Method for inverting leaf chlorophyll content based on new spectrum characteristics

Abstract

The invention discloses a method for inverting the leaf chlorophyll content based on new spectrum characteristics. The method comprises the following steps of: taking a training data set of new spectrum characteristics (deltah) as an independent variable of leaf chlorophyll content inversion, and performing dimension reduction processing on the part of data by using a lasso algorithm based on parameters obtained in the step 3; and carrying out multivariate nonlinear regression by using the obtained dimension reduction data as an independent variable of a final regression model, establishing a multivariate nonlinear regression model with lasso algorithm constraints, namely an LASSO-MNR inversion model, and inverting the leaf chlorophyll content by using the LASSO-MNR inversion model. According to the method, a multivariate nonlinear regression model (LASSO-MNR) based on the lasso algorithm is established, the model is simple in principle and convenient to operate, the redundancy of spectral data is effectively reduced, the inversion precision is improved, and the method has relatively high flexibility and practicability and relatively high universality at the same time.

Description

technical field [0001] The invention belongs to the technical field of inversion of trace element content in leaves of agricultural crops, and in particular relates to a method for inversion of chlorophyll content in leaves based on a combination of a lasso algorithm and a multiple nonlinear regression algorithm. Background technique [0002] The growth of crops such as winter wheat can be expressed by their physiological characteristics, and their physiological characteristics determine their changes in light absorption, transmission and reflection. Therefore, the difference in spectral reflectance data can be used to monitor the chlorophyll content of crops such as winter wheat. Hyperspectral remote sensing can quickly and accurately reflect the physiological and biochemical parameters of crops such as winter wheat, so spectral difference analysis based on hyperspectral has a wider application prospect in the field of vegetation remote sensing research. Hyperspectral data...

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Problems solved by technology

Among them, a single vegetation index discards a large amount of data and does not represent the entire hyperspectral information well, which may affect the inversion accuracy; using characteristic parameters such as red edge index and red edge area to invert chlorophyll content is also widely used. However, there are still some deficiencies in the generality of the model; in recent years, some related scholars have used methods such as neural networks to monitor the chlorophyll content of crops, but most of these methods have the influence of general applicability and poor interpretability.

At present, in the relevant research on hyperspectral data, there are few studies on using lasso algorithm to reduce dimensionality and then using multiple nonlinear regression to fit and invert leaf chlorophyll content.

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