Satellite-borne polarization SMAP-R data gap filling method based on multi-polarization SAR data

Through multi-polarization SAR data modeling and ensemble learning, the SMAP-R data gap problem was solved, high temporal and spatial resolution surface parameter inversion was achieved, and the inversion accuracy of vegetation optical thickness, vegetation water content and soil moisture was improved.

CN120763263AActive Publication Date: 2025-10-10KUNMING UNIV OF SCI & TECH
2 Cites 0 Cited by

Patent Information

Application Number
CN202510643038.9
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-19
Publication Date
2025-10-10
Estimated Expiration
2045-05-19

AI Technical Summary

Technical Problem

Due to weak reflection signals, uneven distribution of reflection points, and incoherent accumulation time, the SMAP-R data contain a large number of gaps or incomplete areas in space, which affects the inversion of surface parameters with high temporal and spatial resolution.

Method used

By using multi-polarization SAR data for backscatter modeling, combined with ensemble learning and neural network models, the gaps in SMAP-R data are supplemented and the spatial resolution of surface parameters is improved.

Benefits of technology

It has achieved high temporal and spatial resolution inversion of SMAP-R data, improved the inversion accuracy of surface parameters such as vegetation optical depth, vegetation water content and soil moisture, filled data gaps and improved spatial resolution.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a space-borne polarization SMAP-R data gap filling method based on multi-polarization SAR data. The method comprises the following steps: acquiring SMAP-R, SMAP, GF-3, Sentinel-1, MODIS and SR TM 30m DEM data; preprocessing the data to obtain a total intensity reflectivity, a normalized Stokes parameter and a backscattering coefficient; unifying data spatial resolution and dividing data sets; building a data supplement method model of the multi-polarization SAR back scattering modeling SMAP-R polarization parameters based on integrated learning; verifying the parameter inversion performance by using an ANN model; and finally filling the missing grid data. According to the method, modeling is carried out on SMAP-R polarization data through SAR back scattering data, secondary inversion verification is carried out, SMAP-R spatial data missing is effectively supplemented, and the spatial resolution of inversion earth surface parameters is improved.
Need to check novelty before this filing date? Find Prior Art