Space-time refined soil organic matter content remote sensing dynamic inversion method

Abstract

The invention relates to a space-time refined soil organic matter content remote sensing dynamic inversion method. The method comprises the steps that the Sentine-2 and Sentine-3 are selected as remote sensing image sources, and a Gram-Schmidt spectrum sharpening image fusion method is used for image fusion, so that the image data used by the method has high space, high time and high spectral resolution. An inversion model framework containing the fused image multiband reflectivity and the multiple vegetation indexes is constructed, an optimal index combination mode is selected from an index selection pool through methods such as relation fitting, selection comparison and autocorrelation verification, and an image inversion method which is based on image fusion and can capture space-time change characteristics of soil organic matter most effectively is constructed and formed. According to the soil organic matter content remote sensing inversion method, the high spectral resolution andthe high space-time precision are both considered, a space-time refined soil organic matter content change diagram in the regional scale can be obtained, and the important basic data and the decisionsupport are provided for exploring the change trend of the farmland productivity so as to accurately guide the agricultural production.

Description

technical field [0001] The invention belongs to the technical field of remote sensing, and in particular relates to a remote sensing inversion method for soil organic matter content taking into account both high spectral resolution and high temporal and spatial accuracy. Background technique [0002] Soil organic matter refers to the animal and plant residues in the soil, microorganisms and the substances they decompose and synthesize. It is an important indicator reflecting soil fertility and an important parameter responding to changes in the ecological environment. On the one hand, the quantitative inversion of organic matter using multispectral remote sensing data has the advantages of low cost, time saving, high efficiency and strong timeliness; Images with high spatial resolution often have low temporal resolution, which cannot refine the research time. Images with high temporal resolution usually have low spatial resolution, and the inversion effect of small-scale are...

AI Technical Summary

Problems solved by technology

[0005] At present, there are many remote sensing inversion methods and researches on soil organic matter, but they all face a common problem in small and medium regional scales: most of the existing methods use Landsat images or Modis sensors as data sources, and the spatial resolution of Modis images is relatively high. Low, it is difficult to effectively grasp the spatial heterogeneity information in small and medium regional scales. Although Landsat has a high spatial resolution, it is difficult to obtain multi-period available data at the same time within a certain time range due to the influence of cloud coverage and transit time. The number of bands is small, which causes the current organic matter inversion method to be relatively static in time, and has insufficient grasp of spatiotemporal heterogeneity

It can be seen that it is necessary to carry out the extraction and spatial mapping of soil organic matter spatio-temporal refinement information, while the existing remote sensing inversion methods focus on a single data source (time, space, and spectral resolution are difficult to take into account) and a single band information ( Unable to weaken the interference of the environmental background on the spectrum, the sensitivity is not high)

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