Crop growth monitoring method based on inverse Gaussian process inversion of leaf area index

Abstract

The invention belongs to the field of agricultural remote sensing, and relates to a crop growth monitoring method based on a leaf area index (LAI) inverted through an inverse Gaussian process. The method comprises the following steps: obtaining consistent sentinel No.2 surface reflectivity, and extracting a spatial distribution map of crops; obtaining PROSAIL simulated crop spectrums under different backgrounds and converting the simulated crop spectrums into waveband spectrums; analyzing the principal components of the simulated surface reflectivity under a black soil background to obtain pure canopy reflection characteristics; analyzing the principal components of the actually measured soil spectral reflectivity to obtain pure soil reflection characteristics; linearly decomposing the simulated surface reflectivity containing soil information to obtain simulated pure canopy reflectivity; learning the mapping relationship between simulated pure canopy reflectivity and LAI in a simulative way through a Gaussian process to obtain a trained Gaussian process model; and linearly decomposing a sentinel No.2 image to obtain pure canopy reflectivity, selecting and inputting a simulated pure canopy reflectivity closest to the pure canopy reflectivity to the trained Gaussian process model to obtain LAI, and judging the current crop growth by taking the previous annual average LAI trajectory as a benchmark. The method of the invention avoids the problem of multi-parameter same effect and is of high precision.

Description

technical field [0001] The invention belongs to the field of agricultural remote sensing, and in particular relates to a crop growth monitoring method based on inverse Gaussian process inversion of leaf area index. Background technique [0002] In current practical applications, growth monitoring is generally based on NDVI, and less based on leaf area index (LAI). The reason is that the inversion of LAI is much more difficult than the NDVI that can be obtained only by band calculation. However, NDVI only reflects the spectral information of crops in the red and near-red bands, while LAI reflects the sum of the single green leaf area per unit surface area, which is related to important biophysical parameters of crops such as canopy interception, evapotranspiration, and photosynthesis. The process has a closer relationship and can reflect the growth of crops more comprehensively. [0003] At present, there are two main methods to obtain LAI using remote sensing technology, on...

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Problems solved by technology

[0004] In order to solve the problem of multi-parameter co-efficiency in the traditional method of obtaining LAI from the inversion of canopy light radiation information through the radiative transfer model, the present invention simulates the radiative transfer model based on the inverse Gaussian process, which can quickly invert the LAI, and in the inversion The actual measurement data on the ground is not required in the exercise, and the same effect of different parameters can also be avoided

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