Cotton field soil moisture inversion method and system combining microwave and optical data

Through the cotton field soil moisture inversion method combined with microwave and optical data, combined with water cloud model and neural network model, the problem of insufficient soil moisture inversion accuracy in the existing technology is solved, and more accurate soil moisture monitoring and disaster warning are achieved.

CN119644283APending Publication Date: 2025-03-18CHUZHOU UNIV
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Patent Information

Application Number
CN202410162687.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-02-05
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

The prior art is difficult to accurately invert soil moisture, especially under the uneven temporal and spatial distribution of surface moisture, which leads to insufficient early warning and estimation capabilities for drought and flood disasters.

Method used

The cotton field soil moisture inversion method combined with microwave and optical data is adopted. The combined use of optical satellite data and radar satellite data is combined with the water cloud model to remove the impact of vegetation, and the feature parameters are screened through the feature selection algorithm, and the neural network model is trained to perform soil moisture inversion.

Benefits of technology

The accuracy of soil moisture inversion has been improved, the space-time monitoring capability of soil moisture changes has been enhanced, and the early warning and loss estimation capability of drought and flood disasters has been improved.

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Abstract

The invention discloses a cotton field soil moisture inversion method and system combining microwave and optical data, and relates to the technical field of remote sensing measurement of soil moisture content, and the method comprises the steps: obtaining satellite data which comprises radar satellite data and optical satellite data; a water cloud model is adopted for radar satellite data to remove vegetation influences, and radar characteristic parameters are obtained; performing polarization decomposition on the optical satellite data to obtain polarization decomposition characteristic parameters, and obtaining a vegetation index of the optical satellite data; the characteristic parameters comprise radar characteristic parameters, polarization decomposition characteristic parameters and vegetation indexes; screening the feature parameters by adopting a feature selection algorithm, presetting an importance threshold, and selecting the feature parameters greater than the importance threshold as a sample set; according to the method, the soil moisture inversion is carried out through combination of the optical satellite data and the radar satellite data, and the soil moisture inversion precision is improved.
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