To explore the changes of chlorophyll content in needles of different leaf ages of Picea koraiensis Nakai of different specifications, the study compared the prediction accuracy changes of chlorophyll in needles of Picea koraiensis Nakai by two modeling methods: the BP neural network and partial least squares regression (PLSR) methods. The effects of different spectral pre-processing and characteristic band selection methods on the performance accuracy of the model were tested, and the optimal combination model was selected to predict forest growth status and community structure productivity through the physiological and biochemical characteristics of needles at different leaf ages.
1) the spectral pre-processing method could avoid systematic errors and eliminate background values; 2) the accuracy of the needle chlorophyll fitting model with different leaf ages was much higher than that of mixed needle chlorophyll model, verifying that needle chlorophyll with different leaf ages could better estimate the annual growth and examine the growth status of Picea koraiensis Nakai; 3) the accuracy of the BP neural network model was significantly higher than that of the PLSR model, with its R 2 above 0.95, and the validation set’s R 2 above 0.86; and 4) the fitting accuracy of different leaf age needle chlorophyll models of the spectral pre-processing model, variable selection model, PLSR model and BP neural network: triennial needles > annual and biennial needles.
The BP neural network method was more accurate than the PLSR method in predicting pigment content model. In the process of model fitting, it was found that the pigment model fitted by fine classification of needles improves the accuracy of the model, which provides the basis and theoretical support for the establishment of the model by combining remote sensing technology with stoichiometry methods in the future.