Vegetation leaf parameter inversion method and device based on remote sensing

Abstract

The invention provides a vegetation leaf parameter inversion device based on remote sensing, which is obtained based on an inverse process of expressing a PROSAIL model by using a deep neural network, and not only has a relatively strong physical mechanism, but also has relatively high accuracy. According to the remote sensing-based vegetation leaf parameter inversion method provided by the invention, the remote sensing data of the vegetation leaf is inversed to obtain the parameters of the vegetation leaf by using the remote sensing-based vegetation leaf parameter inversion device provided by the invention, and the method has relatively high credibility and accuracy.

Description

technical field [0001] The invention relates to the field of remote sensing, and more particularly, to a method and device for inversion of vegetation leaf parameters based on remote sensing. Background technique [0002] Leaf parameters are important indicators to characterize the growth state of vegetation. Quantitatively obtaining leaf parameters is an important field of precision agriculture research, which is of great significance for supporting carbon cycle research. [0003] At present, the application of remote sensing based on deep learning is increasingly widespread, especially in the problem of nonlinear parameter inversion. The deep neural network model can find the intrinsic relationship between the input parameters and output parameters of the model through continuous learning and training. parameter inversion. [0004] However, there is still room for improvement in the accuracy of traditional deep learning-based blade parameter inversion methods. SUMMARY O...

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Problems solved by technology

[0004] However, the accuracy of the traditional deep learning-based blade parameter inversion method still has room for improvement.

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