Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters

Abstract

The invention relates to a leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters. The method includes: downloading a remote sensing image and preprocessing the same to obtain multispectral canopy reflectance data; using a PROSAIL model to establish a lookup table according to different parameter combinations, and determining the relation of different parameters and canopy reflectance, namely an regression equation; establishing an objective function, combining the multispectral canopy reflectance data to optimize the parameters until a global minimum and a corresponding parameter combination of the objective function are obtained, and updating the parameters by the aid of the multispectral canopy reflectance data; and performing inversion according to the regression equation, the multispectral canopy reflectance data and the parameter combination, so that a leaf area index and chlorophyll content are obtained. The method extends a traditional method from points to surfaces, field observation data are not needed, cost of measuring the leaf area index and the chlorophyll content by the traditional method is effectively lowered, and inversion accuracy and speed are increased.

Description

technical field [0001] The invention relates to the processing of remote sensing data and the technical fields of agronomy, in particular to an inversion method of leaf area index and chlorophyll content based on remote sensing image optimization of PROSAIL model parameters. Background technique [0002] Leaf area index and chlorophyll content are important botanical parameters, which are widely used in crop growth monitoring, yield estimation and other fields. The traditional method of measuring leaf area index and chlorophyll content mainly relies on field sampling and instrument measurement. Although this method has high accuracy, it has a large workload and can only obtain leaf area index and chlorophyll content of limited points on the ground. , it is difficult to obtain large-area leaf area index and chlorophyll content, which cannot meet the needs of vegetation ecology and crop growth monitoring. [0003] Remote sensing technology has the characteristics of large det...

AI Technical Summary

Problems solved by technology

Among them, the empirical inversion method mainly inverts the leaf area index by establishing the statistical relationship between the vegetation index and the leaf area index and chlorophyll content. The relationship often lacks universality, and the inversion error is relatively large; the commonly used models in the model inversion method are geometric optics model, radiative transfer model and hybrid model. Different models have different emphases, and their common feature is that they have certain The inversion is closer to reality, and the model inversion requires more parameters. In the existing model inversion methods, the parameters are often selected from empirical values ​​or measured values, lack of optimization of parameters, and poor representativeness. The inversion brings large errors and affects the inversion accuracy

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