Hyperspectral remote sensing data vegetation information extraction method

Abstract

The invention discloses a hyperspectral remote sensing data vegetation information extraction method. The method comprises in an experiment research zone, synchronously or quasi-synchronously acquiring a hyperspectral remote sensing image and reference standard values of physiological and biochemical parameters of vegetation to be detected, carrying out dimensionality reduction pre-treatment on the hyperspectral remote sensing image to obtain vegetation canopy spectral reflectance data, building an empirical mathematic model of the vegetation canopy spectral reflectance data and the vegetation parameter standard values to obtain a hyperspectral remote sensing estimation method of the vegetation physiological and biochemical parameters, through the empirical mathematic model, acquiring a vegetation parameter estimated value by the vegetation canopy spectral reflectance data obtained by the hyperspectral remote sensing image, carrying out estimation on the parameters of the model by the sample value and carrying out examination on a model precision. Through the hyperspectral remote sensing data, vegetation physiological and biochemical parameter estimation is realized so that the method is convenient and fast, does not influence vegetation growth, is suitable for large-area popularization and has a measurement area of the whole earth range.

Description

technical field [0001] The present application relates to the technical field of vegetation information extraction, in particular, to a method for extracting vegetation information from hyperspectral remote sensing data. Background technique [0002] Traditionally, the physiological and biochemical parameters of vegetation are mainly obtained through physical and chemical experiments. These experiments are designed completely according to the definition of vegetation parameters, so the measurement accuracy is relatively high. The disadvantage is that it needs to pick vegetation samples in the field, which is time-consuming and laborious, and it is destructive to vegetation. This kind of experiment is not suitable for carrying out on a large area, and can only use samples to estimate the whole. In addition, for areas where humans cannot or are not easy to reach, it is completely infeasible to use traditional methods to extract vegetation information. [0003] Using hypersp...

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Problems solved by technology

However, due to the complex issues involved in the structure of vegetation canopy and leaves, radiation transfer process, etc., the model structure will be more complicated, and too many variables may affect the actual application effect

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