Soil organic matter content monitoring method and system

Abstract

The invention provides a soil organic matter content monitoring method and system. The hyperspectral image of the unmanned aerial vehicle and the soil organic matter content actually measured on the ground are respectively collected, and the SOM regression estimation model is constructed by combining the variable space iterative shrinkage algorithm with the extreme random tree; then the organic matter content of each pixel point on the soil hyperspectral image is obtained through the constructed SOM regression estimation model, an organic matter content distribution diagram is obtained through inversion, the monitoring workload and research cost can be reduced while the timeliness and accuracy of soil organic matter content monitoring can be improved, therefore, the unmanned aerial vehicle can rapidly monitor crops and natural resources under a customized spatial-temporal scale, and data support is provided for precision agriculture.

Description

technical field [0001] The invention relates to the technical field of soil organic matter content monitoring, in particular to a soil organic matter content monitoring method and system. Background technique [0002] Soil Organic Matter (SOM) can improve the physical, chemical, and biological properties of soil through biosynthesis and decomposition, and plays an important role in controlling soil function and quality, offsetting greenhouse gas emissions, and improving information on the global carbon cycle system. . Hyperspectral technology has fine spectral resolution and can obtain nanoscale continuous spectral information of ground objects. SOM has a variety of functional groups (such as hydroxyl, carboxyl, etc.), which have characteristic absorption in the infrared spectral region, and the absorption intensity of different bands There is a corresponding relationship with the molecular structure and concentration of the substance, which lays a theoretical foundation fo...

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Problems solved by technology

[0003] Traditional soil organic matter content monitoring methods mainly rely on laboratory measurements, which have problems such as time-consuming, heavy workload, and high research costs. Moreover, the spectral characteristics and analysis of collected samples cannot provide a spatially continuous distribution of soil properties in a specific area.

Moreover, in most previous studies, the hyperspectral data measured in the darkroom were mainly used to predict the SOM content, because this type of data is not easily affected by soil moisture, soil roughness and atmospheric environment, and the existence of these interference factors significantly increases the soil properties. forecast uncertainty

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