A Correction Method of Spatial Scale Error of Leaf Area Index Based on Wavelet Transform

Abstract

The invention discloses a leaf area index spatial scale error correction method based on wavelet transform. The method is mainly divided into the following six steps that: obtaining synchronous / nonsynchronous small-scale remote sensing data; estimating a scale transform error rate; estimating a wavelet transform detail loss ratio; carrying out correction regression coefficient least square calibration; estimating a spatial scale effect error; and correcting a large-scale leaf area index spatial scale error. Compared with an existing method, the method has the advantages of a simple and convenient implementation process, in addition, a scale error correction effect is irrelevant to the nonlinear degree of a remote sensing inversion model and is not limited to the continuous and derivable constraint limit of the inversion model, in addition, the assistance of synchronous small-scale prior data is not required, an application range can be effectively enlarged, and the remote sensing inversion accuracy of a large-scale leaf area index product is improved.

Description

technical field [0001] The invention relates to a correction method, in particular to a method for correcting leaf area index spatial scale errors based on wavelet transform. Background technique [0002] Leaf area index is one of the most basic parameters to characterize vegetation canopy structure, and is also a key factor determining vegetation biomass and yield, and is an important data source for crop growth monitoring and yield estimation. Accurate determination of leaf area index is of great significance for understanding the biophysical processes of forests and crops, as well as the precise inversion of various parameters such as photosynthetically active radiation and crop yield, and monitoring of diseases and pests. Taking advantage of the wide coverage of satellite remote sensing data to quantitatively estimate large-area leaf area index not only saves time and effort, but also enables real-time, fast and accurate measurement and dynamic monitoring of large-scale ...

AI Technical Summary

Problems solved by technology

This process requires the remote sensing inversion model to be continuously differentiable, so when the selected remote sensing inversion model is discontinuous or non-differentiable, the spatial scale error cannot be estimated

In addition, because the existing method ignores the third-order and higher-order expansion terms in the Taylor series expansion, a large error will occur when the inversion model is more nonlinear

[0005] Second, the estimation of scale error of large-scale LAI products cannot be separated from synchronous small-scale prior data

Acquisition of supporting small-scale data is difficult and costly

In addition, it is required that the imaging time and observation geometry of the small-scale and large-scale data are approximately consistent, otherwise the estimation of spatial heterogeneity will bring a certain deviation, which will affect the scale error correction accuracy of the large-scale LAI product

This limitation directly leads to the inability to effectively and accurately estimate the scale error of large-scale LAI products due to the lack of small-scale data in the actual application process.

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