf Na + extraction. Then, the optimal variable screening method and the model of inverting vegetation Na + was identified. The results showed that the estimation of Na + content within saline vegetation leaves by constructing spectral indices is feasible as 33 vegetation indices meets the requirements, the RF (R 2  = 0.73, RMSE = 0.50) and PLS (R 2  = 0.72, RMSE = 0.59) models are relatively good, followed by the SVM (R 2  = 0.68, RMSE = 0.53) model. In addition, all the three models have been improved using the ABT variable importance screening method, where the RF (R 2  = 0.81, RMSE = 0.42) model had the most satisfactory effect. Similarly, based on the RF importance screening method, all the three models have improved significantly, among which the most effective was the SVM (R 2  = 0.82, RMSE = 0.45) model. This study indicates that ABT-RF and RF-SVM are the most ideal combination framework to invert the Na + content of saline vegetation leaves. This study brings out some inspiration for the combination between the screening approach of variables and model building, improving the accuracy of hyperspectral sensor to monitor the changes in the relevant chemical characteristics of vegetation.